title:
Study of residual stress in reactively sputtered epitaxial Si-doped GaN
  films

abstract:
  Si-doped GaN films were grown on $\textit{c}$-sapphire by rf magnetron
reactive co-sputtering of GaAs and Si at various partial pressures of N$_2$ in
Ar-N2 growth atmosphere and their epitaxial character was ascertained by
phi-scans. Energy dispersive x-ray spectroscopy revealed $\thicksim$2 at.% Si
in all the films, but the N/Ga ratio decreased substantially as N$_2$
percentage was reduced from 100% to 10%. High resolution x-ray diffraction
revealed the dominant presence of edge dislocations ($\thicksim$10$^{12}$
cm$^{-2}$) in the films grown at 30% - 100% N$_2$, which decreased to
$\thicksim$5 x 10$^{11}$ cm$^{-2}$ at lower N$_2$ percentages, at which, the
density of screw dislocations was found to increase and attained values
comparable to that of edge dislocations. The lattice parameters ($\textit{a}$
and $\textit{c}$) were obtained independently to determine the in-plane and
out-of-plane components of strain in films, which were analyzed to deduce the
hydrostatic and biaxial strain contributions. The film grown at 100% N$_2$
displayed large micro-strain and hydrostatic strain due to excess/interstitial
nitrogen, both of which decreased with the initial reduction of N$_2$
percentage, but increased again below 30% N$_2$ due to Ar incorporation. The
films grown above 75% N$_2$ displayed compressive biaxial stress, which is
attributed to possible nitrogen incorporation into grain boundaries and tensile
side of edge dislocations. The reversal of biaxial stress to tensile character
in films grown below 75% N$_2$ is explained by the prevalence of in-plane
tensile stress generated during coalescence. The tensile stress decreased in
films grown below 30% N$_2$, which is ascribed to the Ar incorporation and
voided morphology. The presence of Si in the films does not have a significant
influence on strain behaviour and appears to be masked by the dominant
growth-related intrinsic effects.


title:
From MIM-Based GAN to Anomaly Detection:Event Probability Influence on
  Generative Adversarial Networks

abstract:
  In order to introduce deep learning technologies into anomaly detection,
Generative Adversarial Networks (GANs) are considered as important roles in the
algorithm design and realistic applications. In terms of GANs, event
probability reflected in the objective function, has an impact on the event
generation which plays a crucial part in GAN-based anomaly detection. The
information metric, e.g. Kullback-Leibler divergence in the original GAN, makes
the objective function have different sensitivity on different event
probability, which provides an opportunity to refine GAN-based anomaly
detection by influencing data generation. In this paper, we introduce the
exponential information metric into the GAN, referred to as MIM-based GAN,
whose superior characteristics on data generation are discussed in theory.
Furthermore, we propose an anomaly detection method with MIM-based GAN, as well
as explain its principle for the unsupervised learning case from the viewpoint
of probability event generation. Since this method is promising to detect
anomalies in Internet of Things (IoT), such as environmental, medical and
biochemical outliers, we make use of several datasets from the online ODDS
repository to evaluate its performance and compare it with other methods.


title:
CAD: Co-Adapting Discriminative Features for Improved Few-Shot
  Classification

abstract:
  Few-shot classification is a challenging problem that aims to learn a model
that can adapt to unseen classes given a few labeled samples. Recent approaches
pre-train a feature extractor, and then fine-tune for episodic meta-learning.
Other methods leverage spatial features to learn pixel-level correspondence
while jointly training a classifier. However, results using such approaches
show marginal improvements. In this paper, inspired by the transformer style
self-attention mechanism, we propose a strategy to cross-attend and re-weight
discriminative features for few-shot classification. Given a base
representation of support and query images after global pooling, we introduce a
single shared module that projects features and cross-attends in two aspects:
(i) query to support, and (ii) support to query. The module computes attention
scores between features to produce an attention pooled representation of
features in the same class that is later added to the original representation
followed by a projection head. This effectively re-weights features in both
aspects (i & ii) to produce features that better facilitate improved
metric-based meta-learning. Extensive experiments on public benchmarks show our
approach outperforms state-of-the-art methods by 3%~5%.


title: Quantum thermometry in diffraction-limited systems

abstract:
  We investigate the ultimate quantum limit of resolving the temperatures of
two thermal sources affected by the diffraction. More quantum Fisher
information can be obtained with the priori information than that without the
priori information. We carefully consider two strategies: the simultaneous
estimation and the individual estimation. The simultaneous estimation of two
temperatures is proved to satisfy the saturation condition of quantum
Cram\'{e}r bound and performs better than the individual estimation in the case
of small degree of diffraction given the same resources. However, in the case
of high degree of diffraction, the individual estimation performs better. In
particular, at the maximum diffraction, the simultaneous estimation can not get
any information, which is supported by a practical measurement, while the
individual estimation can still get the information. In addition, we find that
for the individual estimation, a practical and feasible estimation strategy by
using the full Hermite-Gauss basis can saturate the quantum Cram\'{e}r bound
without being affected by the attenuation factor at the maximum diffraction.
using the full Hermite-Gauss basis can saturate the quantum Cram\'er bound
without being affected by the attenuation factor at the maximum diffraction.


title: RD-Optimized Trit-Plane Coding of Deep Compressed Image Latent Tensors

abstract:
  DPICT is the first learning-based image codec supporting fine granular
scalability. In this paper, we describe how to implement two key components of
DPICT efficiently: trit-plane slicing and RD-prioritized transmission. In
DPICT, we transform an image into a latent tensor, represent the tensor in
ternary digits (trits), and encode the trits in the decreasing order of
significance. For entropy encoding, we should compute the probability of each
trit, which demands high time complexity in both the encoder and the decoder.
To reduce the complexity, we develop a parallel computing scheme for the
probabilities and describe it in detail with pseudo-codes. Moreover, in this
paper, we compare the trit-plane slicing in DPICT with the alternative
bit-plane slicing. Experimental results show that the time complexity is
reduced significantly by the parallel computing and that the trit-plane slicing
provides better rate-distortion performances than the bit-plane slicing.


title: Asymptotic stability of kink with internal modes under odd perturbation

abstract:
  We give a sufficient condition, in the spirit of Kowalczyk-Martel-Munoz-Van
Den Bosch \cite{KMMvdB21AnnPDE}, for the local asymptotic stability of kinks
under odd perturbations. In particular, we allow the existence of quite general
configuration of internal modes. The extension of our result to moving kinks
remains an open problem.


title: Quantum Annealing for Vehicle Routing Problem with weighted Segment

abstract:
  Quantum annealing technologies aim to solve computational optimization and
sampling problems. QPU (Quantum Processing Unit) machines such as the D-Wave
system use the QUBO (Quadratic Unconstrained Binary Optimization) formula to
define model optimization problems for quantum annealing. This machine uses
quantum effects to speed up computing time better than classical computers. We
propose a vehicle routing problem that can be formulated in the QUBO model as a
combinatorial problem, which gives the possible route solutions increases
exponentially. The solution aims to optimize the vehicle's journey to reach a
destination. The study presents a QUBO formulation to solve traffic congestion
problems on certain roads. The resulting route selection by optimizing the
distribution of the flow of alternative road vehicles based on the weighting of
road segments. Constraints formulated as a condition for the level of road
density. The road weight parameter influences the cost function for each road
choice. The simulations on the D-Wave quantum annealer show optimal results on
the route deployment of several vehicles. So that each vehicle will be able to
go through different road options and reduce road congestion accurately. This
solution provides an opportunity to develop QUBO modeling for more complex
vehicle routing problems for road congestion.


title: Interactive Style Transfer: All is Your Palette

abstract:
  Neural style transfer (NST) can create impressive artworks by transferring
reference style to content image. Current image-to-image NST methods are short
of fine-grained controls, which are often demanded by artistic editing. To
mitigate this limitation, we propose a drawing-like interactive style transfer
(IST) method, by which users can interactively create a harmonious-style image.
Our IST method can serve as a brush, dip style from anywhere, and then paint to
any region of the target content image. To determine the action scope, we
formulate a fluid simulation algorithm, which takes styles as pigments around
the position of brush interaction, and diffusion in style or content images
according to the similarity maps. Our IST method expands the creative dimension
of NST. By dipping and painting, even employing one style image can produce
thousands of eye-catching works. The demo video is available in supplementary
files or in http://mmcheng.net/ist.


title:
Non-Probability Sampling Network for Stochastic Human Trajectory
  Prediction

abstract:
  Capturing multimodal natures is essential for stochastic pedestrian
trajectory prediction, to infer a finite set of future trajectories. The
inferred trajectories are based on observation paths and the latent vectors of
potential decisions of pedestrians in the inference step. However, stochastic
approaches provide varying results for the same data and parameter settings,
due to the random sampling of the latent vector. In this paper, we analyze the
problem by reconstructing and comparing probabilistic distributions from
prediction samples and socially-acceptable paths, respectively. Through this
analysis, we observe that the inferences of all stochastic models are biased
toward the random sampling, and fail to generate a set of realistic paths from
finite samples. The problem cannot be resolved unless an infinite number of
samples is available, which is infeasible in practice. We introduce that the
Quasi-Monte Carlo (QMC) method, ensuring uniform coverage on the sampling
space, as an alternative to the conventional random sampling. With the same
finite number of samples, the QMC improves all the multimodal prediction
results. We take an additional step ahead by incorporating a learnable sampling
network into the existing networks for trajectory prediction. For this purpose,
we propose the Non-Probability Sampling Network (NPSN), a very small network
(~5K parameters) that generates purposive sample sequences using the past paths
of pedestrians and their social interactions. Extensive experiments confirm
that NPSN can significantly improve both the prediction accuracy (up to 60%)
and reliability of the public pedestrian trajectory prediction benchmark. Code
is publicly available at https://github.com/inhwanbae/NPSN .


title: Facial Expression Recognition with Swin Transformer

abstract:
  The task of recognizing human facial expressions plays a vital role in
various human-related systems, including health care and medical fields. With
the recent success of deep learning and the accessibility of a large amount of
annotated data, facial expression recognition research has been mature enough
to be utilized in real-world scenarios with audio-visual datasets. In this
paper, we introduce Swin transformer-based facial expression approach for an
in-the-wild audio-visual dataset of the Aff-Wild2 Expression dataset.
Specifically, we employ a three-stream network (i.e., Visual stream, Temporal
stream, and Audio stream) for the audio-visual videos to fuse the multi-modal
information into facial expression recognition. Experimental results on the
Aff-Wild2 dataset show the effectiveness of our proposed multi-modal
approaches.


title:
Nondegeneracy of ground states for nonlinear scalar field equations
  involving the Sobolev-critical exponent at high frequencies in three and four
  dimensions

abstract:
  We consider nonlinear scalar field equations involving the Sobolev-critical
exponent at high frequencies $\omega$. Since the limiting profile of the ground
state as $\omega \to \infty$ is the Aubin-Talenti function and degenerate in a
certain sense, from the point of view of perturbation methods, the
nondegeneracy problem for the ground states at high frequencies is subtle. In
addition, since the limiting profile (Aubin-Talenti function) fails to lie in
$L^{2}(\mathbb{R}^{d})$ for $d=3,4$, the nondegeneracy problem for $d=3,4$ is
more difficult than that for $d\ge 5$ and an applicable methodology is not
known. In this paper, we solve the nondegeneracy problem for $d=3,4$ by
modifying the arguments in [2, 3]. We also show that the linearized operator
around the ground state has exactly one negative eigenvalue.


title: A Conversational Paradigm for Program Synthesis

abstract:
  Program synthesis strives to generate a computer program as a solution to a
given problem specification. We propose a conversational program synthesis
approach via large language models, which addresses the challenges of searching
over a vast program space and user intent specification faced in prior
approaches. Our new approach casts the process of writing a specification and
program as a multi-turn conversation between a user and a system. It treats
program synthesis as a sequence prediction problem, in which the specification
is expressed in natural language and the desired program is conditionally
sampled. We train a family of large language models, called CodeGen, on natural
language and programming language data. With weak supervision in the data and
the scaling up of data size and model size, conversational capacities emerge
from the simple autoregressive language modeling. To study the model behavior
on conversational program synthesis, we develop a multi-turn programming
benchmark (MTPB), where solving each problem requires multi-step synthesis via
multi-turn conversation between the user and the model. Our findings show the
emergence of conversational capabilities and the effectiveness of the proposed
conversational program synthesis paradigm. In addition, our model CodeGen (with
up to 16B parameters trained on TPU-v4) outperforms OpenAI's Codex on the
HumanEval benchmark. We make the training library JaxFormer including
checkpoints available as open source contribution:
https://github.com/salesforce/CodeGen.


title:
A Dichotomy in Consistent Query Answering for Primary Keys and Unary
  Foreign Keys

abstract:
  Since 2005, significant progress has been made in the problem of Consistent
Query Answering (CQA) with respect to primary keys. In this problem, the input
is a database instance that may violate one or more primary key constraints. A
repair is defined as a maximal subinstance that satisfies all primary keys.
Given a Boolean query q, the question then is whether q holds true in every
repair.
  So far, theoretical research in this field has not addressed the combination
of primary key and foreign key constraints, despite the importance of
referential integrity in database systems. This paper addresses the problem of
CQA with respect to both primary keys and foreign keys. In this setting, it is
natural to adopt the notion of symmetric-difference repairs, because foreign
keys can be repaired by inserting new tuples.
  We consider the case where foreign keys are unary, and queries are
conjunctive queries without self-joins. In this setting, we characterize the
boundary between those CQA problems that admit a consistent first-order
rewriting, and those that do not.


title: Improved Lower Bounds for Multicolour Ramsey Numbers using SAT-Solvers

abstract:
  This paper sets out the results of some searches for linear and cyclic graph
colourings with specific Ramsey properties. The new graphs comprise mainly
'template graphs' which can be used in a construction described by the current
author in 2021 to build linear or cyclic compound graphs with inherited Ramsey
properties. These graphs result in improved lower bounds for a range of
multicolour Ramsey numbers by generating larger compound graphs, and, in a few
cases, directly.
  Searches were carried out using relatively simple programs (written in the
language 'C') to generate clauses for input to the PeneLoPe and Plingeling
parallel SAT-solvers. When solutions were found, the output from the solvers
specified the desired graph colourings.
  The template graphs are generally cyclic and non-diagonal, including one
which shows directly that $R(3,6,6) \ge 336$ and another demonstrating that
$R(3,5,5,5) \ge 1429$.
  Using the template graphs in familiar constructions, it is shown that $R_5(5)
\ge 41626$ and $R_4(6) \ge 23502$. Improved lower bounds follow, for all larger
$R_r(5)$ and $R_r(6)$. Another case shows that $R(3,4,5,5) \ge 729$.
  These improvements also serve to increase some lower bounds on $\Gamma(k) =
\lim_{\substack{r \rightarrow \infty}} R{_r}(k)^{1/r}$ , for some small values
of $k$. In particular, we show that $\Gamma(6) \ge \sqrt{234} = 15.297\dots$.
  The searches also revealed some other interesting lower bounds, including
$R(3,6,6) \ge 338$, based on a non-template cyclic graph.
  It is intended that this paper will be updated periodically: and a
spreadsheet containing specimens of the graphs mentioned here will be attached
as an ArXiv ancillary file.


title: Shadow of a nonsingular black hole

abstract:
  Seeking singularity free solutions are important for further understanding
black holes in quantum level. Recently, a five-dimensional singularity free
topology star/black hole was constructed [Phys. Rev. Lett. 126, 151101 (2021)].
Through the Kaluza-Klein reduction, an effective four-dimensional static
spherically symmetric nonsingular black hole can be obtained. In this paper, we
study shadow of this nonsingular black hole using three kinds of observers,
i.e. static observers, surrounding observers, and freely falling observers, in
four-dimensional spacetime. For a spherically symmetric black hole, the shadow
is circular for any observer, but the shadow size depends on the motion status
of the observer. The shadow size observed by a moving observer will tend to be
shrunk. On the other hand, the effect of plasma is also investigated by a
simple model. The radius of the photon sphere depends on the plasma model. Most
importantly, we find that the shadow sizes do not monotonically decrease with r
in some cases. This is a typical phenomenon for this nonsingular black hole.


title:
Smart Wireless Environments Enabled by RISs: Deployment Scenarios and
  Two Key Challenges

abstract:
  Reconfigurable Intelligent Surfaces (RISs) constitute the enabler for
programmable propagation of electromagnetic signals, and are lately being
considered as a candidate physical-layer technology for the demanding
connectivity, reliability, localization, and sustainability requirements of
next generation wireless communications networks. In this paper, we present
various deployment scenarios for RIS-enabled smart wireless environments that
have been recently designed by the ongoing EU H2020 RISE-6G project. The
scenarios are taxonomized according to performance objectives, in particular,
connectivity and reliability, localization and sensing, as well as
sustainability and secrecy. We identify various deployment strategies and
sketch the core architectural requirements in terms of RIS control and
signaling, depending on the RIS hardware architectures and their respective
capabilities. Furthermore, we introduce and discuss, via preliminary simulation
results and reflectarray measurements, two key novel challenges with
RIS-enabled smart wireless environments, namely, the area of influence and the
bandwidth of influence of RISs, which corroborate the need for careful
deployment and planning of this new technology.


title: Improving Adversarial Transferability with Spatial Momentum

abstract:
  Deep Neural Networks (DNN) are vulnerable to adversarial examples. Although
many adversarial attack methods achieve satisfactory attack success rates under
the white-box setting, they usually show poor transferability when attacking
other DNN models. Momentum-based attack (MI-FGSM) is one effective method to
improve transferability. It integrates the momentum term into the iterative
process, which can stabilize the update directions by adding the gradients'
temporal correlation for each pixel. We argue that only this temporal momentum
is not enough, the gradients from the spatial domain within an image, i.e.
gradients from the context pixels centered on the target pixel are also
important to the stabilization. For that, in this paper, we propose a novel
method named Spatial Momentum Iterative FGSM Attack (SMI-FGSM), which
introduces the mechanism of momentum accumulation from temporal domain to
spatial domain by considering the context gradient information from different
regions within the image. SMI-FGSM is then integrated with MI-FGSM to
simultaneously stabilize the gradients' update direction from both the temporal
and spatial domain. The final method is called SM$^2$I-FGSM. Extensive
experiments are conducted on the ImageNet dataset and results show that
SM$^2$I-FGSM indeed further enhances the transferability. It achieves the best
transferability success rate for multiple mainstream undefended and defended
models, which outperforms the state-of-the-art methods by a large margin.


title:
Measuring photophysical transition rates with fluorescence correlation
  spectroscopy and antibunching

abstract:
  We present a new method that combines fluorescence correlation spectroscopy
(FCS) on the microsecond time scale with fluorescence antibunching measurements
on the nanosecond time scale for measuring photophysical rate constants of
fluorescent molecules. The antibunching measurements allow us to quantify the
average excitation rate of fluorescent molecules within the confocal detection
volume of the FCS measurement setup. Knowledge of this value allows us then to
quantify, in an absolute manner, the intersystem crossing rate and triplet
state lifetime from the microsecond temporal decay of the FCS curves. We
present a theoretical analysis of the method and estimate the maximum bias
caused by the averaging of all quantities (excitation rate, photophysical
rates) over the confocal detection volume, and we show that this bias is
smaller than 5\% in most cases. We apply the method for measuring the
photophysical rate constants of the widely used dyes Rhodamine~110 and
ATTO~655.


title: Limiting effects in tori clusters

abstract:
  We consider agglomerates of misaligned tori orbiting a supermassive black
hole. The aggregate of tilted tori is modeled as a single orbiting
configuration by introducing a leading function governing the distribution of
toroids (and maximum pressure points inside the disks) around the black hole
attractor. The orbiting clusters are composed by geometrically thick, pressure
supported, perfect fluid tori. This analysis places constraints on the
existence and properties of tilted tori and more general aggregates of orbiting
disks. We study the constraints on the tori collision emergence and the
instability of the agglomerates of tori with general relative inclination
angles, the possible effects of the tori geometrical thickness and on the
oscillatory phenomena. Some notes are discussed on the orbiting ringed
structure in dependence of the dimensionless parameter ${\xi}$ representing the
(total) BH rotational energy extracted versus the mass of the BH, associating
${\xi}$ to the characteristics of the accretion processes.


title:
Polarization Multiplexed Diffractive Computing: All-Optical
  Implementation of a Group of Linear Transformations Through a
  Polarization-Encoded Diffractive Network

abstract:
  Research on optical computing has recently attracted significant attention
due to the transformative advances in machine learning. Among different
approaches, diffractive optical networks composed of spatially-engineered
transmissive surfaces have been demonstrated for all-optical statistical
inference and performing arbitrary linear transformations using passive,
free-space optical layers. Here, we introduce a polarization multiplexed
diffractive processor to all-optically perform multiple, arbitrarily-selected
linear transformations through a single diffractive network trained using deep
learning. In this framework, an array of pre-selected linear polarizers is
positioned between trainable transmissive diffractive materials that are
isotropic, and different target linear transformations (complex-valued) are
uniquely assigned to different combinations of input/output polarization
states. The transmission layers of this polarization multiplexed diffractive
network are trained and optimized via deep learning and error-backpropagation
by using thousands of examples of the input/output fields corresponding to each
one of the complex-valued linear transformations assigned to different
input/output polarization combinations. Our results and analysis reveal that a
single diffractive network can successfully approximate and all-optically
implement a group of arbitrarily-selected target transformations with a
negligible error when the number of trainable diffractive features/neurons (N)
approaches N_p x N_i x N_o, where N_i and N_o represent the number of pixels at
the input and output fields-of-view, respectively, and N_p refers to the number
of unique linear transformations assigned to different input/output
polarization combinations. This polarization-multiplexed all-optical
diffractive processor can find various applications in optical computing and
polarization-based machine vision tasks.


title: MKQ-BERT: Quantized BERT with 4-bits Weights and Activations

abstract:
  Recently, pre-trained Transformer based language models, such as BERT, have
shown great superiority over the traditional methods in many Natural Language
Processing (NLP) tasks. However, the computational cost for deploying these
models is prohibitive on resource-restricted devices. One method to alleviate
this computation overhead is to quantize the original model into fewer bits
representation, and previous work has proved that we can at most quantize both
weights and activations of BERT into 8-bits, without degrading its performance.
In this work, we propose MKQ-BERT, which further improves the compression level
and uses 4-bits for quantization. In MKQ-BERT, we propose a novel way for
computing the gradient of the quantization scale, combined with an advanced
distillation strategy. On the one hand, we prove that MKQ-BERT outperforms the
existing BERT quantization methods for achieving a higher accuracy under the
same compression level. On the other hand, we are the first work that
successfully deploys the 4-bits BERT and achieves an end-to-end speedup for
inference. Our results suggest that we could achieve 5.3x of bits reduction
without degrading the model accuracy, and the inference speed of one int4 layer
is 15x faster than a float32 layer in Transformer based model.


title:
Which nuclear shape is generating the strongest attraction on a
  relativistic electron? An open problem in relativistic quantum mechanics

abstract:
  In this article we formulate several conjectures concerning the lowest
eigenvalue of a Dirac operator with an external electrostatic potential. The
latter describes a relativistic quantum electron moving in the field of some
(pointwise or extended) nuclei. The main question we ask is whether the
eigenvalue is minimal when the nuclear charge is concentrated at one single
point. This well-known property in nonrelativistic quantum mechanics has
escaped all attempts of proof in the relativistic case.


title: Neutrinos as Qubits and Qutrits

abstract:
  We map neutrinos to qubit and qutrit states of quantum information theory by
constructing the Poincar\'e sphere using SU(2) Pauli matrices and SU(3)
Gell-Mann matrices, respectively. The construction of the Poincar\'e sphere in
the two-qubit system enables us to construct the Bloch matrix, which yields
valuable symmetries in the Bloch vector space of two neutrino systems. By
identifying neutrinos with qutrits, we calculate the measures of qutrit
entanglement for neutrinos. We use SU(3) Gell-Mann matrices tensor products to
construct the Poincar\'e sphere of two qutrits neutrino systems. The comparison
between the entanglement measures of bipartite qubits and bipartite qutrits in
the two neutrino system are shown. The result warrants a study of two qutrits
entanglement in the three neutrino system.


title: Self-healing of non-Hermitian topological skin modes

abstract:
  A unique feature of non-Hermitian (NH) systems is the NH skin effect, i.e.
the edge localization of an extensive number of bulk-band eigenstates in a
lattice with open or semi-infinite boundaries. Unlike extended Bloch waves in
Hermitian systems, the skin modes are normalizable eigenstates of the
Hamiltonian that originate from the intrinsic non-Hermitian point-gap topology
of the Bloch band energy spectra. Here we unravel a fascinating property of NH
skin modes, namely self-healing, i.e. the ability to self-reconstruct their
shape after being scattered off by a space-time potential.


title: Compare learning: bi-attention network for few-shot learning

abstract:
  Learning with few labeled data is a key challenge for visual recognition, as
deep neural networks tend to overfit using a few samples only. One of the
Few-shot learning methods called metric learning addresses this challenge by
first learning a deep distance metric to determine whether a pair of images
belong to the same category, then applying the trained metric to instances from
other test set with limited labels. This method makes the most of the few
samples and limits the overfitting effectively. However, extant metric networks
usually employ Linear classifiers or Convolutional neural networks (CNN) that
are not precise enough to globally capture the subtle differences between
vectors. In this paper, we propose a novel approach named Bi-attention network
to compare the instances, which can measure the similarity between embeddings
of instances precisely, globally and efficiently. We verify the effectiveness
of our model on two benchmarks. Experiments show that our approach achieved
improved accuracy and convergence speed over baseline models.


title:
Nonlinear damped spatially periodic breathers and the emergence of
  soliton-like rogue waves

abstract:
  The spatially periodic breather solutions (SPBs) of the nonlinear
Schr\"odinger equation, prominent in modeling rogue waves, are unstable. In
this paper we numerically investigate the effects of nonlinear dissipation and
higher order nonlinearities on the routes to stability of the SPBs in the
framework of the nonlinear damped higher order nonlinear Schr\"odinger
(NLD-HONLS) equation. The initial data used in the experiments are generated by
evaluating exact SPB solutions at time $T_0$. The number of instabilities of
the background Stokes wave and the damping strength are varied. The Floquet
spectral theory of the NLS equation is used to interpret and provide a
characterization of the perturbed dynamics in terms of nearby solutions of the
NLS equation. Significantly, as $T_0$ is varied, tiny bands of complex spectrum
are observed to pinch off in the Floquet decomposition of the NLD-HONLS data,
reflecting the breakup of the SPB into a waveform that is close to either a one
or two "soliton-like" structure. For wide ranges of $T_0$, i.e. for solutions
initialized in the early to middle stage of the development of the MI, all
rogue waves are observed to occur when the spectrum is close to a one or two
soliton-like state. When the solutions are initialized as the MI is saturating,
rogue waves also can occur after the spectrum has left a soliton-like state.
Other novel features arise due to nonlinear damping: enhanced asymmetry, two
timescales in the evolution of the spectrum and a delay in the growth of
instabilities due to frequency downshifting.


title: Cloud-cloud collision as origin of the G31.41+0.31 massive protocluster

abstract:
  The G31.41+0.31 (G31) hot molecular core (HMC) is a high-mass protocluster
showing accelerated infall and rotational spin-up that is well studied at
high-angular resolution. To complement the accurate view of the small scale in
G31, we have traced the kinematics of the large-scale material by carrying out
N$_2$H$^+$\,(1--0) observations with the IRAM 30m telescope of an area of
$\sim$6$\times6$\,arcmin$^2$ around the HMC. The N$_2$H$^+$ observations have
revealed a large-scale (5\,pc) hub-filament system (HFS) composed by at least
four filamentary arms and a NNE--SSW velocity gradient ($\sim$0.4\,km/s/pc)
between the northern and southern filaments. The linewidth increases towards
the hub at the center of the HFS reaching values of 2.5--3\,km\,s$^{-1}$ in the
central 1\,pc. The origin of the large-scale velocity gradient is likely
cloud-cloud collision. In this scenario, the filaments in G31 would have formed
by compression resulting from the collision and the rotation of the HMC
observed at scales of 1000\,au would have been induced by shear caused by the
cloud-cloud collision at scales of a few pc. We conclude that G31 represents a
HFS in a compressed layer with an orthogonal orientation to the plane of the
sky, and represents a benchmark for the filaments-to-clusters paradigm of star
formation.


title:
Phase diagrams and excitations of anisotropic $S=1$ quantum magnets on
  the triangular lattice

abstract:
  The $S=1$ bilinear-biquadratic Heisenberg exchange model on the triangular
lattice with a single-ion anisotropy has previously been shown to host a number
of exotic magnetic and nematic orders [Moreno-Cardoner $\textit{et al.}$, Phys.
Rev. B $\textbf{90}$, 144409 (2014)], including an extensive region of
"supersolid" order. In this work, we amend the model by an XXZ anisotropy in
the exchange interactions. Tuning to the limit of an exactly solvable $S=1$
generalized Ising-/Blume-Capel-type model provides a controlled limit to access
phases at finite transverse exchange. Notably, we find an additional
macroscopically degenerate region in the phase diagram and study its fate under
perturbation theory. We further map out phase diagrams as a function of the XXZ
anisotropy parameter, ratio of bilinear and biquadratic interactions and
single-ion anisotropy, and compute corrections to the total ordered moment in
various phases using systematically constructed linear flavor-wave theory. We
also present linear flavor-wave spectra of various states, finding that the
lowest-energy band in three-sublattice generalized (i.e. with $S^z=\pm1,0$)
Ising/Blume-Capel states, stabilized by strong exchange anisotropies, is
remarkably flat, opening up the way to flat-band engineering of magnetic
excitation via stabilizing non-trivial Ising-ordered ground states.


title:
Striking a Balance: Alleviating Inconsistency in Pre-trained Models for
  Symmetric Classification Tasks

abstract:
  While fine-tuning pre-trained models for downstream classification is the
conventional paradigm in NLP, often task-specific nuances may not get captured
in the resultant models. Specifically, for tasks that take two inputs and
require the output to be invariant of the order of the inputs, inconsistency is
often observed in the predicted labels or confidence scores. We highlight this
model shortcoming and apply a consistency loss function to alleviate
inconsistency in symmetric classification. Our results show an improved
consistency in predictions for three paraphrase detection datasets without a
significant drop in the accuracy scores. We examine the classification
performance of six datasets (both symmetric and non-symmetric) to showcase the
strengths and limitations of our approach.


title:
Move Complexity of a Self-Stabilizing Algorithm for Maximal Independent
  Sets

abstract:
  ${\cal A}_\mathsf{deg}$ is a self-stabilizing algorithm that computes a
maximal independent set in a finite graph with approximation ratio $(\Delta +
2)/3$. In this note we show that under the central scheduler the number of
moves of ${\cal A}_\mathsf{deg}$ is not bounded by a polynomial in $n$.


title:
Perturbative Calculations of Gravitational Form Factors at Large
  Momentum Transfer

abstract:
  We perform a perturbative QCD analysis of the gravitational form factors
(GFFs) of nucleon at large momentum transfer. We derive the explicit
factorization formula of the GFFs in terms of twist-3 and twist-4 light-cone
distribution amplitudes of nucleon. Power behaviors for these GFFs are obtained
from the leading order calculations. Numeric results of the quark and gluon
contributions to various GFFs are presented with model assumptions for the
distribution amplitudes in the literature. We also present the perturbative
calculations of the scalar form factor $\langle P'|F^2| P\rangle$ for pion and
proton at large momentum transfer.


title:
Big data ethics, machine ethics or information ethics? Navigating the
  maze of applied ethics in IT

abstract:
  Digitalization efforts are rapidly spreading across societies, challenging
new and important ethical issues that arise from technological development.
Software developers, designers and managerial decision-makers are ever more
expected to consider ethical values and conduct normative evaluations when
building digital products. Yet, when one looks for guidance in the academic
literature one encounters a plethora of branches of applied ethics. Depending
on the context of the system that is to be developed, interesting subfields
like big data ethics, machine ethics, information ethics, AI ethics or computer
ethics (to only name a few) may present themselves. In this paper we want to
offer assistance to any member of a development team by giving a clear and
brief introduction into two fields of ethical endeavor (normative ethics and
applied ethics), describing how they are related to each other and, finally,
provide an ordering of the different branches of applied ethics (big data
ethics, machine ethics, information ethics, AI ethics or computer ethics etc.)
which have gained traction over the last years. Finally, we discuss an example
in the domain of facial recognition software in the domain of medicine to
illustrate how this process of normative analysis might be conducted.


title:
Machine-Learning Based Objective Function Selection for Community
  Detection

abstract:
  NECTAR, a Node-centric ovErlapping Community deTection AlgoRithm, presented
in 2016 by Cohen et. al, chooses dynamically between two objective functions
which function to optimize, based on the network on which it is invoked. This
approach, as shown by Cohen et al., outperforms six state-of-the-art algorithms
for overlapping community detection. In this work, we present NECTAR-ML, an
extension of the NECTAR algorithm that uses a machine-learning based model for
automating the selection of the objective function, trained and evaluated on a
dataset of 15,755 synthetic and 7 real-world networks. Our analysis shows that
in approximately 90% of the cases our model was able to successfully select the
correct objective function. We conducted a competitive analysis of NECTAR and
NECTAR-ML. NECTAR-ML was shown to significantly outperform NECTAR's ability to
select the best objective function. We also conducted a competitive analysis of
NECTAR-ML and two additional state-of-the-art multi-objective community
detection algorithms. NECTAR-ML outperformed both algorithms in terms of
average detection quality. Multiobjective EAs (MOEAs) are considered to be the
most popular approach to solve MOP and the fact that NECTAR-ML significantly
outperforms them demonstrates the effectiveness of ML-based objective function
selection.


title: Emergent Universe from Energy-Momentum Squared Gravity

abstract:
  In order to bypass the big bang singularity, we develop an emergent universe
scenario within a covariant extension of General Relativity known as
\emph{"Energy-Momentum Squared Gravity"}. The extra terms of the model emerge
in the high energy regime. Considering dynamics in a
Friedmann-Lema\^itre-Robertson-Walker background, critical points, representing
stable Einstein static states of the phase space, result as solutions. It then
turns out that as the equation of state parameter $\omega$ gradually declines
from a constant value as $t\rightarrow-\infty$, eventually some of the static
past eternal solutions find the chance to naturally enter into thermal history
through a graceful exit mechanism. In this way, the successful realization of
the emergent universe allows an expanding thermal history without the big bang
singularity for the spatially flat universe free of cosmological constant.


title: WaveFuzz: A Clean-Label Poisoning Attack to Protect Your Voice

abstract:
  People are not always receptive to their voice data being collected and
misused. Training the audio intelligence systems needs these data to build
useful features, but the cost for getting permissions or purchasing data is
very high, which inevitably encourages hackers to collect these voice data
without people's awareness. To discourage the hackers from proactively
collecting people's voice data, we are the first to propose a clean-label
poisoning attack, called WaveFuzz, which can prevent intelligence audio models
from building useful features from protected (poisoned) voice data but still
preserve the semantic information to the humans. Specifically, WaveFuzz
perturbs the voice data to cause Mel Frequency Cepstral Coefficients (MFCC)
(typical representations of audio signals) to generate the poisoned frequency
features. These poisoned features are then fed to audio prediction models,
which degrades the performance of audio intelligence systems. Empirically, we
show the efficacy of WaveFuzz by attacking two representative types of
intelligent audio systems, i.e., speaker recognition system (SR) and speech
command recognition system (SCR). For example, the accuracies of models are
declined by $19.78\%$ when only $10\%$ of the poisoned voice data is to
fine-tune models, and the accuracies of models declined by $6.07\%$ when only
$10\%$ of the training voice data is poisoned. Consequently, WaveFuzz is an
effective technique that enables people to fight back to protect their own
voice data, which sheds new light on ameliorating privacy issues.


title:
Strain-dependent structural and electronic reconstructions in
  long-wavelength WS$_{2}$ moir\'e superlattices

abstract:
  In long-wavelength moir\'e superlattices of stacked transition metal
dichalcogenides (TMDs), structural reconstruction ubiquitously occurs, which
has reported to impact significantly their electronic properties. However,
complete microscopic understandings of the interplay between the lattice
reconstruction and alteration of electronic properties, and their further
response to external perturbations in the reconstructed TMDs moir\'e
superlattice are still lacking. Here, using scanning tunneling microscopy (STM)
and scanning tunneling spectroscopy (STS) combined with first-principles
calculation, we study the strain-dependent structural reconstruction and its
correlated electronic reconstruction in long-wavelength H-type WS$_{2}$ moir\'e
superlattice at nanometer scale. We observe that the long-wavelength WS$_{2}$
moir\'e superlattices experiencing strong atomic reconstruction transform into
a hexagonal array of screw dislocations separating large-sized H-stacked
domains. Both the geometry and the moir\'e wavelength of the moir\'e
superlattice are dramatically tuned by external intralayer heterostrain in our
experiment. Remarkably, the STS measurements further demonstrate that the
location of the K point in conduction band is modulated sensitively by
strain-induced lattice deformation at nanometer scale in this system, with the
maximum energy shift reaching up to 300 meV. Our results highlight that
intralayer strain plays a vital role in determining structural and electronic
properties in TMD moir\'e superlattice.


title: On Lelong Numbers of Generalized Monge-Amp\`ere Products

abstract:
  We consider generalized (mixed) Monge-Amp\`ere products of
quasiplurisubharmonic functions (with and without analytic singularities) as
they were introduced and studied in several articles written by subsets of M.
Andersson, E. Wulcan, Z. B{\l}ocki, R. L\"ark\"ang, H. Raufi, J. Ruppenthal,
and the author. We continue these studies and present estimates for the Lelong
numbers of pushforwards of such products by proper holomorphic submersions.
Furthermore, we apply these estimates to Chern and Segre currents of
pseudoeffective vector bundles. Among other corollaries, we obtain the
following generalization of a recent result by X. Wu. If the nonnef locus of a
pseudoeffective vector bundle $E$ on a K\"ahler manifold is contained in a
countable union of $k$-codimensional analytic sets, and if the $k$-power of the
first Chern class of $E$ is trivial, then $E$ is nef.


title:
Air quality monitoring survey in german school classrooms during the
  COVID-19 pandemic 2021

abstract:
  The air quality in classrooms across all types of schools in Lower Saxony in
the north of Germany has been monitored during June to December 2021 in a large
study. Novel sensor instruments for air quality monitoring were wall mounted in
329 rooms in 50 schools and day nurseries. Each sensor instrument records data
for carbon dioxide CO$_2$, sound level, temperature, and humidity. All
collected data are transmitted for monitoring and further statistical
evaluation by WiFi connection to the database. This study provides for the
first time a detailed survey of the air quality situation and handling in
schools during more than six months. The situation can be characterized by
large variety in classroom area and volume combined with very diverse air
ventilation possibilities. To control virus transmission via aerosol in
classrooms an individual monitoring of air quality combined with high
compliance for ventilation according to the signaled recommendation is urgently
required.


title:
Cooperative Path-following Control of Remotely Operated Underwater
  Robots for Human Visual Inspection Task

abstract:
  Remotely operated vehicles (ROVs) have drawn much attention to underwater
tasks, such as the inspection and maintenance of infrastructure. The workload
of ROV operators tends to be high, even for the skilled ones. Therefore,
assistance methods for the operators are desired. This study focuses on a task
in which a human operator controls an underwater robot to follow a certain path
while visually inspecting objects in the vicinity of the path. In such a task,
it is desirable to realize the speed of trajectory control manually because the
visual inspection is performed by a human operator. However, to allocate
resources to visual inspection, it is desirable to minimize the workload on the
path-following by assisting with the automatic control. Therefore, the
objective of this study was to develop a cooperative path-following control
method that achieves the above-mentioned task by expanding a robust
path-following control law of nonholonomic wheeled vehicles. To simplify this
problem, we considered a path-following and visual objects recognition task in
a two-dimensional plane. We conducted an experiment with participants (n=16)
who completed the task using the proposed method and manual control. The
results showed that both the path-following errors and the workload of the
participants were significantly smaller with the proposed method than with
manual control. In addition, subjective responses demonstrated that operator
attention tended to be allocated to objects recognition rather than robot
operation tasks with the proposed method. These results indicate the
effectiveness of the proposed cooperative path-following control method.


title: CVEM-BEM coupling with decoupled orders for 2D exterior Poisson problems

abstract:
  For the solution of 2D exterior Dirichlet Poisson problems we propose the
coupling of a Curved Virtual Element Method (CVEM) with a Boundary Element
Method (BEM), by using decoupled approximation orders. We provide optimal
convergence error estimates, in the energy and in the weaker
$\textit{L}^\text{2}$-norm, in which the CVEM and BEM contributions to the
error are separated. This allows taking advantage of the high order flexibility
of the CVEM to retrieve an accurate discrete solution by using a low order BEM.
The numerical results confirm the a priori estimates and show the effectiveness
of the proposed approach.


title:
Supplemental Material: Lifelong Generative Modelling Using Dynamic
  Expansion Graph Model

abstract:
  In this article, we provide the appendix for Lifelong Generative Modelling
Using Dynamic Expansion Graph Model. This appendix includes additional visual
results as well as the numerical results on the challenging datasets. In
addition, we also provide detailed proofs for the proposed theoretical analysis
framework. The source code can be found in
https://github.com/dtuzi123/Expansion-Graph-Model.


title:
EmoCaps: Emotion Capsule based Model for Conversational Emotion
  Recognition

abstract:
  Emotion recognition in conversation (ERC) aims to analyze the speaker's state
and identify their emotion in the conversation. Recent works in ERC focus on
context modeling but ignore the representation of contextual emotional
tendency. In order to extract multi-modal information and the emotional
tendency of the utterance effectively, we propose a new structure named
Emoformer to extract multi-modal emotion vectors from different modalities and
fuse them with sentence vector to be an emotion capsule. Furthermore, we design
an end-to-end ERC model called EmoCaps, which extracts emotion vectors through
the Emoformer structure and obtain the emotion classification results from a
context analysis model. Through the experiments with two benchmark datasets,
our model shows better performance than the existing state-of-the-art models.


title:
Contrastive learning of Class-agnostic Activation Map for Weakly
  Supervised Object Localization and Semantic Segmentation

abstract:
  While class activation map (CAM) generated by image classification network
has been widely used for weakly supervised object localization (WSOL) and
semantic segmentation (WSSS), such classifiers usually focus on discriminative
object regions. In this paper, we propose Contrastive learning for
Class-agnostic Activation Map (C$^2$AM) generation only using unlabeled image
data, without the involvement of image-level supervision. The core idea comes
from the observation that i) semantic information of foreground objects usually
differs from their backgrounds; ii) foreground objects with similar appearance
or background with similar color/texture have similar representations in the
feature space. We form the positive and negative pairs based on the above
relations and force the network to disentangle foreground and background with a
class-agnostic activation map using a novel contrastive loss. As the network is
guided to discriminate cross-image foreground-background, the class-agnostic
activation maps learned by our approach generate more complete object regions.
We successfully extracted from C$^2$AM class-agnostic object bounding boxes for
object localization and background cues to refine CAM generated by
classification network for semantic segmentation. Extensive experiments on
CUB-200-2011, ImageNet-1K, and PASCAL VOC2012 datasets show that both WSOL and
WSSS can benefit from the proposed C$^2$AM.


title:
Analysis of the Production Strategy of Mask Types in the COVID-19
  Environment

abstract:
  Since the outbreak of the COVID-19 in December 2019, medical protective
equipment such as disposable medical masks and KN95 masks have become essential
resources for the public. Enterprises in all sectors of society have also
transformed the production of medical masks. After the outbreak, how to choose
the right time to produce medical protective masks, and what type of medical
masks to produce will play a positive role in preventing and controlling the
epidemic in a short time. In this regard, the evolutionary game competition
analysis will be conducted through the relevant data of disposable medical
masks and KN95 masks to determine the appropriate nodes for the production of
corresponding mask types. After the research and analysis of the production
strategy of mask types, it has a positive effect on how to guide the resumption
of work and production.


title: On extremes of random clusters and marked renewal cluster processes

abstract:
  The article describes the limiting distribution of the extremes of
observations that arrive in clusters. We start by studying the tail behaviour
of an individual cluster and then we apply the developed theory to determine
the limiting distribution of $\max\{X_j: j=0,\ldots, K(t)\}$, where $K(t)$ is
the number of i.i.d. observations $(X_j)$ arriving up to the time $t$ according
to a general marked renewal cluster process. The results are illustrated in the
context of some commonly used Poisson cluster models such as the marked Hawkes
process.


title:
BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality
  Speech Synthesis

abstract:
  Diffusion probabilistic models (DPMs) and their extensions have emerged as
competitive generative models yet confront challenges of efficient sampling. We
propose a new bilateral denoising diffusion model (BDDM) that parameterizes
both the forward and reverse processes with a schedule network and a score
network, which can train with a novel bilateral modeling objective. We show
that the new surrogate objective can achieve a lower bound of the log marginal
likelihood tighter than a conventional surrogate. We also find that BDDM allows
inheriting pre-trained score network parameters from any DPMs and consequently
enables speedy and stable learning of the schedule network and optimization of
a noise schedule for sampling. Our experiments demonstrate that BDDMs can
generate high-fidelity audio samples with as few as three sampling steps.
Moreover, compared to other state-of-the-art diffusion-based neural vocoders,
BDDMs produce comparable or higher quality samples indistinguishable from human
speech, notably with only seven sampling steps (143x faster than WaveGrad and
28.6x faster than DiffWave). We release our code at
https://github.com/tencent-ailab/bddm.


title: Photo-induced Hidden Phase of 1T-TaS2 with Tunable Lifetime

abstract:
  Phase transitions are ubiquitous, appearing at every length scale from atoms
to galaxies. In condensed matter, ultrafast laser pulses drive materials to
highly non-equilibrium conditions allowing transitions to new phases of matter
not attainable under thermal excitation. Despite the intense scrutiny these
hidden phases have received, the details of the dynamics of transition and
reestablishment of the ground state remain largely unexplored. Here, we show
the transition to a hidden phase of 1T-TaS2 driven by the screening of
Coulombic repulsive interaction by photoexcited electrons. The temporal
evolution of the coherent lattice dynamics highlights the existence of a novel
phase with a laser fluence-dependent lifetime. The modeling of the dynamics
reveals that the transition is caused by photo-excited carriers and it
disappears at the rate of electron-phonon scattering. Our results demonstrate
how femtosecond laser absorption leads to a decoupling of the electronic and
lattice sub-systems, opening the way to novel states of matter, which can be
controlled with light. We expect our investigation to be a starting point
towards the development of novel ultrafast photonics devices, such as switches
and modulators, taking advantage of fast and tunable phase transitions.


title: Joint Distribution of Distance and Angles in Finite Wireless Networks

abstract:
  Directional beamforming will play a paramount role in 5G and beyond networks
in order to combat the higher path losses incurred at millimeter wave bands.
Appropriate modeling and analysis of the angles and distances between
transmitters and receivers in these networks are thus essential to understand
performance and limiting factors. Most existing literature considers either
infinite and uniform networks, where nodes are drawn according to a Poisson
point process, or finite networks with the reference receiver placed at the
origin of a disk. Under either of these assumptions, the distance and azimuth
angle between transmitter and receiver are independent, and the angle follows a
uniform distribution between $0$ and $2\pi$. Here, we consider a more realistic
case of finite networks where the reference node is placed at any arbitrary
location. We obtain the joint distribution between the distance and azimuth
angle and demonstrate that these random variables do exhibit certain
correlation, which depends on the shape of the region and the location of the
reference node. To conduct the analysis, we present a general mathematical
framework which is specialized to exemplify the case of a rectangular region.
We then also derive the statistics for the 3D case where, considering antenna
heights, the joint distribution of distance, azimuth and zenith angles is
obtained. Finally, we describe some immediate applications of the present work,
including the analysis of directional beamforming, the design of analog
codebooks and wireless routing algorithms.


title: Rapid prototyping and performance evaluation of MEC-based applications

abstract:
  Multi-access Edge Computing (MEC) will enable context-aware services for
users of mobile 4G/5G networks. MEC application developers need tools to aid
the design and the performance evaluation of their apps. During the early
stages of deployment, they should be able to evaluate the performance impact of
design choices - e.g., what round-trip delay can be expected due to the
interplay of computation, communication and service consumption. When a
prototype of the app exists, it needs to be tested it live, under controllable
conditions, to measure key performance indicators. In this paper, we present an
open-source framework that allows developers to do all the above. Our framework
is based on Simu5G, the OMNeT++-based simulator of 5G (NewRadio) and 4G (LTE)
mobile networks. It includes models of MEC entities (i.e., MEC orchestrator,
MEC host, etc.) and provides a standard-compliant RESTful interface towards
application endpoints. Moreover, it can interface with external applications,
and can also run in real time. Therefore, one can use it as a cradle to run a
MEC app live, having underneath both 4G/5G data packet transport and MEC
services based on information generated by the underlying emulated radio access
network. We describe our framework and present a use-case of an emulated
MEC-enabled 5G scenario.


title:
Efficient k-clique Listing with Set Intersection Speedup [Technical
  Report]

abstract:
  Listing all k-cliques is a fundamental problem in graph mining, with
applications in finance, biology, and social network analysis. However, owing
to the exponential growth of the search space as k increases, listing all
k-cliques is algorithmically challenging. DDegree and DDegCol are the
state-of-the-art algorithms that exploit ordering heuristics based on degree
ordering and color ordering, respectively. Both DDegree and DDegCol induce high
time and space overhead for set intersections cause they construct and maintain
all induced subgraphs. Meanwhile, it is non-trivial to implement the data level
parallelism to further accelerate on DDegree and DDegCol.
  In this paper, we propose two efficient algorithms SDegree and BitCol for
k-clique listing. We mainly focus on accelerating the set intersections for
k-clique listing. Both SDegree and BitCol exploit the data level parallelism
for further acceleration with single instruction multiple data (SIMD) or vector
instruction sets. Furthermore, we propose two preprocessing techniques Pre-Core
and Pre-List, which run in linear time. The preprocessing techniques
significantly reduce the size of the original graph and prevent exploring a
large number of invalid nodes. In the theoretical analysis, our algorithms have
a comparable time complexity and a slightly lower space complexity than the
state-of-the-art algorithms. The comprehensive experiments reveal that our
algorithms outperform the state-of-the-art algorithms by 3.75x for degree
ordering and 5.67x for color ordering on average.


title:
Ab initio description of multipolar responses in superfluid and deformed
  nuclei at finite temperature: application to dipole modes in $^{56}$Fe

abstract:
  Ab initio approaches to the nuclear many-body problem have seen their reach
considerably extended over the past decade. However, collective excitations
have rarely been addressed due to the prohibitive cost of solving the
corresponding equations of motion. Here, a numerically efficient method to
compute ab initio multipolar responses of superfluid and deformed nuclei at
finite temperature is presented. As a pilot application, the electromagnetic
dipolar responses in $^{56}$Fe based on chiral two- and three-body nuclear
interactions are computed over the temperature interval $k_B T \in [0,4]$ MeV,
where $k_B$ is the Boltzmann constant. This development directly opens the way
to the systematic ab initio study of multipolar excitations at finite
temperature in light and medium-mass nuclei.


title:
The local Schwarz paradox and the gradient of strongly differentiable
  functions

abstract:
  We show that the gradient of a multi-variable strongly differentiable
function at a point is the limit of a single coordinate-free Clifford quotient
between a multi-difference pseudo-vector and a pseudo-scalar, or of a sum of
Clifford quotients between scalars (as numerators) and vectors (as
denominators), both built on a same non degenerate simplex contracting to that
point. Then, we provide some conjectures implied by the foregoing result.


title: The Effective Theory of Quantum Black Holes

abstract:
  We explore the quantum nature of black holes by introducing an effective
framework that takes into account deviations from the classical results. The
approach is based on introducing quantum corrections to the classical
Schwarzschild geometry in a way that is consistent with the physical scales of
the black hole and its classical symmetries. This is achieved by organizing the
quantum corrections in inverse powers of a physical distance. By solving the
system in a self-consistent way we show that the derived physical quantities,
such as event horizons, temperature and entropy can be expressed in a well
defined expansion in the inverse powers of the black hole mass. The approach
captures the general form of the quantum corrections to black hole physics
without requiring to commit to a specific model of quantum gravity.


title:
$\cos2\phi_t$ azimuthal asymmetry in back-to-back $J/\psi$-jet in
  $e~p\rightarrow e~J/\psi~Jet~ X$ at the EIC

abstract:
  In this article, we investigate the $\cos2\phi_t$ azimuthal asymmetry in $e
~p\rightarrow e ~J/\psi ~Jet~ X$, where the $J/\psi$-jet pair is almost
back-to-back in the transverse plane, within the framework of the generalized
parton model(GPM). We use non-relativistic QCD(NRQCD) to calculate the $J/\psi$
production amplitude and incorporate both color singlet(CS) and color octet(CO)
contributions to the asymmetry. We estimate the asymmetry using different
parameterizations of the gluon TMDs in the kinematics that can be accessed at
the future electron-ion collider (EIC) and also investigate the impact of
transverse momentum dependent (TMD) evolution on the asymmetry. We present the
contributions coming from different states to the asymmetry in NRQCD.


title: Sparse Federated Learning with Hierarchical Personalization Models

abstract:
  Federated learning (FL) is widely used in the Internet of Things (IoT),
wireless networks, mobile devices, autonomous vehicles, and human activity due
to its excellent potential in cybersecurity and privacy security. Though FL
method can achieve privacy-safe and reliable collaborative training without
collecting users' privacy data, it suffers from many challenges during both
training and deployment. The main challenges in FL are the difficulty of
non-i.i.d co-training data caused by the statistical diversity of the data from
various participants, and the difficulty of application deployment caused by
the excessive traffic volume and long communication delay between the central
server and the client. To address these problems, we propose a sparse FL scheme
with hierarchical personalization models (sFedHP), which minimizes clients'
loss functions including the properties of an approximated L1-norm and the
hierarchical proximal mapping, to reduce the communicational and computational
loads required in the network, while improving the performance on statistical
diversity data. Convergence analysis shows that the sparse constraint in sFedHP
only reduces the convergence speed to a small extent, while the communication
cost is greatly reduced. Experimentally, we demonstrate the benefits of this
sparse hierarchical personalization architecture compared with the
client-edge-cloud hierarchical FedAvg and the state-of-the-art personalization
methods.


title:
Quantitative analysis of phase formation and growth in ternary mixtures
  upon evaporation of one component

abstract:
  We perform a quantitative analysis of Monte Carlo simulations results of
phase separation in ternary blends upon evaporation of one component.
Specifically, we calculate the average domain size and plot it as a function of
simulation time to compute the exponent of the obtained power-law. We compare
and discuss results obtained by two different methods, for three different
models: 2D binary-state model (Ising model), 2D ternary-state model with and
without evaporation. For the ternary-state models, we study additionally the
dependence of the domain growth on concentration, temperature and initial
composition. We reproduce the expected 1/3 exponent for the Ising model, while
for the ternary-state model without evaporation and for the one with
evaporation we obtain lower values of the exponent. It turns out that phase
separation patterns that can form in this type of systems are complex. The
obtained quantitative results give valuable insights towards devising
computable theoretical estimations of size effects on morphologies as they
occur in the context of organic solar cells.


title:
Measurement based estimator scheme for continuous quantum error
  correction

abstract:
  Quantum error correction (QEC) is necessary for any prospective quantum
computation in the near future. Canonical QEC schemes use projective von
Neumann measurements on certain parity operators (stabilizers) to discretize
the error syndromes into a finite set, and fast unitary gates are applied to
recover the corrupted information. We call such QEC schemes discrete quantum
error correction codes (DQEC). To date, DQEC has proven very difficult to
implement experimentally and so far has been demonstrated with limited success
in a few platforms that include ion traps, diamond NV centers, and
superconducting circuits. A less explored alternative is the use of weak
continuous measurements instead of projective measurements, first proposed
theoretically about two decades ago, which has been recently demonstrated
experimentally. Such QEC schemes are known as continuous quantum error
correction codes (CQEC) and in principle can be executed faster than DQEC.
However, CQEC requires meticulous filtering of noisy continuous measurement
data to reliably extract error syndromes on the basis of which errors could be
detected. In this work, we show that by constructing a real-time
measurement-based estimator (MBE) of the real logical qubit to be protected,
which is driven by the noisy continuous measurement currents of the
stabilizers, it is possible to accurately track the actual errors occurring
within the real qubits in real-time. We use this MBE to develop a novel
continuous quantum error correction (MBE-CQEC) scheme that can protect the
logical qubit to a high degree, and also allows the error correction to be
applied either immediately or at a later time.


title: Topological BF description of 2D accelerated chiral edge modes

abstract:
  We consider the topological abelian BF theory with radial boundary on a
generic 3D manifold. Our aim is to study if, where and how the boundary keeps
memory of the details of the background metric. We find that some features are
topologically protected and do not depend on the bulk metric. The 2D action
holographically induced on the boundary depends on two scalar fields, and can
be decoupled in two Luttinger actions describing two chiral bosons moving on
the edge of the 3D bulk. The outcome is that these edge excitations are
accelerated, as a direct consequence of the non-flat nature of the bulk
spacetime. The chiral velocities of the edge modes, indeed, acquire a local
dependence through the determinant of the induced metric on the boundary. We
find three possibilities for the motion of the edge quasiparticles: same
directions, opposite directions and a single-moving mode. But, requiring that
the Hamiltonian of the 2D theory is bounded by below, the case of edge modes
moving in the same direction is ruled out: systems involving parallel Hall
currents (for instance Fractional Quantum Hall Effect with $\nu=2/5$) cannot be
described by a BF theory with boundary, independently from the geometry of the
bulk spacetime, because of positive energy considerations. We are therefore
left with physical situations characterized by edge excitations moving with
opposite velocities (examples are FQHE with $\nu=1-1/n$, with $n$ positive
integer, and Helical Luttinger Liquids phenomena) or a single-moving mode
(Quantum Anomalous Hall). A strong restriction is obtained by requiring Time
Reversal symmetry, which uniquely identifies modes with equal and opposite
velocities, and we know that this is the case of Topological Insulators. The
novelty, with respect to the flat bulk background, is that the modes have local
velocities, which corresponds to Topological Insulators with accelerated edge
modes.


title: Introduction to the signature method

abstract:
  The sequential data observed in earth science can be regarded as paths in
multidimensional space. To read the path effectively, it is useful to convert
it into a sequence of numbers called the signature, which can faithfully
describe the order of points and nonlinearity in the path. In particular, a
linear combination of the terms in a signature can be used to approximate any
nonlinear function defined on a set of paths. Thereby, when one learns a set of
sequential data with labels attached to it, linear regression can be applied to
the pairs of signature and label, which will achieve high performance learning
even when the labels are determined by a nonlinear function. By incorporating
the signature methods into machine learning and data assimilation utilizing
sequential data, it is expected that we can extract information that has
previously been overlooked.


title: New Quantum Algorithms for Computing Quantum Entropies and Distances

abstract:
  We propose a series of quantum algorithms for computing a wide range of
quantum entropies and distances, including the von Neumann entropy, quantum
R\'{e}nyi entropy, trace distance, and fidelity. The proposed algorithms
significantly outperform the best known (and even quantum) ones in the low-rank
case, some of which achieve exponential speedups. In particular, for
$N$-dimensional quantum states of rank $r$, our proposed quantum algorithms for
computing the von Neumann entropy, trace distance and fidelity within additive
error $\varepsilon$ have time complexity of $\tilde O(r^2/\varepsilon^2)$,
$\tilde O(r^5/\varepsilon^6)$ and $\tilde O(r^{6.5}/\varepsilon^{7.5})$,
respectively. In contrast, the known algorithms for the von Neumann entropy and
trace distance require quantum time complexity of $\Omega(N)$
[AISW19,GL20,GHS21], and the best known one for fidelity requires $\tilde
O(r^{21.5}/\varepsilon^{23.5})$ [WZC+21].
  The key idea of our quantum algorithms is to extend block-encoding from
unitary operators in previous work to quantum states (i.e., density operators).
It is realized by developing several convenient techniques to manipulate
quantum states and extract information from them. In particular, we introduce a
novel technique for eigenvalue transformation of density operators and their
(non-integer) positive powers, based on the powerful quantum singular value
transformation (QSVT) [GSLW19]. The advantage of our techniques over the
existing methods is that no restrictions on density operators are required; in
sharp contrast, the previous methods usually require a lower bound of the
minimal non-zero eigenvalue of density operators. In addition, we provide some
techniques of independent interest for trace estimation, linear combinations,
and eigenvalue threshold projectors of (subnormalized) density operators, which
will be, we believe, useful in other quantum algorithms.


title: Linear Complexity of Sequences on Koblitz Curves of Genus 2

abstract:
  In this paper, we consider the hyperelliptic analogue of the Frobenius
endomorphism generator and show that it produces sequences with large linear
complexity on the Jacobian of genus 2 curves.


title: Angular distributions for $\Lambda_b\to \Lambda^*_J (pK^-)J/\psi$ Decays

abstract:
  We carry out an analysis of the multibody decay cascade $\Lambda_b\to
\Lambda^*_J J/\psi \to pK^- J/\psi$ with the $ \Lambda^*_J$ resonance including
$\Lambda^*_{1520},\Lambda^*_{1600}$, and $\Lambda^*_{1800}$, and $J/\psi$
reconstructed by the lepton pair final state. Using the helicity amplitude
technique, we derive a compact form for the angular distributions for the decay
chain, from which one can extract various one-dimensional distributions. Using
the $\Lambda_b\to \Lambda_J^*$ form factors from Lattice QCD and quark model,
we calculate the differential and integrated partial widths. Decay branching
fractions are found as $\mathcal{B}(\Lambda_b\to
pK\mu^+\mu^-)=(1.41\pm0.31)\times 10^{-5}$, $\mathcal{B}(\Lambda_b\to
pKe^+e^-)=(1.41\pm0.32)\times 10^{-5}$. In addition, we also explore
forward-backward asymmetry, and various polarizations. Results in this work
will serve a calibration for the study of $b\to s \bar{\mu}\mu$ decays in
$\Lambda_b$ decays in future and provide useful information towards the
understanding of the properties of the $\Lambda_J^*$ baryons.


title: Topology optimization of wind farm layouts

abstract:
  A novel approach for the solution of the wind farm layout optimization
problem is presented. The annual energy production is maximized with
constraints on the minimum and maximum number of wind turbines placed, and on
the minimum spacing between the wind turbines. The proposed approach relies on
a density-based topology optimization method, where continuous density
variables varying between zero and one are assigned to each potential wind
turbine location. A wind turbine exists if its associated variable equals one,
otherwise it does not exist if the associated variable is zero. Intermediate
values of the density variables are penalized with interpolation schemes
traditionally used in the context of multi-material structural topology
optimization. The penalized intermediate values of the design variables become
uneconomical and the optimization algorithm is implicitly pushed towards a
preference of crisp 0-1 final values. The optimization problem is solved with a
gradient-based algorithm based on first-order information. Because of the
proposed problem formulation, the functions involved are formulated explicitly
in terms of the design variables and their analytical gradients can be
calculated directly. The numerical results highlight the capability of the
proposed approach in finding far from intuitive wind farm layouts with small
computational resources and time.


title:
Energy-Efficient and Physical Layer Secure Computation Offloading in
  Blockchain-Empowered Internet of Things

abstract:
  This paper investigates computation offloading in blockchain-empowered
Internet of Things (IoT), where the task data uploading link from sensors to a
base station (BS) is protected by intelligent reflecting surface (IRS)-assisted
physical layer security (PLS). After receiving task data, the BS allocates
computational resources provided by mobile edge computing (MEC) servers to help
sensors perform tasks. Existing blockchain-based computation offloading schemes
usually focus on network performance improvements, such as energy consumption
minimization or latency minimization, and neglect the Gas fee for computation
offloading, resulting in the dissatisfaction of high Gas providers. Also, the
secrecy rate during the data uploading process can not be measured by a steady
value because of the time-varying characteristics of IRS-based wireless
channels, thereby computational resources allocation with a secrecy rate
measured before data uploading is inappropriate. In this paper, we design a
Gas-oriented computation offloading scheme that guarantees a low degree of
dissatisfaction of sensors, while reducing energy consumption. Also, we deduce
the ergodic secrecy rate of IRS-assisted PLS transmission that can represent
the global secrecy performance to allocate computational resources. The
simulations show that the proposed scheme has lower energy consumption compared
to existing schemes, and ensures that the node paying higher Gas gets stronger
computational resources.


title: A Lagrange subspace approach to dissipation inequalities

abstract:
  The standard dissipation inequality for passivity is extended from storage
functions to general Lagrange subspaces. This is shown to have some interesting
consequences. A classical factorization result for passive systems is extended
to this generalized case, making use of the newly defined concept of the
Hamiltonian lift of a DAE system.


title:
Single Model Ensemble for Subword Regularized Models in Low-Resource
  Machine Translation

abstract:
  Subword regularizations use multiple subword segmentations during training to
improve the robustness of neural machine translation models. In previous
subword regularizations, we use multiple segmentations in the training process
but use only one segmentation in the inference. In this study, we propose an
inference strategy to address this discrepancy. The proposed strategy
approximates the marginalized likelihood by using multiple segmentations
including the most plausible segmentation and several sampled segmentations.
Because the proposed strategy aggregates predictions from several
segmentations, we can regard it as a single model ensemble that does not
require any additional cost for training. Experimental results show that the
proposed strategy improves the performance of models trained with subword
regularization in low-resource machine translation tasks.


title:
Computing X-ray absorption spectra from linear-response particles atop
  optimized holes

abstract:
  State specific orbital optimized density functional theory (OO-DFT) methods
like restricted open-shell Kohn-Sham (ROKS) can attain semiquantitative
accuracy for predicting X-ray absorption spectra of closed-shell molecules.
OO-DFT methods however require that each state be individually optimized. In
this work, we present an approach to generate an approximate core-excited state
density for use with the ROKS energy ansatz, that is capable of giving
reasonable accuracy without requiring state-specific optimization. This is
achieved by fully optimizing the core-hole through the core-ionized state,
followed by use of electron-addition configuration interaction singles (EA-CIS)
to obtain the particle level. This hybrid approach can be viewed as a DFT
generalization of the static-exchange (STEX) method, and can attain $\sim 0.6$
eV RMS error for the K-edges of C-F through the use of local functionals like
PBE and OLYP. This ROKS(STEX) approach can also be used to identify important
transitions for full OO ROKS treatment, and can thus help reduce the
computational cost for obtaining OO-DFT quality spectra. ROKS(STEX) therefore
appears to be a useful technique for efficient prediction of X-ray absorption
spectra.


title:
Multimodal Pre-training Based on Graph Attention Network for Document
  Understanding

abstract:
  Document intelligence as a relatively new research topic supports many
business applications. Its main task is to automatically read, understand, and
analyze documents. However, due to the diversity of formats (invoices, reports,
forms, etc.) and layouts in documents, it is difficult to make machines
understand documents. In this paper, we present the GraphDoc, a multimodal
graph attention-based model for various document understanding tasks. GraphDoc
is pre-trained in a multimodal framework by utilizing text, layout, and image
information simultaneously. In a document, a text block relies heavily on its
surrounding contexts, so we inject the graph structure into the attention
mechanism to form a graph attention layer so that each input node can only
attend to its neighborhoods. The input nodes of each graph attention layer are
composed of textual, visual, and positional features from semantically
meaningful regions in a document image. We do the multimodal feature fusion of
each node by the gate fusion layer. The contextualization between each node is
modeled by the graph attention layer. GraphDoc learns a generic representation
from only 320k unlabeled documents via the Masked Sentence Modeling task.
Extensive experimental results on the publicly available datasets show that
GraphDoc achieves state-of-the-art performance, which demonstrates the
effectiveness of our proposed method.


title: Automation of antenna subtraction in colour space: gluonic processes

abstract:
  We present the colourful antenna subtraction method, a reformulation of the
antenna subtraction scheme for next-to-next-to-leading order (NNLO)
calculations in QCD. The aim of this new approach is to achieve a general and
process-independent construction of the subtraction infrastructure at NNLO. We
rely on the predictability of the infrared singularity structure of one- and
two-loop amplitudes in colour space to generate virtual subtraction terms and,
subsequently, we define an automatable procedure to systematically infer the
expression of the real subtraction terms, guided by the correspondence between
unintegrated and integrated antenna functions. To demonstrate the applicability
of the described approach, we compute the full colour NNLO correction to
gluonic three-jet production $pp(gg)\to ggg$, in the gluons-only assumption.


title:
Frequency response of diffusion-based molecular communication channels
  in bounded environment

abstract:
  Recently, molecular communication (MC) has been studied as a micro-scale
communication between cells or molecular robots. In previous works, the MC
channels in unbounded environment was analyzed. However, many of the
experimentally implemented MC channels are surrounded by walls, thus the
boundary condition should be explicitly considered to analyze the dynamics of
MC channels. In this paper, we propose a framework to analyze the frequency
response of one-dimensional MC channels based on a diffusion equation with a
boundary. In particular, we decompose the MC channel into the diffusion system
and the boundary system, and show the relation between the cut-off frequency of
the MC channel and the communication distance based on the transfer function.
We then analyze the frequency response of a specific MC channel and reveal that
the boundary can restrict the communication bandwidth of the MC channel.


title: High-Performance Transformer Tracking

abstract:
  Correlation has a critical role in the tracking field, especially in recent
popular Siamese-based trackers. The correlation operation is a simple fusion
manner to consider the similarity between the template and the search region.
However, the correlation operation is a local linear matching process, losing
semantic information and falling into local optimum easily, which may be the
bottleneck of designing high-accuracy tracking algorithms. In this work, to
determine whether a better feature fusion method exists than correlation, a
novel attention-based feature fusion network, inspired by Transformer, is
presented. This network effectively combines the template and the search region
features using attention. Specifically, the proposed method includes an
ego-context augment module based on self-attention and a cross-feature augment
module based on cross-attention. First, we present a Transformer tracking
(named TransT) method based on the Siamese-like feature extraction backbone,
the designed attention-based fusion mechanism, and the classification and
regression head. Based on the TransT baseline, we further design a segmentation
branch to generate an accurate mask. Finally, we propose a stronger version of
TransT by extending TransT with a multi-template design and an IoU prediction
head, named TransT-M. Experiments show that our TransT and TransT-M methods
achieve promising results on seven popular datasets. Code and models are
available at https://github.com/chenxin-dlut/TransT-M.


title: Generalization bounds for learning under graph-dependence: A survey

abstract:
  Traditional statistical learning theory relies on the assumption that data
are identically and independently generated from a given distribution (i.i.d.).
The independently distributed assumption, on the other hand, fails to hold in
many real applications. In this survey, we consider learning settings in which
examples are dependent and their dependence relationship can be characterized
by a graph. We collect various graph-dependent concentration bounds, which are
then used to derive Rademacher and stability generalization bounds for learning
from graph-dependent data. We illustrate this paradigm with three learning
tasks and provide some research directions for future work. To the best of our
knowledge, this is the first survey on this subject.


title: SeCo: Separating Unknown Musical Visual Sounds with Consistency Guidance

abstract:
  Recent years have witnessed the success of deep learning on the visual sound
separation task. However, existing works follow similar settings where the
training and testing datasets share the same musical instrument categories,
which to some extent limits the versatility of this task. In this work, we
focus on a more general and challenging scenario, namely the separation of
unknown musical instruments, where the categories in training and testing
phases have no overlap with each other. To tackle this new setting, we propose
the Separation-with-Consistency (SeCo) framework, which can accomplish the
separation on unknown categories by exploiting the consistency constraints.
Furthermore, to capture richer characteristics of the novel melodies, we devise
an online matching strategy, which can bring stable enhancements with no cost
of extra parameters. Experiments demonstrate that our SeCo framework exhibits
strong adaptation ability on the novel musical categories and outperforms the
baseline methods by a significant margin.


title:
Physical Layer Security Assisted Computation Offloading in Intelligently
  Connected Vehicle Networks

abstract:
  In this paper, we propose a secure computation offloading scheme (SCOS) in
intelligently connected vehicle (ICV) networks, aiming to minimize overall
latency of computing via offloading part of computational tasks to nearby
servers in small cell base stations (SBSs), while securing the information
delivered during offloading and feedback phases via physical layer security.
Existing computation offloading schemes usually neglected time-varying
characteristics of channels and their corresponding secrecy rates, resulting in
an inappropriate task partition ratio and a large secrecy outage probability.
To address these issues, we utilize an ergodic secrecy rate to determine how
many tasks are offloaded to the edge, where ergodic secrecy rate represents the
average secrecy rate over all realizations in a time-varying wireless channel.
Adaptive wiretap code rates are proposed with a secrecy outage constraint to
match time-varying wireless channels. In addition, the proposed secure
beamforming and artificial noise (AN) schemes can improve the ergodic secrecy
rates of uplink and downlink channels even without eavesdropper channel state
information (CSI). Numerical results demonstrate that the proposed schemes have
a shorter system delay than the strategies neglecting time-varying
characteristics.


title: Efficient Visual Tracking via Hierarchical Cross-Attention Transformer

abstract:
  In recent years, target tracking has made great progress in accuracy. This
development is mainly attributed to powerful networks (such as transformers)
and additional modules (such as online update and refinement modules). However,
less attention has been paid to tracking speed. Most state-of-the-art trackers
are satisfied with the real-time speed on powerful GPUs. However, practical
applications necessitate higher requirements for tracking speed, especially
when edge platforms with limited resources are used. In this work, we present
an efficient tracking method via a hierarchical cross-attention transformer
named HCAT. Our model runs about 195 fps on GPU, 45 fps on CPU, and 55 fps on
the edge AI platform of NVidia Jetson AGX Xavier. Experiments show that our
HCAT achieves promising results on LaSOT, GOT-10k, TrackingNet, NFS, OTB100,
UAV123, and VOT2020. Code and models are available at
https://github.com/chenxin-dlut/HCAT.


title:
Colossal power extraction from active cyclic Brownian information
  engines

abstract:
  Brownian information engines can extract work from thermal fluctuations by
utilizing information. So far, the studies on Brownian information engines
consider the system in a thermal bath; however, many processes in nature occur
in a nonequilibrium setting, such as the suspensions of self-propelled
microorganisms or cellular environments called an active bath. Here, we
introduce an archetypal model for Maxwell-demon type cyclic Brownian
information engine operating in a Gaussian correlated active bath. The active
engine can extract more work than its thermal counterpart, exceeding the bound
set by the second law of information thermodynamics. We obtain a general
integral fluctuation theorem for the active engine that includes additional
mutual information gained from the active bath with a unique effective
temperature. This effective description modifies the second law and provides a
new upper bound for the extracted work. Unlike the passive information engine
operating in a thermal bath, the active information engine extracts colossal
power that peaks at the finite cycle period. Our study provides fundamental
insights into the design and functioning of synthetic and biological submicron
motors in active baths under measurement and feedback control.


title: Classical mass inflation vs semiclassical inner horizon inflation

abstract:
  We analyse the geometry of a spherically symmetric black hole with an inner
and outer apparent horizon which is perturbed by spherical null shells of
matter. On a classical level we observe that the mass inflation instability is
triggered, resulting in a growth of curvature and an inward displacement of the
inner horizon. We study in detail the inner structure of the mass-inflated
region and compare it with previous results obtained for the case in which the
perturbing matter content has a continuous distribution. We then perform an
approximate calculation of the renormalised stress-energy tensor of a quantum
field in the vicinity of the inner horizon, and analyse the semiclassical
backreaction on this region of the geometry. We find that the classical
tendency for this horizon to move inward due to mass inflation is challenged
and potentially overcome by a semiclassical tendency for it to inflate outward.


title: Supporting tangible multi-factor key exchange in households

abstract:
  A common approach to securing end-to-end connectivity between devices on the
Internet is to utilise a cloud-based intermediary. With this reliance upon a
third-party comes a set of security and privacy concerns that are difficult to
mitigate. A promising new protocol, Wireguard, dispenses with the middleman to
provide secure peer-to-peer communication. However, support for initial key
exchange falls outside Wireguard's scope, making it potentially vulnerable to
insecure out-of-band key exchange. The design of secure and usable key exchange
methods is challenging, not least in domestic spaces, as they're often
characterised by technically naive users in multi-occupancy environments,
making them susceptible to insider and passer-by attacks (i.e.: theft,
observation attacks, relay and impersonation attacks). We describe and present
the results from a design ideation study that probes the use of tangible,
multi-factor approaches for securing key exchange in domestic spaces. The study
suggests that a home's semi-fixed features (e.g.: lamps, shelves, chairs) can
be instrumented to support a promising three-factor authentication approach
('what you have, what you know and where you are') to enable key exchange
solutions that are i. more secure than commonly used naive approaches and ii.
desirable for end users.


title: Shell-model study of octupole collectivity near 208Pb

abstract:
  We show that the collectivity of the particle-hole wave function of low-lying
octupole 3^- states in doubly magic nuclei is mainly due to the neutron-proton
interaction. Both the enhanced reduced transition probability to the ground
state, B(E3;3-_1 -> 0+_1), and the coupling of the octupole excitation to a
nucleon result from the coherent action of all the components of the collective
state. The results obtained with a realistic shell-model interaction both for
208Pb and 209Pb agree with the geometric collective model of Bohr and
Mottelson, where octupole excitations are associated with phonons corresponding
to collective shape oscillations of the surface of the nucleus.


title: EnHDC: Ensemble Learning for Brain-Inspired Hyperdimensional Computing

abstract:
  Ensemble learning is a classical learning method utilizing a group of weak
learners to form a strong learner, which aims to increase the accuracy of the
model. Recently, brain-inspired hyperdimensional computing (HDC) becomes an
emerging computational paradigm that has achieved success in various domains
such as human activity recognition, voice recognition, and bio-medical signal
classification. HDC mimics the brain cognition and leverages high-dimensional
vectors (e.g., 10000 dimensions) with fully distributed holographic
representation and (pseudo-)randomness. This paper presents the first effort in
exploring ensemble learning in the context of HDC and proposes the first
ensemble HDC model referred to as EnHDC. EnHDC uses a majority voting-based
mechanism to synergistically integrate the prediction outcomes of multiple base
HDC classifiers. To enhance the diversity of base classifiers, we vary the
encoding mechanisms, dimensions, and data width settings among base
classifiers. By applying EnHDC on a wide range of applications, results show
that the EnHDC can achieve on average 3.2\% accuracy improvement over a single
HDC classifier. Further, we show that EnHDC with reduced dimensionality, e.g.,
1000 dimensions, can achieve similar or even surpass the accuracy of baseline
HDC with higher dimensionality, e.g., 10000 dimensions. This leads to a 20\%
reduction of storage requirement of HDC model, which is key to enabling HDC on
low-power computing platforms.


title: Stanley's Shuffle Theorem and Insertion Lemma

abstract:
  This note is devoted to a combinatorial proof of Stanley's Shuffle Theorem by
using the Insertion Lemma of Haglund, Loehr and Remmel.


title:
Performance evaluation of switching between WiFi and LiFi under a common
  virtual network interface

abstract:
  The LiFi paradigm is rapidly emerging as one of the technologies that will
enable an impressive increase in the bandwidth made available to wireless
devices in 6G networks. Due to their intrinsic physical characteristics,
visible light and infrared communications present coverage features that are
different from their radio wave counterparts. A popular paradigm which has
recently emerged is that of hybrid networks, in which the two wireless
connection are used together in an hybrid configuration. We consider a hybrid
wireless local area network composed of both WiFi and LiFi Access Points (AP)
and wireless devices, in which the union of the two technologies is performed
at the data link layer, i.e., each device is identified in the network by a
unique IP address, using a virtual network interface obtained by bonding the
WiFi and LiFi physical interfaces, implemented through commercially available
products. We measure the time it takes to switch across the two physical
interfaces and its impact on the traffic flow, under specific triggering events
for the switch, namely an interface malfunctioning, a loss of signal, and a
manual switch. Our experimental results show that the different types of
triggering events have an impact on the time it takes to reconfigure the
currently active interface, with recovery times ranging from few tens
milliseconds to few seconds. In terms of packet losses at the flow level, a
switch can entail a maximum packet loss percentage in the order of 10% during 1
second.