title: Deep Learning-Based Intra Mode Derivation for Versatile Video Coding

abstract:
  In intra coding, Rate Distortion Optimization (RDO) is performed to achieve
the optimal intra mode from a pre-defined candidate list. The optimal intra
mode is also required to be encoded and transmitted to the decoder side besides
the residual signal, where lots of coding bits are consumed. To further improve
the performance of intra coding in Versatile Video Coding (VVC), an intelligent
intra mode derivation method is proposed in this paper, termed as Deep Learning
based Intra Mode Derivation (DLIMD). In specific, the process of intra mode
derivation is formulated as a multi-class classification task, which aims to
skip the module of intra mode signaling for coding bits reduction. The
architecture of DLIMD is developed to adapt to different quantization parameter
settings and variable coding blocks including non-square ones, which are
handled by one single trained model. Different from the existing deep learning
based classification problems, the hand-crafted features are also fed into the
intra mode derivation network besides the learned features from feature
learning network. To compete with traditional method, one additional binary
flag is utilized in the video codec to indicate the selected scheme with RDO.
Extensive experimental results reveal that the proposed method can achieve
2.28%, 1.74%, and 2.18% bit rate reduction on average for Y, U, and V
components on the platform of VVC test model, which outperforms the
state-of-the-art works.


title: Deep-Learning-Based Identification of LPV Models for Nonlinear Systems

abstract:
  The framework of Linear Parameter-Varying (LPV) systems is part of the modern
modeling and control design toolchain for addressing nonlinear system behaviors
through linear surrogate models. Despite the significant research effort spent
on LPV data-driven modeling, a key shortcoming of the current identification
theory is that often the scheduling variable is assumed to be a given measured
signal in the data set. In case of identifying an LPV model of a nonlinear
system, the selection of the scheduling, i.e., the scheduling map that
describes its relation to measurable signals in the system, is put on the
users' shoulder, with only limited supporting tools available. Although, such a
choice greatly affects the usability and the complexity of the resulting LPV
model. This paper presents a deep-learning based approach to provide joint
estimation of a scheduling map and an LPV state-space model of a nonlinear
system from input-output data. The approach has consistency guarantees under
general innovation type of noise conditions, and its efficiency is demonstrated
on the identification problem of a control moment gyroscope.


title:
"Am I Private and If So, how Many?" -- Using Risk Communication Formats
  for Making Differential Privacy Understandable

abstract:
  Mobility data is essential for cities and communities to identify areas for
necessary improvement. Data collected by mobility providers already contains
all the information necessary, but privacy of the individuals needs to be
preserved. Differential privacy (DP) defines a mathematical property which
guarantees that certain limits of privacy are preserved while sharing such
data, but its functionality and privacy protection are difficult to explain to
laypeople. In this paper, we adapt risk communication formats in conjunction
with a model for the privacy risks of DP. The result are privacy notifications
which explain the risk to an individual's privacy when using DP, rather than
DP's functionality. We evaluate these novel privacy communication formats in a
crowdsourced study. We find that they perform similarly to the best performing
DP communications used currently in terms of objective understanding, but did
not make our participants as confident in their understanding. We also
discovered an influence, similar to the Dunning-Kruger effect, of the
statistical numeracy on the effectiveness of some of our privacy communication
formats and the DP communication format used currently. These results generate
hypotheses in multiple directions, for example, toward the use of risk
visualization to improve the understandability of our formats or toward
adaptive user interfaces which tailor the risk communication to the
characteristics of the reader.


title:
Hidden unique possibilities of mathematical physics equations (Formalism
  of skew-symmetric forms)

abstract:
  It is shown that mathematical physics differential equations have properties
that allow describing processes such as the structures emergence, discrete
transitions, quantum jumps. The peculiarity is that such properties are hidden.
They do not follow directly from the mathematical physics equations but are
realized discretely in the solving process. This is due to the mathematical
physics equations integrability, which, as shown, can be realized only
discretely in the presence of any degrees of freedom. In this case, a
transition occurs from the original coordinate space with a solution that is
not a function (the solution derivatives do not compose a differential) to
integrable structures with a solution that is a discrete function. It is the
double solutions and spatial transitions that can describe the processes of the
emergence of any structures or phenomena. Due to hidden properties, the
mathematical physics equations have unique possibilities in describing physical
processes and phenomena that cannot be described in the framework of other
mathematical formalisms. Such results were obtained using skew-symmetric
differential forms.


title:
Transfer Attacks Revisited: A Large-Scale Empirical Study in Real
  Computer Vision Settings

abstract:
  One intriguing property of adversarial attacks is their "transferability" --
an adversarial example crafted with respect to one deep neural network (DNN)
model is often found effective against other DNNs as well. Intensive research
has been conducted on this phenomenon under simplistic controlled conditions.
Yet, thus far, there is still a lack of comprehensive understanding about
transferability-based attacks ("transfer attacks") in real-world environments.
  To bridge this critical gap, we conduct the first large-scale systematic
empirical study of transfer attacks against major cloud-based MLaaS platforms,
taking the components of a real transfer attack into account. The study leads
to a number of interesting findings which are inconsistent to the existing
ones, including: (1) Simple surrogates do not necessarily improve real transfer
attacks. (2) No dominant surrogate architecture is found in real transfer
attacks. (3) It is the gap between posterior (output of the softmax layer)
rather than the gap between logit (so-called $\kappa$ value) that increases
transferability. Moreover, by comparing with prior works, we demonstrate that
transfer attacks possess many previously unknown properties in real-world
environments, such as (1) Model similarity is not a well-defined concept. (2)
$L_2$ norm of perturbation can generate high transferability without usage of
gradient and is a more powerful source than $L_\infty$ norm. We believe this
work sheds light on the vulnerabilities of popular MLaaS platforms and points
to a few promising research directions.


title: Reconstruction of Videos Taken by a Non-Regular Sampling Sensor

abstract:
  Recently, it has been shown that a high resolution image can be obtained
without the usage of a high resolution sensor. The main idea has been that a
low resolution sensor is covered with a non-regular sampling mask followed by a
reconstruction of the incomplete high resolution image captured this way. In
this paper, a multi-frame reconstruction approach is proposed where a video is
taken by a non-regular sampling sensor and fully reconstructed afterwards. By
utilizing the temporal correlation between neighboring frames, the
reconstruction quality can be further enhanced. Compared to a state-of-the-art
single-frame reconstruction approach, this leads to a visually noticeable gain
in PSNR of up to 1.19 dB on average.


title:
Reducing Randomness of Non-Regular Sampling Masks for Image
  Reconstruction

abstract:
  Increasing spatial image resolution is an often required, yet challenging
task in image acquisition. Recently, it has been shown that it is possible to
obtain a high resolution image by covering a low resolution sensor with a
non-regular sampling mask. Due to the masking, however, some pixel information
in the resulting high resolution image is not available and has to be
reconstructed by an efficient image reconstruction algorithm in order to get a
fully reconstructed high resolution image. In this paper, the influence of
different sampling masks with a reduced randomness of the non-regularity on the
image reconstruction process is evaluated. Simulation results show that it is
sufficient to use sampling masks that are non-regular only on a smaller scale.
These sampling masks lead to a visually noticeable gain in PSNR compared to
arbitrary chosen sampling masks which are non-regular over the whole image
sensor size. At the same time, they simplify the manufacturing process and
allow for efficient storage.


title: "Gettr-ing" Deep Insights from the Social Network Gettr

abstract:
  As yet another alternative social network, Gettr positions itself as the
"marketplace of ideas" where users should expect the truth to emerge without
any administrative censorship. We looked deep inside the platform by analyzing
it's structure, a sample of 6.8 million posts, and the responses from a sample
of 124 Gettr users we interviewed to see if this actually is the case.
Administratively, Gettr makes a deliberate attempt to stifle any external
evaluation of the platform as collecting data is marred with unpredictable and
abrupt changes in their API. Content-wise, Gettr notably hosts pro-Trump
content mixed with conspiracy theories and attacks on the perceived "left."
It's social network structure is asymmetric and centered around prominent
right-thought leaders, which is characteristic for all alt-platforms. While
right-leaning users joined Gettr as a result of a perceived freedom of speech
infringement by the mainstream platforms, left-leaning users followed them in
numbers as to "keep up with the misinformation." We contextualize these
findings by looking into the Gettr's user interface design to provide a
comprehensive insight into the incentive structure for joining and competing
for the truth on Gettr.


title:
Reconstruction of images taken by a pair of non-regular sampling sensors
  using correlation based matching

abstract:
  Multi-view image acquisition systems with two or more cameras can be rather
costly due to the number of high resolution image sensors that are required.
Recently, it has been shown that by covering a low resolution sensor with a
non-regular sampling mask and by using an efficient algorithm for image
reconstruction, a high resolution image can be obtained. In this paper, a
stereo image reconstruction setup for multi-view scenarios is proposed. A scene
is captured by a pair of non-regular sampling sensors and by incorporating
information from the adjacent view, the reconstruction quality can be
increased. Compared to a state-of-the-art single-view reconstruction algorithm,
this leads to a visually noticeable average gain in PSNR of 0.74 dB.


title: Declipping of Speech Signals Using Frequency Selective Extrapolation

abstract:
  The reconstruction of clipped speech signals is an important task in audio
signal processing to achieve an enhanced audio quality for further processing.
In this paper, Frequency Selective Extrapolation (FSE), which is commonly used
for error concealment or the reconstruction of incomplete image data, is
adapted to be able to restore audio signals which are distorted from clipping.
For this, FSE generates a model of the signal as an iterative superposition of
Fourier basis functions. Clipped samples can then be replaced by estimated
samples from the model. The performance of the proposed algorithm is evaluated
by using different speech test data sets. Compared to other state-of-the-art
declipping algorithms, this leads to a maximum gain in SNR of up to 3:5 dB and
an average gain of 1 dB.


title: Second harmonic generation at the edge of a two-dimensional electron gas

abstract:
  We show that driving a two-dimensional electron gas by an in-plane electric
field oscillating at the frequency $\omega$ gives rise to an electric current
at $2\omega$ flowing near the edge of the system. This current has both
parallel and perpendicular to the edge components, which emit electromagnetic
waves at $2\omega$ with different polarizations. We develop a microscopic
theory of such an edge second harmonic generation and calculate the edge
current at $2\omega$ in different regimes of electron transport and electric
field screening. We also show that at high frequencies the spatial profile of
the edge current contains oscillations caused by excitation of plasma waves.


title: Optimal Ratchet Potentials for Run-and-Tumble particles

abstract:
  Run-and-Tumble particles, mimicking the behaviour of microorganisms like E.
coli, are a paradigmatic model of active matter. Due to self-propulsion, their
random and undirected motion can be rectified in a ratchet potential. Using
perturbative field theory, we determine the shape of the potential that
produces the maximum particle current as a function of the particles'
parameters.


title: Utility Functions for Human/Robot Interaction

abstract:
  In this paper, we place ourselves in the context of human robot interaction
and address the problem of cognitive robot modelling. More precisely we are
investigating properties of a utility-based model that will govern a robot's
actions. The novelty of this approach lies in embedding the responsibility of
the robot over the state of affairs into the utility model via a utility
aggregation function. We describe desiderata for such a function and consider
related properties.


title: Characterizing (non-)Markovianity through Fisher Information

abstract:
  A non-isolated physical system typically loses information to its
environment, and when such loss is irreversible the evolution is said to be
Markovian. Non-Markovian effects are studied by monitoring how information
quantifiers, such as the distance between physical states, evolve in time. Here
we show that the Fisher information metric emerges as the natural object to
study in this context; we fully characterize the relation between its
contractivity properties and Markovianity, both from the mathematical and
operational point of view. We prove, both for classical and quantum dynamics,
that Markovianity is equivalent to the monotonous contraction of the Fisher
metric at all points of the set of states. At the same time, operational
witnesses of non-Markovianity based on the dilation of the Fisher distance
cannot, in general, detect all non-Markovian evolutions, unless specific
physical postprocessing is applied to the dynamics. Finally, we show for the
first time that non-Markovian dilations of Fisher distance between states at
any time correspond to backflow of information about the initial state of the
dynamics at time 0, via Bayesian retrodiction.


title:
The impact of thermal noise on kink propagation through a heterogeneous
  system

abstract:
  The impact of thermal noise on kink motion through the curved region of the
long Josephson junction is studied. On the basis of the Fokker-Planck equation
the analytical formula that describes the probability of transmission of the
kink over the potential barrier is proposed. The analytical results are
compared with the simulations based on the field model.


title: Spin functional renormalization group for dimerized quantum spin systems

abstract:
  We investigate dimerized quantum spin systems using the spin functional
renormalization group approach proposed by Krieg and Kopietz [Phys. Rev. B 99,
060403(R) (2019)] which directly focuses on the physical spin correlation
functions and avoids the representation of the spins in terms of fermionic or
bosonic auxiliary operators. Starting from decoupled dimers as initial
condition for the renormalization group flow equations, we obtain the spectrum
of the triplet excitations as well as the magnetization in the quantum
paramagnetic, ferromagnetic, and thermally disordered phases at all
temperatures. Moreover, we compute the full phase diagram of a weakly coupled
dimerized spin system in three dimensions, including the correct mean field
critical exponents at the two quantum critical points.


title:
Hyper-holomorphic connections on vector bundles on hyper-K\"ahler
  manifolds

abstract:
  We study infinitesimal deformations of autodual and hyper-holomorphic
connections on complex vector bundles on hyper-K\"ahler manifolds of arbitrary
dimension. We describe the DG Lie algebra controlling this deformation problem,
and prove that it is formal when the connection is hyper-holomorphic. Moreover,
we prove associative formality for derived endomorphisms of a holomorphic
vector bundle admitting a projectively hyper-holomorphic connection.


title: Invariant Descriptors for Intrinsic Reflectance Optimization

abstract:
  Intrinsic image decomposition aims to factorize an image into albedo
(reflectance) and shading (illumination) sub-components. Being ill-posed and
under-constrained, it is a very challenging computer vision problem. There are
infinite pairs of reflectance and shading images that can reconstruct the same
input. To address the problem, Intrinsic Images in the Wild provides an
optimization framework based on a dense conditional random field (CRF)
formulation that considers long-range material relations. We improve upon their
model by introducing illumination invariant image descriptors: color ratios.
The color ratios and the reflectance intrinsic are both invariant to
illumination and thus are highly correlated. Through detailed experiments, we
provide ways to inject the color ratios into the dense CRF optimization. Our
approach is physics-based, learning-free and leads to more accurate and robust
reflectance decompositions.


title:
A CRASH simulation of the contribution of binary stars to the epoch of
  reionization

abstract:
  We use a set of 3D radiative transfer simulations to study the effect that a
large fraction of binary stars in galaxies during the epoch of reionization has
on the physical properties of the intergalactic medium (i.e. the gas
temperature and the ionization state of hydrogen and helium), on the topology
of the ionized bubbles and on the 21 cm power spectra. Consistently to previous
literature, we find that the inclusion of binary stars can speed up the
reionization process of HI and HeI, while HeII reionization is still dominated
by more energetic sources, especially accreting black holes. The earlier
ionization attained with binary stars allows for more time for cooling and
recombination, so that gas fully ionized by binary stars is typically colder
than that ionized by single stars at any given redshift. With the same volume
averaged ionization fraction, the inclusion of binary stars results in fewer
small ionized bubbles and more large ones, with visible effects also on the
large scales of the 21 cm power spectrum.


title:
General Incremental Learning with Domain-aware Categorical
  Representations

abstract:
  Continual learning is an important problem for achieving human-level
intelligence in real-world applications as an agent must continuously
accumulate knowledge in response to streaming data/tasks. In this work, we
consider a general and yet under-explored incremental learning problem in which
both the class distribution and class-specific domain distribution change over
time. In addition to the typical challenges in class incremental learning, this
setting also faces the intra-class stability-plasticity dilemma and intra-class
domain imbalance problems. To address above issues, we develop a novel
domain-aware continual learning method based on the EM framework. Specifically,
we introduce a flexible class representation based on the von Mises-Fisher
mixture model to capture the intra-class structure, using an
expansion-and-reduction strategy to dynamically increase the number of
components according to the class complexity. Moreover, we design a bi-level
balanced memory to cope with data imbalances within and across classes, which
combines with a distillation loss to achieve better inter- and intra-class
stability-plasticity trade-off. We conduct exhaustive experiments on three
benchmarks: iDigits, iDomainNet and iCIFAR-20. The results show that our
approach consistently outperforms previous methods by a significant margin,
demonstrating its superiority.


title:
Assessing the impact of soil moisture-temperature coupling on
  temperature extremes over the Indian region

abstract:
  While previous model sensitivity studies have mainly focused on discerning
the soil moisture-precipitation feedback processes over the Indian region, the
present study investigates the impact of soil moisture-temperature (SM-T)
coupling on the temperature extremes (ExT) using the high-resolution (~60 km)
model simulations. These simulations include the control and soil moisture (SM)
sensitivity experiments (DRY-SM and WET-SM) initialized by perturbing
(decreasing/increasing) SM from the historical (HIST: 1951-2010) and future 4K
warming (FUT: 2051-2100) control runs. The analysis identifies the transitional
regions of north-central India (NCI) as the hotspot of strong SM-T coupling.
Over NCI, the HIST experiment shows an occurrence of 4-5 extreme events per
year, with an average duration of 5-6 days per event and intensity exceeding
46oC. Whereas, FUT estimates indicate relatively severe, long-lasting, and more
frequent extreme events. The SM sensitivity experiments reveal the significant
influence of SM-T coupling on the ExT over NCI in both historical and future
climates. We find that the DRY-SM results in significant enhancement of
frequency, duration and intensity of ExT, in contrast to WET-SM. We note that
the difference between DRY-SM and WET-SM 50-year return value of the block
maxima GEV fit can reach upto 1.25oC and 3oC for historical and future climate,
respectively. The enhanced (reduced) extreme temperature conditions in DRY-SM
(WET-SM) simulation are caused by the intensification (abridgement) of sensible
heat flux by limiting (intensifying) available total energy for evaporative
cooling due to faster (slower) dissipation of positive soil moisture anomalies
(also called as soil moisture memory). In addition, the influence of SM on ExT
over NCI is found to be larger during the post-monsoon season as compared to
the pre-monsoon and monsoon seasons.


title:
Learning the Ordering of Coordinate Compounds and Elaborate Expressions
  in Hmong, Lahu, and Chinese

abstract:
  Coordinate compounds (CCs) and elaborate expressions (EEs) are coordinate
constructions common in languages of East and Southeast Asia. Mortensen (2006)
claims that (1) the linear ordering of EEs and CCs in Hmong, Lahu, and Chinese
can be predicted via phonological hierarchies and (2) these phonological
hierarchies lack a clear phonetic rationale. These claims are significant
because morphosyntax has often been seen as in a feed-forward relationship with
phonology, and phonological generalizations have often been assumed to be
phonetically "natural". We investigate whether the ordering of CCs and EEs can
be learned empirically and whether computational models (classifiers and
sequence labeling models) learn unnatural hierarchies similar to those posited
by Mortensen (2006). We find that decision trees and SVMs learn to predict the
order of CCs/EEs on the basis of phonology, with DTs learning hierarchies
strikingly similar to those proposed by Mortensen. However, we also find that a
neural sequence labeling model is able to learn the ordering of elaborate
expressions in Hmong very effectively without using any phonological
information. We argue that EE ordering can be learned through two independent
routes: phonology and lexical distribution, presenting a more nuanced picture
than previous work. [ISO 639-3:hmn, lhu, cmn]


title:
Bad metal and negative compressibility transitions in a two-band Hubbard
  model

abstract:
  We analyze the paramagnetic state of a two-band Hubbard model with finite
Hund's coupling close to integer filling at n=2 in two spacial dimensions.
Previously, a Mott metal-insulator transition was established at $n=2$ with a
coexistence region of a metallic and a bad metal state in the vicinity of that
integer filling. The coexistence region ends at a critical point beyond which a
charge instability persists. Here we investigate the transition into negative
electronic compressibility states for an extended filling range close to n=2
within a slave boson setup. We analyze the separate contributions from the
(fermionic) quasiparticles and the (bosonic) multiparticle incoherent
background and find that the total compressibility depends on a subtle
interplay between the quasiparticle excitations and collective fields.
Implementing a Blume-Emery-Griffiths model approach for the slave bosons, which
mimics the bosonic fields by Ising-like pseudospins, we suggest a feedback
mechanism between these fields and the fermionic degrees of freedom. We argue
that the negative compressibility can be sustained for heterostructures of such
strongly correlated planes and results in a large capacitance of these
structures. The strong density dependence of these capacitances allows to tune
them through small electronic density variations. Moreover, by resistive
switching from a Mott insulating state to a metallic state through short
electric pulses, transitions between fairly different capacitances are within
reach.


title:
Efficient scheme for realizing a multiplex-controlled phase gate with
  photonic qubits in circuit quantum electrodynamics

abstract:
  We propose an efficient scheme to implement a multiplex-controlled phase gate
with multiple photonic qubits simultaneously controlling one target photonic
qubit based on circuit quantum electrodynamics (QED). For convenience, we
denote this multiqubit gate as MCP gate. The gate is realized by using a
two-level coupler to couple multiple cavities. The coupler here is a
superconducting qubit. This scheme is simple because the gate implementation
requires only \textit{one step} of operation. In addition, this scheme is quite
general because the two logic states of each photonic qubit can be encoded with
a vacuum state and an arbitrary non-vacuum state (e.g., a Fock state, a
superposition of Fock states, a cat state, or a coherent state, etc.) which is
orthogonal or quasi-orthogonal to the vacuum state. The scheme has some
additional advantages: Because only two levels of the coupler are used, i.e.,
no auxiliary levels are utilized, decoherence from higher energy levels of the
coupler is avoided; the gate operation time does not depend on the number of
qubits; and the gate is implemented deterministically because no measurement is
applied. As an example, we numerically analyze the circuit-QED based
experimental feasibility of implementing a three-qubit MCP gate with photonic
qubits each encoded via a vacuum state and a cat state. The scheme can be
applied to accomplish the same task in a wide range of physical system, which
consists of multiple microwave or optical cavities coupled to a two-level
coupler such as a natural or artificial atom.


title:
POSTER: A Pyramid Cross-Fusion Transformer Network for Facial Expression
  Recognition

abstract:
  Facial Expression Recognition (FER) has received increasing interest in the
computer vision community. As a challenging task, there are three key issues
especially prevalent in FER: inter-class similarity, intra-class discrepancy,
and scale sensitivity. Existing methods typically address some of these issues,
but do not tackle them all in a unified framework. Therefore, in this paper, we
propose a two-stream Pyramid crOss-fuSion TransformER network (POSTER) that
aims to holistically solve these issues. Specifically, we design a
transformer-based cross-fusion paradigm that enables effective collaboration of
facial landmark and direct image features to maximize proper attention to
salient facial regions. Furthermore, POSTER employs a pyramid structure to
promote scale invariance. Extensive experimental results demonstrate that our
POSTER outperforms SOTA methods on RAF-DB with 92.05%, FERPlus with 91.62%,
AffectNet (7 cls) with 67.31%, and AffectNet (8 cls) with 63.34%, respectively.


title: On the Effectiveness of Binary Emulation in Malware Classification

abstract:
  Malware authors are continuously evolving their code base to include
counter-analysis methods that can significantly hinder their detection and
blocking. While the execution of malware in a sandboxed environment may provide
a lot of insightful feedback about what the malware actually does in a machine,
anti-virtualisation and hooking evasion methods may allow malware to bypass
such detection methods. The main objective of this work is to complement
sandbox execution with the use of binary emulation frameworks. The core idea is
to exploit the fact that binary emulation frameworks may quickly test samples
quicker than a sandbox environment as they do not need to open a whole new
virtual machine to execute the binary. While with this approach, we lose the
granularity of the data that can be collected through a sandbox, due to
scalability issues, one may need to simply determine whether a file is
malicious or to which malware family it belongs. To this end, we record the API
calls that are performed and use them to explore the efficacy of using them as
features for binary and multiclass classification. Our extensive experiments
with real-world malware illustrate that this approach is very accurate,
achieving state-of-the art outcomes with a statistically robust set of
classification experiments while simultaneously having a relatively low
computational overhead compared to traditional sandbox approaches. In fact, we
compare the binary analysis results with a commercial sandbox, and our
classification outperforms it at the expense of the fine-grained results that a
sandbox provides.


title:
Predicting Berth Stay for Tanker Terminals: A Systematic and Dynamic
  Approach

abstract:
  Given the trend of digitization and increasing number of maritime transport,
prediction of vessel berth stay has been triggered for requirements of
operation research and scheduling optimization problem in the era of maritime
big data, which takes a significant part in port efficiency and maritime
logistics enhancement. This study proposes a systematic and dynamic approach of
predicting berth stay for tanker terminals. The approach covers three
innovative aspects: 1) Data source employed is multi-faceted, including cargo
operation data from tanker terminals, time-series data from automatic
identification system (AIS), etc. 2) The process of berth stay is decomposed
into multiple blocks according to data analysis and information extraction
innovatively, and practical operation scenarios are also developed accordingly.
3) The predictive models of berth stay are developed on the basis of prior data
analysis and information extraction under two methods, including regression and
decomposed distribution. The models are evaluated under four dynamic scenarios
with certain designated cargoes among two different terminals. The evaluation
results show that the proposed approach can predict berth stay with the
accuracy up to 98.81% validated by historical baselines, and also demonstrate
the proposed approach has dynamic capability of predicting berth stay among the
scenarios. The model may be potentially applied for short-term pilot-booking or
scheduling optimizations within a reasonable time frame for advancement of port
intelligence and logistics efficiency.


title: Gone Quishing: A Field Study of Phishing with Malicious QR Codes

abstract:
  The COVID-19 pandemic enabled "quishing", or phishing with malicious QR
codes, as they became a convenient go-between for sharing URLs, including
malicious ones. To explore the quishing phenomenon, we conducted a
173-participant study where we used a COVID-19 digital passport sign-up trial
with a malicious QR code as a pretext. We found that 67 % of the participants
were happy to sign-up with their Google or Facebook credentials, 18.5% to
create a new account, and only 14.5% to skip on the sign-up. Convenience was
the single most cited factor for the willingness to yield participants'
credentials. Reluctance of linking personal accounts with new services was the
reason for creating a new account or skipping the registration. We also
developed a Quishing Awareness Scale (QAS) and found a significant relationship
between participants' QR code behavior and their sign-up choices: the ones
choosing to sign-up with Facebook scored the lowest while the one choosing to
skip the highest on average. We used our results to propose quishing awareness
training guidelines and develop and test usable security indicators for warning
users about the threat of quishing.


title: Games on AF-algebras

abstract:
  We analyze $\mathrm{C}^\ast$-algebras, particularly AF-algebras, and their
$K_0$-groups in the context of the infinitary logic $\mathcal{L}_{\omega_1
\omega}$. Given two separable unital AF-algebras $A$ and $B$, and considering
their $K_0$-groups as ordered unital groups, we prove that $K_0(A)
\equiv_{\omega \cdot \alpha} K_0(B)$ implies $A \equiv_\alpha B$, where $M
\equiv_\beta N$ means that $M$ and $N$ agree on all sentences of quantifier
rank at most $\beta$. This implication is proved using techniques from
Elliott's classification of separable AF-algebras, together with an adaptation
of the Ehrenfeucht-Fra\"iss\'e game to the metric setting. We use moreover this
result to build a family $\{ A_\alpha \}_{\alpha < \omega_1}$ of pairwise
non-isomorphic separable simple unital AF-algebras which satisfy $A_\alpha
\equiv_\alpha A_\beta$ for every $\alpha < \beta$. In particular, we obtain a
set of separable simple unital AF-algebras of arbitrarily high Scott rank.
Next, we give a partial converse to the aforementioned implication, showing
that $A \otimes \mathcal{K} \equiv_{\omega + 2 \cdot \alpha +2} B \otimes
\mathcal{K}$ implies $K_0(A) \equiv_\alpha K_0(B)$, for every unital
$\mathrm{C}^\ast$-algebras $A$ and $B$.


title:
Stochastic Gradient-based Fast Distributed Multi-Energy Management for
  an Industrial Park with Temporally-Coupled Constraints

abstract:
  Contemporary industrial parks are challenged by the growing concerns about
high cost and low efficiency of energy supply. Moreover, in the case of
uncertain supply/demand, how to mobilize delay-tolerant elastic loads and
compensate real-time inelastic loads to match multi-energy generation/storage
and minimize energy cost is a key issue. Since energy management is hardly to
be implemented offline without knowing statistical information of random
variables, this paper presents a systematic online energy cost minimization
framework to fulfill the complementary utilization of multi-energy with
time-varying generation, demand and price. Specifically to achieve
charging/discharging constraints due to storage and short-term energy
balancing, a fast distributed algorithm based on stochastic gradient with
two-timescale implementation is proposed to ensure online implementation. To
reduce the peak loads, an incentive mechanism is implemented by estimating
users' willingness to shift. Analytical results on parameter setting are also
given to guarantee feasibility and optimality of the proposed design. Numerical
results show that when the bid-ask spread of electricity is small enough, the
proposed algorithm can achieve the close-to-optimal cost asymptotically.


title:
The Supersymmetric Neveu-Schwarz Branes of Non-Relativistic String
  Theory

abstract:
  We construct the basic Neveu-Schwarz (NS) brane solutions of non-relativistic
string theory using longitudinal T-duality as a solution generating technique.
Extending the NS background fields to a supergravity multiplet, we verify that
all solutions we find are half-supersymmetric. The two perturbative solutions
we find both have an interpretation as the background geometry outside a
string-like object. Correspondingly, we refer to these non-Lorentzian
backgrounds as winding string and unwound string solution. Whereas the winding
string is part of the on-shell spectrum of non-relativistic string theory, the
unwound string only makes sense off-shell where it mediates the instantaneous
gravitational force. Seen from the nine-dimensional point of view, we find that
the winding string solution is sourced by a non-relativistic massive particle
and that the unwound string solution is sourced by a massless Galilean particle
of zero colour and spin. We explain how these two string solutions fit into a
discrete lightcone quantization of string theory. We shortly discuss the basic
NS five-brane and Kaluza-Klein monopole solutions and show that they are both
half-supersymmetric.


title: Generative Adversarial Method Based on Neural Tangent Kernels

abstract:
  The recent development of Generative adversarial networks (GANs) has driven
many computer vision applications. Despite the great synthesis quality,
training GANs often confronts several issues, including non-convergence, mode
collapse, and gradient vanishing. There exist several workarounds, for example,
regularizing Lipschitz continuity and adopting Wasserstein distance. Although
these methods can partially solve the problems, we argue that the problems are
result from modeling the discriminator with deep neural networks. In this
paper, we base on newly derived deep neural network theories called Neural
Tangent Kernel (NTK) and propose a new generative algorithm called generative
adversarial NTK (GA-NTK). The GA-NTK models the discriminator as a Gaussian
Process (GP). With the help of the NTK theories, the training dynamics of
GA-NTK can be described with a closed-form formula. To synthesize data with the
closed-form formula, the objectives can be simplified into a single-level
adversarial optimization problem. We conduct extensive experiments on
real-world datasets, and the results show that GA-NTK can generate images
comparable to those by GANs but is much easier to train under various
conditions. We also study the current limitations of GA-NTK and propose some
workarounds to make GA-NTK more practical.


title:
FFT-based investigation of the shear stress distribution in
  face-centered cubic polycrystals

abstract:
  The onset of nonlinear effects in metals, such as plasticity and damage, is
strongly influenced by the heterogeneous stress distribution at the grain
level. This work is devoted to studying the local stress distribution of shear
stresses resolved in the slip systems as the critical driving force for plastic
deformations. Specific grain orientations with respect to load direction are
investigated in the linear elastic regime and at incipient plastic deformations
based on a large ensemble of microstructures. The elastic anisotropy of the
single crystal is found to have a crucial influence on the scatter of the
stress distribution, whereas the Young's modulus in the respective crystal
direction governs the mean stress in the grain. It is further demonstrated
that, for higher anisotropy, the shear stresses deviate from the normal
distribution and are better approximated by a log-normal fit. Comparing the
full-field simulations to the Maximum Entropy Method (MEM), reveals that the
MEM provides an excellent prediction up to the second statistical moment in the
linear elastic range. In a study on the spatial distribution of shear stresses,
the grain boundary is identified as a region of pronounced stress fluctuations
and as the starting point of yielding during the elastic-plastic transition.


title: Dynamic super-resolution in particle tracking problems

abstract:
  Particle tracking in biological imaging is concerned with reconstructing the
trajectories, locations, or velocities of the targeting particles. The standard
approach of particle tracking consists of two steps: first reconstructing
statically the source locations in each time step, and second applying tracking
techniques to obtain the trajectories and velocities. In contrast, the dynamic
reconstruction seeks to simultaneously recover the source locations and
velocities from all frames, which enjoys certain advantages. In this paper, we
provide a rigorous mathematical analysis for the resolution limit of
reconstructing source number, locations, and velocities by general dynamical
reconstruction in particle tracking problems, by which we demonstrate the
possibility of achieving super-resolution for the dynamic reconstruction. We
show that when the location-velocity pairs of the particles are separated
beyond certain distances (the resolution limits), the number of particles and
the location-velocity pair can be stably recovered. The resolution limits are
related to the cut-off frequency of the imaging system, signal-to-noise ratio,
and the sparsity of the source. By these estimates, we also derive a stability
result for a sparsity-promoting dynamic reconstruction. In addition, we further
show that the reconstruction of velocities has a better resolution limit which
improves constantly as the particles moving. This result is derived by an
observation that the inherent cut-off frequency for the velocity recovery can
be viewed as the total observation time multiplies the cut-off frequency of the
imaging system, which may lead to a better resolution limit as compared to the
one for each diffraction-limited frame. It is anticipated that this observation
can inspire new reconstruction algorithms that improve the resolution of
particle tracking in practice.


title: Floer homology and right-veering monodromy

abstract:
  We prove that the knot Floer complex of a fibered knot detects whether the
monodromy of its fibration is right-veering. In particular, this leads to a
purely knot Floer-theoretic characterization of tight contact structures, by
the work of Honda, Kazez, and Matic. Our proof makes use of the relationship
between the Heegaard Floer homology of mapping tori and the symplectic Floer
homology of area-preserving surface diffeomorphisms. We describe applications
of this work to Dehn surgeries and taut foliations.


title:
A new endstation for extreme-ultraviolet spectroscopy of free clusters
  and nanodroplets

abstract:
  We present a new endstation for the AMOLine of the ASTRID2 synchrotron at
Aarhus University, which combines a cluster and nanodroplet beam source with a
velocity map imaging and time-of-flight spectrometer for coincidence imaging
spectroscopy. Extreme-ultraviolet spectroscopy of free nanoparticles is a
powerful tool for studying the photophysics and photochemistry of resonantly
excited or ionized nanometer-sized condensed-phase systems. Here we demonstrate
this capability by performing photoelectron-photoion coincidence (PEPICO)
experiments with pure and doped superfluid helium nanodroplets. Different
doping options and beam sources provide a versatile platform to generate
various van der Waals clusters as well as He nanodroplets. We present a
detailed characterization of the new setup and present examples of its use for
measuring high-resolution yield spectra of charged particles, time-of-flight
ion mass spectra, anion-cation coincidence spectra, multi-coincidence electron
spectra and angular distributions. A particular focus of the research with this
new endstation is on intermolecular charge and energy-transfer processes in
heterogeneous nanosystems induced by valence-shell excitation and ionization.


title:
Probing the spin dimensionality in single-layer CrSBr van der Waals
  heterostructures by magneto-transport measurements

abstract:
  Two-dimensional (2D) magnetic materials offer unprecedented opportunities
both in terms of fundamental concepts and applied devices with special
relevance in the fields of spintronics and magnonics. Beyond the pioneering
studies on CrI3 and Cr2Ge2Te6, the family of 2D magnets has expanded to layered
antiferromagnets with different spin anisotropies and spin textures. However,
all these compounds are highly insulating, thus limiting their possibilities
for being integrated into devices. Of particular interest is the case of the
layered metamagnet CrSBr, a 2D semiconductor formed by ferromagnetic layers (Tc
~ 150 K) coupled antiferromagnetically between them. This material exhibits a
rich physical scenario, including thermal spin dimensionality crossovers and
low-temperature hidden order (T* ~ 40 K). Here, we inspect the
magneto-transport properties of monolayer, bilayer and trilayer CrSBr
integrated into vertical van der Waals heterostructures. Our results in the
monolayer limit demonstrate (1) the marked low dimensional character of the
ferromagnetic layer, with short-range correlations extending at temperatures
well above Tc, (2) a spin anisotropy, with the spins spontaneously aligned
along the easy axis (b) of the plane, (3) a reorientation of these spins along
a and c upon applying a moderate magnetic field in these directions, and (4)
the appearance of field-induced phases in these two directions below ca. 30-40
K due to a cooperative freezing of the spins. In the multilayer case, a spin
valve-like behavior is also observed characterized by a negative MR strongly
enhanced below T*. Overall, the present results, supported by first-principles
calculations, show that the monolayer and bilayer of CrSBr capture most of the
physics present in the bulk, offering new insights into the physics of 2D
magnets and the integration of these layers into vertical spintronic devices.


title: Variations around K\"ahler hyperbolicity and Lang's conjecture

abstract:
  We consider several variants of the notion of K\"ahler hyperbolic manifold
introduced in the early '90s by M. Gromov, and establish their basic features.
We then investigate their spectral properties and show a spectral gap result.
As applications, we study the geometry of subvarieties and of entire curves,
verifying -- among other things -- various aspects of Lang's conjecture for
these classes of manifolds.


title:
Quenching in Cosmic Sheets: Tracing the Impact of Large Scale Structure
  Collapse on the Evolution of Dwarf Galaxies

abstract:
  Dwarf galaxies are thought to quench primarily due to environmental processes
most typically occurring in galaxy groups and clusters or around single,
massive galaxies. However, at earlier epochs, ($5 < z < 2$), the collapse of
large scale structure (forming Zel'dovich sheets and subsequently filaments of
the cosmic web) can produce volume-filling accretion shocks which elevate large
swaths of the intergalactic medium (IGM) in these structures to a hot ($T>10^6$
K) phase. We study the impact of such an event on the evolution of central
dwarf galaxies ($5.5 < \log M_* < 8.5$) in the field using a spatially large,
high resolution cosmological zoom simulation which covers the cosmic web
environment between two protoclusters. We find that the shock-heated sheet acts
as an environmental quencher much like clusters and filaments at lower
redshift, creating a population of quenched, central dwarf galaxies. Even
massive dwarfs which do not quench are affected by the shock, with reductions
to their sSFR and gas accretion. This process can potentially explain the
presence of isolated quenched dwarf galaxies, and represents an avenue of
pre-processing, via which quenched satellites of bound systems quench before
infall.


title:
Identifying Experts in Question \& Answer Portals: A Case Study on Data
  Science Competencies in Reddit

abstract:
  The irreplaceable key to the triumph of Question & Answer (Q&A) platforms is
their users providing high-quality answers to the challenging questions posted
across various topics of interest. Recently, the expert finding problem
attracted much attention in information retrieval research. In this work, we
inspect the feasibility of supervised learning model to identify data science
experts in Reddit. Our method is based on the manual coding results where two
data science experts labelled expert, non-expert and out-of-scope comments. We
present a semi-supervised approach using the activity behaviour of every user,
including Natural Language Processing (NLP), crowdsourced and user feature
sets. We conclude that the NLP and user feature sets contribute the most to the
better identification of these three classes It means that this method can
generalise well within the domain. Moreover, we present different types of
users, which can be helpful to detect various types of users in the future.


title:
Seeded graph matching for the correlated Wigner model via the projected
  power method

abstract:
  In the graph matching problem we observe two graphs $G,H$ and the goal is to
find an assignment (or matching) between their vertices such that some measure
of edge agreement is maximized. We assume in this work that the observed pair
$G,H$ has been drawn from the correlated Wigner model -- a popular model for
correlated weighted graphs -- where the entries of the adjacency matrices of
$G$ and $H$ are independent Gaussians and each edge of $G$ is correlated with
one edge of $H$ (determined by the unknown matching) with the edge correlation
described by a parameter $\sigma\in [0,1)$. In this paper, we analyse the
performance of the projected power method (PPM) as a seeded graph matching
algorithm where we are given an initial partially correct matching (called the
seed) as side information. We prove that if the seed is close enough to the
ground-truth matching, then with high probability, PPM iteratively improves the
seed and recovers the ground-truth matching (either partially or exactly) in
$\mathcal{O}(\log n)$ iterations. Our results prove that PPM works even in
regimes of constant $\sigma$, thus extending the analysis in (Mao et al.,2021)
for the sparse Erd\"os-Renyi model to the (dense) Wigner model. As a byproduct
of our analysis, we see that the PPM framework generalizes some of the
state-of-art algorithms for seeded graph matching. We support and complement
our theoretical findings with numerical experiments on synthetic data.


title:
R.E. Bruck, proof mining and a rate of asymptotic regularity for ergodic
  averages in Banach spaces

abstract:
  We analyze a proof of Bruck to obtain an explicit rate of asymptotic
regularity for Ces\`aro means in uniformly convex Banach spaces. Our rate will
only depend on a norm bound and a modulus $\eta$ of uniform convexity. One
ingredient for the proof by Bruck is a result of Pisier, which shows that every
uniformly convex (in fact every uniformly nonsquare) Banach space has some
Rademacher type $q>1$ with a suitable constant $C_q$. We explicitly determine
$q$ and $C_q$, which only depend on the single value $\eta(1)$ of our modulus.
Beyond these specific results, we summarize how work of Bruck has inspired
developments in the proof mining program, which applies tools from logic to
obtain results in various areas of mathematics.


title: Dynamical Mahler Measure: A survey and some recent results

abstract:
  We study the dynamical Mahler measure of multivariate polynomials and present
dynamical analogues of various results from the classical Mahler measure as
well as examples of formulas allowing the computation of the dynamical Mahler
measure in certain cases. We discuss multivariate analogues of dynamical
Kronecker's Lemma and present some improvements on the result for two variables
due to Carter, Lal\'in, Manes, Miller, and Mocz.


title: Huisken-Yau-type uniqueness for area-constrained Willmore spheres

abstract:
  Let $(M,g)$ be a Riemannian $3$-manifold that is asymptotic to Schwarzschild.
We study the existence of large area-constrained Willmore spheres $\Sigma
\subset M$ with non-negative Hawking mass and inner radius $\rho$ dominated by
the area radius $\lambda$. If the scalar curvature of $(M,g)$ is non-negative,
we show that no such surfaces with $\log \lambda \ll \rho$ exist. This answers
a question of G. Huisken.


title:
Linking atmospheric chemistry of the hot Jupiter HD 209458b to its
  formation location through infrared transmission and emission spectra

abstract:
  The elemental ratios of carbon, nitrogen, and oxygen in the atmospheres of
hot Jupiters may hold clues to their formation locations in the protostellar
disc. In this work, we adopt gas phase chemical abundances of C, N and O from
several locations in a disc chemical kinetics model as sources for the envelope
of the hot Jupiter HD 209458b and evolve the planet's atmospheric composition
using a 1D chemical kinetics model, treating both vertical mixing and
photochemistry. We consider two atmospheric pressure-temperature profiles, one
with and one without a thermal inversion. From each of the resulting 32
atmospheric composition profiles, we find that the molecules CH4, NH3, HCN, and
C2H2 are more prominent in the atmospheres computed using a realistic
non-inverted P-T profile in comparison to a prior equilibrium chemistry based
work which used an analytical P-T profile. We also compute the synthetic
transmission and emission spectra for these atmospheres and find that many
spectral features vary with the location in the disc where the planet's
envelope was accreted. By comparing with the species detected using the latest
high-resolution ground-based observations, our model suggests HD 209458b could
have accreted most of its gas between the CO2 and CH4 icelines with a super
solar C/O ratio from its protostellar disc, which in turn directly inherited
its chemical abundances from the protostellar cloud. Finally, we simulate
observing the planet with the James Webb Space Telescope (JWST) and show that
differences in spectral signatures of key species can be recognized. Our study
demonstrates the enormous importance of JWST in providing new insights into hot
Jupiter's formation environments.


title: Finding a Winning Strategy for Wordle is NP-complete

abstract:
  In this paper, we give a formal definition of the popular word-guessing game
Wordle. We show that, in general, determining if a given Wordle instance admits
a winning strategy is NP-complete. We also show that given a Wordle instance of
size $N$, a winning strategy that uses $g$ guesses in the worst case (if any)
can be found in time $N^{O(g)}$.


title: Improving LSHADE by means of a pre-screening mechanism

abstract:
  Evolutionary algorithms have proven to be highly effective in continuous
optimization, especially when numerous fitness function evaluations (FFEs) are
possible. In certain cases, however, an expensive optimization approach (i.e.
with relatively low number of FFEs) must be taken, and such a setting is
considered in this work. The paper introduces an extension to the well-known
LSHADE algorithm in the form of a pre-screening mechanism (psLSHADE). The
proposed pre-screening relies on the three following components: a specific
initial sampling procedure, an archive of samples, and a global linear
meta-model of a fitness function that consists of 6 independent transformations
of variables. The pre-screening mechanism preliminary assesses the trial
vectors and designates the best one of them for further evaluation with the
fitness function. The performance of psLSHADE is evaluated using the CEC2021
benchmark in an expensive scenario with an optimization budget of 10^2-10^4
FFEs per dimension. We compare psLSHADE with the baseline LSHADE method and the
MadDE algorithm. The results indicate that with restricted optimization budgets
psLSHADE visibly outperforms both competitive algorithms. In addition, the use
of the pre-screening mechanism results in faster population convergence of
psLSHADE compared to LSHADE.


title:
Collective excitation and decay of waveguide-coupled atoms: from timed
  Dicke states to inverted ensembles

abstract:
  The collective absorption and emission of light by an ensemble of atoms is at
the heart of many fundamental quantum optical effects and the basis for
numerous applications. However, beyond weak excitation, both experiment and
theory become increasingly challenging. Here, we explore the regimes from weak
excitation to inversion with ensembles of up to one thousand atoms that are
trapped and optically interfaced using the evanescent field surrounding an
optical nanofiber. We realize strong inversion, with about 80% of the atoms
being excited, and study their subsequent radiative decay into the guided
modes. The data is very well described by a simple model that assumes a
cascaded interaction of the guided light with the atoms. Our results contribute
to the fundamental understanding of the collective interaction of light and
matter and are relevant for applications ranging from quantum memories to
sources of nonclassical light to optical frequency standards.


title: Linear perturbations of Einstein-Gauss-Bonnet black holes

abstract:
  We study linear perturbations about non rotating black hole solutions in
scalar-tensor theories, more specifically Horndeski theories. We consider two
particular theories that admit known hairy black hole solutions. The first one,
Einstein-scalar-Gauss-Bonnet theory, contains a Gauss-Bonnet term coupled to a
scalar field, and its black hole solution is given as a perturbative expansion
in a small parameter that measures the deviation from general relativity. The
second one, known as 4-dimensional-Einstein-Gauss-Bonnet theory, can be seen as
a compactification of higher-dimensional Lovelock theories and admits an exact
black hole solution. We study both axial and polar perturbations about these
solutions and write their equations of motion as a first-order (radial) system
of differential equations, which enables us to study the asymptotic behaviours
of the perturbations at infinity and at the horizon following an algorithm we
developed recently. For the axial perturbations, we also obtain effective
Schr\"odinger-like equations with explicit expressions for the potentials and
the propagation speeds. We see that while the Einstein-scalar-Gauss-Bonnet
solution has well-behaved perturbations, the solution of the
4-dimensional-Einstein-Gauss-Bonnet theory exhibits unusual asymptotic
behaviour of its perturbations near its horizon and at infinity, which makes
the definition of ingoing and outgoing modes impossible. This indicates that
the dynamics of these perturbations strongly differs from the general
relativity case and seems pathological.


title: Semidefinite programming bounds for complex spherical codes

abstract:
  A complex spherical code is a finite subset on the unit sphere in
$\mathbb{C}^d$. A fundamental problem on complex spherical codes is to find
upper bounds for those with prescribed inner products. In this paper, we
determine the irreducible decomposition under the action of the one-point
stabilizer of the unitary group $U(d)$ on the polynomial ring
$\mathbb{C}[z_1\ldots,z_d,\bar{z}_1,\ldots,\bar{z}_d]$ in order to obtain the
semidefinite programming bounds for complex spherical codes.


title: Fast metric embedding into the Hamming cube

abstract:
  We consider the problem of embedding a subset of $\mathbb{R}^n$ into a
low-dimensional Hamming cube in an almost isometric way. We construct a simple
and computationally efficient map that achieves this task with high
probability: we first apply a specific structured random matrix, which we call
the double circulant matrix; using that matrix requires little storage and
matrix-vector multiplication can be performed in near-linear time. We then
binarize each vector by comparing each of its entries to a random threshold,
selected uniformly at random from a well-chosen interval.
  We estimate the number of bits required for this encoding scheme in terms of
two natural geometric complexity parameters of the set -- its Euclidean
covering numbers and its localized Gaussian complexity. The estimate we derive
turns out to be the best that one can hope for -- up to logarithmic terms.
  The key to the proof is a phenomenon of independent interest: we show that
the double circulant matrix mimics the behavior of a Gaussian matrix in two
important ways. First, it yields an almost isometric embedding of any subset of
$\ell_2^n$ into $\ell_1^m$ and, second, it maps an arbitrary set in
$\mathbb{R}^n$ into a set of well-spread vectors.


title: Rotating cosmologies: classical and quantum

abstract:
  We revisit spatially flat, anisotropic cosmologies within the framework of
mini-superspace. Putting special emphasis on the symmetries of the
mini-superspace action and on the associated conservation laws, we unveil a new
class of rotating cosmologies driven by solid matter. Their rotating is
physical, in that it is characterized in an invariant way in terms of a
conserved angular momentum. Along the way, we confirm the results of Bartolo et
al. regarding the slow decay of anisotropies for solid inflation. We then use
our minisuperspace approach as a laboratory to address certain puzzles of
quantum cosmology-among these, how to characterize the spacetime symmetries of
a quantum state at the level of the wavefunction of the universe. For the case
of a solid driven cosmology, this question seems better defined than in more
standard cases. Other questions remain unanswered, though; in particular, the
general question of how to operate a minisuperspace-like truncation of degrees
of freedom that is consistent at the quantum level.


title: Pseudo-Anosov subgroups of general fibered 3-manifold groups

abstract:
  We show that finitely generated and purely pseudo-Anosov subgroups of fibered
3-manifolds with reducible monodromy are convex cocompact as subgroups of the
mapping class group via the Birman exact sequence. Combined with results of
Dowdall--Kent--Leininger and Kent--Leininger--Schleimer, this establishes the
result for the image of all such fibered 3-manifold groups in the mapping class
group.


title: Automatic Census of Mussel Platforms Using Sentinel 2 Images

abstract:
  Mussel platforms are big floating structures made of wood (size is normally
about 20x20 meters or even a bit larger) that are used for aquaculture, id EST:
growing mussels in appropriate marine waters. These structures are very typical
in Galician estuaries. Being interesting to produce a periodic census of these
structures that would allow knowing their number and positions, as well as
changes on those parameters; Satellites that obtain periodic images for earth
observation are a natural election for this issue. This paper describes a
preliminary application able to construct automatically such a census using
Sentinel 2 images (Copernicus Project). Copernicus satellites are run by
European Space Agency (ESA) and the produced images are freely distributed on
the internet. Sentinel 2 images have thirteen frequency bands and are updated
each five days. In our application, we use remote sensing normalized
(differential) indexes and artificial Neural Networks applied to multiband
data. Different methods are described and tested. Finally, results are
presented.


title:
Exponential convergence of hp-FEM for the integral fractional Laplacian
  in 1D

abstract:
  We prove weighted analytic regularity for the solution of the integral
fractional Poisson problem on bounded intervals with analytic right-hand side.
Based on this regularity result, we prove exponential convergence of the hp-FEM
on geometric boundary-refined meshes.


title: Detonation modeling with the Particles on Demand method

abstract:
  A kinetic model based on the Particles on Demand method is introduced for gas
phase detonation hydrodynamics in conjunction with the Lee--Tarver reaction
model. The proposed model is realized on two- and three-dimensional lattices
and is validated with a set of benchmarks. Quantitative validation is performed
with the Chapman--Jouguet theory up to a detonation wave speed of Mach 20 in
one dimension. Two-dimensional outward expanding circular detonation is tested
for isotropy of the model as well as for the asymptotic detonation wave speed.
Mach reflection angles are verified in setups consisting of interacting strong
bow shocks emanating from detonation. Spherical detonation is computed to show
viability of the proposed model for three dimensional simulations.


title:
The MeerTime Pulsar Timing Array -- A Census of Emission Properties and
  Timing Potential

abstract:
  MeerTime is a five-year Large Survey Project to time pulsars with MeerKAT,
the 64-dish South African precursor to the Square Kilometre Array. The science
goals for the programme include timing millisecond pulsars (MSPs) to high
precision (< 1 $\mu$s) to study the Galactic MSP population and to contribute
to global efforts to detect nanohertz gravitational waves with the
International Pulsar Timing Array (IPTA). In order to plan for the remainder of
the programme and to use the allocated time most efficiently, we have conducted
an initial census with the MeerKAT "L-band" receiver of 189 MSPs visible to
MeerKAT and here present their dispersion measures, polarization profiles,
polarization fractions, rotation measures, flux density measurements, spectral
indices, and timing potential. As all of these observations are taken with the
same instrument (which uses coherent dedispersion, interferometric polarization
calibration techniques, and a uniform flux scale), they present an excellent
resource for population studies. We used wideband pulse portraits as timing
standards for each MSP and demonstrated that the MeerTime Pulsar Timing Array
(MPTA) can already contribute significantly to the IPTA as it currently
achieves better than 1 $\mu$s timing accuracy on 89 MSPs (observed with
fortnightly cadence). By the conclusion of the initial five-year MeerTime
programme in July 2024, the MPTA will be extremely significant in global
efforts to detect the gravitational wave background with a contribution to the
detection statistic comparable to other long-standing timing programmes.


title: A potential smoking gun for new physics in charm sector

abstract:
  We point out the decay mode $\Lambda_c^+\to \Delta^+K(t)(\to \pi^+\pi^-)$ is
a potential smoking gun for new physics in charm sector. The Standard Model
accommodated $CP$ asymmetry in this mode can be determined by branching
fractions of the $\Lambda^+_c\to \Delta^{++} K^-$, $\Lambda^+_c\to \Delta^{+}
K^0_{S,L}$ and $\Lambda^+_c\to \Delta^{0} K^+$ modes without \textit{ad hoc}
assumptions. The only theoretical uncertainty is tiny isospin breaking. The
ambiguities from the loop-induced quantities and $SU(3)_F$ breaking hadronic
effects are avoided. Once the $CP$ asymmetry in the $\Lambda_c^+$ decays into
$\Delta^+$ and neutral kaons is confirmed by experiments, we can check if it is
beyond the Standard Model or not. Besides, it helps to verify the
$CP$-violating effect resulted from interference between the Cabibbo-favored
and doubly Cabibbo-suppressed amplitudes with the neutral kaon mixing. Future
branching fractions measurements play a key role to reduce the theoretical
uncertainties.


title:
Finding shortest and nearly shortest path nodes in large substantially
  incomplete networks

abstract:
  Dynamic processes on networks, be it information transfer in the Internet,
contagious spreading in a social network, or neural signaling, take place along
shortest or nearly shortest paths. Unfortunately, our maps of most large
networks are substantially incomplete due to either the highly dynamic nature
of networks, or high cost of network measurements, or both, rendering
traditional path finding methods inefficient. We find that shortest paths in
large real networks, such as the network of protein-protein interactions (PPI)
and the Internet at the autonomous system (AS) level, are not random but are
organized according to latent-geometric rules. If nodes of these networks are
mapped to points in latent hyperbolic spaces, shortest paths in them align
along geodesic curves connecting endpoint nodes. We find that this alignment is
sufficiently strong to allow for the identification of shortest path nodes even
in the case of substantially incomplete networks. We demonstrate the utility of
latent-geometric path-finding in problems of cellular pathway reconstruction
and communication security.


title:
Prescribed-Time Seeking of a Repulsive Source by Unicycle Angular
  Velocity Tuning

abstract:
  All the existing source seeking algorithms for unicycle models in GPS-denied
settings guarantee at best an exponential rate of convergence over an infinite
interval. Using the recently introduced time-varying feedback tools for
prescribed-time stabilization, we achieve source seeking in prescribed time,
i.e., the convergence to the source, without the measurements of the position
and velocity of the unicycle, in as short a time as the user desires, starting
from an arbitrary distance from the source. The convergence is established
using a singularly perturbed version of the Lie bracket averaging method,
combined with time dilation and time contraction operations. The algorithm is
robust, provably, even to an arbitrarily strong gradient-dependent repulsive
velocity drift emanating from the source.


title: Bespoke Instrumental Variables for Causal Inference

abstract:
  Many proposals for the identification of causal effects in the presence of
unmeasured confounding require an instrumental variable or negative control
that satisfies strong, untestable assumptions. In this paper, we will instead
show how one can identify causal effects for a point exposure by using a
measured confounder as a 'bespoke instrumental variable'. This strategy
requires an external reference population that does not have access to the
exposure, and a stability condition on the confounder outcome association
between reference and target populations. Building on recent identification
results of Richardson and Tchetgen Tchetgen (2021), we develop the
semiparametric efficiency theory for a general bespoke instrumental variable
model, and obtain a multiply robust locally efficient estimator of the average
treatment effect in the treated.


title: Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline

abstract:
  With the popularity of multi-modal sensors, visible-thermal (RGB-T) object
tracking is to achieve robust performance and wider application scenarios with
the guidance of objects' temperature information. However, the lack of paired
training samples is the main bottleneck for unlocking the power of RGB-T
tracking. Since it is laborious to collect high-quality RGB-T sequences, recent
benchmarks only provide test sequences. In this paper, we construct a
large-scale benchmark with high diversity for visible-thermal UAV tracking
(VTUAV), including 500 sequences with 1.7 million high-resolution (1920
$\times$ 1080 pixels) frame pairs. In addition, comprehensive applications
(short-term tracking, long-term tracking and segmentation mask prediction) with
diverse categories and scenes are considered for exhaustive evaluation.
Moreover, we provide a coarse-to-fine attribute annotation, where frame-level
attributes are provided to exploit the potential of challenge-specific
trackers. In addition, we design a new RGB-T baseline, named Hierarchical
Multi-modal Fusion Tracker (HMFT), which fuses RGB-T data in various levels.
Numerous experiments on several datasets are conducted to reveal the
effectiveness of HMFT and the complement of different fusion types. The project
is available at here.


title: Pluto near the edge of chaos

abstract:
  Many of the unusual properties of Pluto's orbit are widely accepted as
evidence for the orbital migration of the giant planets in early solar system
history. However, some properties remain an enigma. Pluto's long term orbital
stability is supported by two special properties of its orbit that limit the
location of its perihelion in azimuth and in latitude. We revisit Pluto's
orbital dynamics with a view to elucidating the individual and collective
gravitational effects of the giant planets on constraining its perihelion
location. While the resonant perturbations from Neptune account for the
azimuthal constraint on Pluto's perihelion location, we demonstrate that the
long term and steady persistence of the latitudinal constraint is possible only
in a narrow range of additional secular forcing which arises fortuitously from
the particular orbital architecture of the other giant planets. Our
investigations also find that Jupiter has a largely stabilizing influence
whereas Uranus has a largely destabilizing influence on Pluto's orbit. Overall,
Pluto's orbit is rather surprisingly close to a zone of strong chaos.


title:
Calculation of tunneling current across Trapezoidal potential barrier in
  a Scanning Tunneling Microscope

abstract:
  The Planar Model of the Electrode-Vacuum-Electrode configuration for STM in
which electrode surfaces are assumed to be infinite parallel planes, with
atomic size separation and vacuum between them, is used to calculate tunneling
current densities for both low and high bias voltages. Non WKB, Airy function
solutions for the Schr\"odinger Equation for the trapezoidal barrier in the
tunneling region are used to calculate the tunneling probability. Pauli
blocking effects are found to cancel in the calculation of the net
(Forward-Reverse) current density. Temperature dependent Fermi Factors for each
electrode are introduced and the calculation involves integration over the
electron energies. Thus the energy of the tunneling electrons is not limited to
the Fermi energy in this calculation. In order to convert the current densities
obtained in the planar model to tunneling currents the tip and sample surfaces
cannot be treated as infinite plane surfaces. Instead the tip and the sample
surfaces are modelled as confocal hyperboloids, and the tip sample distance is
replaced by the length of the line of force (field line). The current is found
by integrating the current density over a finite area of the tip. The
calculated tunnel currents for a few electrode pairs at room temperature are
plotted for several values of bias voltage and tip sample distances. The effect
of the curvature of the tip is also studied by repeating the calculations for
various tip radii. The dependence of tunneling current on electrode
temperatures is also studied. Some estimate of lateral resolution and its
dependence on bias voltage and tip radius is also presented.


title:
Generalized Schur-Weyl dualities for quantum affine symmetric pairs and
  orientifold KLR algebras

abstract:
  We define a boundary analogue of the Kang-Kashiwara-Kim-Oh generalized
Schur-Weyl dualities between quantum affine algebras and
Khovanov-Lauda-Rouquier (KLR) algebras. Let $\mathfrak{g}$ be a complex simple
Lie algebra and $U_qL\mathfrak{g}$ the corresponding quantum affine algebra. We
construct a functor ${}^{\theta}{\sf F}$ between finite-dimensional modules
over a quantum symmetric pair of affine type $U_q\mathfrak{k}\subset
U_qL{\mathfrak{g}}$ and an orientifold KLR ($o$KLR) algebra arising from a
framed quiver with a contravariant involution, whose nodes are indexed by
finite-dimensional $U_qL{\mathfrak{g}}$-modules. With respect to the
Kang-Kashiwara-Kim-Oh construction, our combinatorial model is further enriched
with the poles of the trigonometric K-matrices (that is trigonometric solutions
of a generalized reflection equation) intertwining the action of
$U_q\mathfrak{k}$ on finite-dimensional $U_qL{\mathfrak{g}}$-modules. By
construction, ${}^{\theta}{\sf F}$ is naturally compatible with the
Kang-Kashiwara-Kim-Oh functor in that, while the latter is a functor of
monoidal categories, ${}^{\theta}{\sf F}$ is a functor of module categories.
Relying on an isomorphism between suitable completions of $o$KLR algebras and
affine Hecke algebras of type $\sf C$, we prove that ${}^{\theta}{\sf F}$
recovers the Schur-Weyl dualities due to Fan-Lai-Li-Luo-Wang-Watanabe in
quasi-split type $\sf AIII$. Finally, we construct spectral K-matrices for
orientifold KLR algebras, yielding a meromorphic braiding on its category of
finite-dimensional representations. We prove that, in the case of the ${\sf
A}_{\infty}$ quiver with no fixed points and no framing, the functor
${}^{\theta}{\sf F}$ is exact, factors through a suitable localization, and
takes values in a boundary analogue of the Hernandez-Leclerc category.


title: On the random G equation with nonzero divergence

abstract:
  We prove a quantitative rate of homogenization for the G equation in a random
setting with finite range of dependence and nonzero divergence, with explicit
dependence of the constants on the Lipschitz norm of the environment. Inspired
by work of Burago, Ivanov, and Novikov, the proof uses explicit bounds on the
waiting time for the associated metric problem.


title: The winner takes it all but one

abstract:
  We study competing first passage percolation on graphs generated by the
configuration model with infinite-mean degrees. Initially, two uniformly chosen
vertices are infected with type 1 and type 2 infection, respectively, and the
infection then spreads via nearest neighbors in the graph. The time it takes
for the type 1 (resp. 2) infection to traverse an edge $e$ is given by a random
variable $X_1(e)$ (resp. $X_2(e)$) and, if the vertex at the other end of the
edge is still uninfected, it then becomes type 1 (resp. 2) infected and immune
to the other type. Assuming that the degrees follow a power-law distribution
with exponent $\tau \in (1,2)$, we show that, with high probability as the
number of vertices tends to infinity, one of the infection types occupies all
vertices except for the starting point of the other type. Moreover, both
infections have a positive probability of winning regardless of the passage
times distribution. The result is also shown to hold for the erased
configuration model, where self-loops are erased and multiple edges are merged,
and when the degrees are conditioned to be smaller than $n^\alpha$ for some
$\alpha > 0$.


title:
Sputter-deposited, amorphous alloy films of ruthenium and molybdenum
  with extremely smooth surfaces

abstract:
  Thin metal films find broad application in advanced as well as every-day
technology as a relatively cost-effective way to customize and improve
materials properties, ranging from the reflectivity and transmission of optical
coatings all the way to nanolayers of improved conductivity, wear resistance or
chemical stability. On a microscopic scale, such films are usually
polycrystalline, i.e. they consist of ordered, crystalline grains of different
orientations. The macroscopic film properties, are often dominated by the
boundaries between these grains that affect the chemical and mechanical
stability of these layers. A simple but effective alternative, a metallic
glass, avoids the detrimental effects of grain boundaries by avoiding
crystallinity altogether. Metallic glasses, however, represent a
non-equilibrium state of matter that is practically impossible to achieve and
retain for pure, elemental metals, while for simple alloys, extremely high
cooling rates are required to reach amorphicity, as is possible via thin-film
deposition methods. In this article, we start by showing that such an amorphous
arrangement can be achieved readily for a binary metal alloy of ruthenium and
molybdenum, by employing the straightforward, industrial technique of sputter
deposition. We find that the amorphous films display superior smoothness with
respect to traditional, polycrystalline films of the same materials, and
speculate that also other, e.g. mechanical, electrical and chemical, properties
should benefit.


title:
Karaoker: Alignment-free singing voice synthesis with speech training
  data

abstract:
  Existing singing voice synthesis models (SVS) are usually trained on singing
data and depend on either error-prone time-alignment and duration features or
explicit music score information. In this paper, we propose Karaoker, a
multispeaker Tacotron-based model conditioned on voice characteristic features
that is trained exclusively on spoken data without requiring time-alignments.
Karaoker synthesizes singing voice following a multi-dimensional template
extracted from a source waveform of an unseen speaker/singer. The model is
jointly conditioned with a single deep convolutional encoder on continuous data
including pitch, intensity, harmonicity, formants, cepstral peak prominence and
octaves. We extend the text-to-speech training objective with feature
reconstruction, classification and speaker identification tasks that guide the
model to an accurate result. Except for multi-tasking, we also employ a
Wasserstein GAN training scheme as well as new losses on the acoustic model's
output to further refine the quality of the model.


title: Critical Tests of Leading Gamma Ray Burst Theories II

abstract:
  It has been observationally established that supernovae (SNe) of Type Ic
produce long duration gamma ray bursts (GRBs) and that neutron star mergers
generate short hard GRBs. SN-Less GRBs presumably originate in a phase
transition of a neutron star in a high mass X-ray binary. How these phenomena
actually generate GRBs is debated. The fireball and cannonball models of GRBs
and their afterglows have been widely confronted with the huge observational
data, with their defenders claiming success. The claims, however, may reflect
multiple choices and the use of many adjustable parameters, rather than the
validity of the models. Only a confrontation of key falsifiable predictions of
the models with solid observational data can test their validity. Such critical
tests are reviewed in this report.


title: The Lyapunov spectrum for conditioned random dynamical systems

abstract:
  We establish the existence of a full spectrum of Lyapunov exponents for
memoryless random dynamical systems with absorption. To this end, we crucially
embed the process conditioned to never being absorbed, the Q-process, into the
framework of random dynamical systems, allowing us to study multiplicative
ergodic properties. We show that the finite-time Lyapunov exponents converge in
conditioned probability and apply our results to iterated function systems and
stochastic differential equations.


title: Floquet theory of spin dynamics under circularly polarized light pulses

abstract:
  Within the Floquet theory of periodically driven quantum systems, the
nonlinear single-spin dynamics under pulse of a circularly polarized
electromagnetic field is analyzed. It is demonstrated that the field, first,
lifts the spin degeneracy and, second, the field-induced spin splitting is
accompanied by the photon emission at the spitting frequency. This two-stage
process leads, particularly, to the polarization of spins along angular
momentum of the circularly polarized field. As a result, the pulse-induced
magnetization appears, what can be observed in state-of the-art measurements.


title:
Tunnel configurations and seismic isolation optimization in underground
  gravitational wave detectors

abstract:
  Gravitational wave detectors like the Einstein Telescope will be built a few
hundred meters under Earth's surface to reduce both direct seismic and
Newtonian noise. Underground facilities must be designed to take full advantage
of the shielding properties of the rock mass to maximize the detector's
performance. A major issue with the Einstein Telescope design are the corner
points, where caverns need to be excavated in stable, low permeability rock to
host the sensitive measurement infrastructure. This paper proposes a new
topology that moves the top stages of the seismic attenuation chains and
Michelson beam re-combination in separate excavations far from the beam-line
and equipment induced noise while the test mass mirrors remain in the main
tunnels. Distributing the seismic attenuation chain components over multiple
tunnel levels allows the use of arbitrarily long seismic attenuation chains
that relegate the seismic noise at frequencies completely outside the
low-frequency noise budget, thus keeping the door open for future Newtonian
noise suppression methods. Separating the input-output and recombination optics
of different detectors into separate caverns drastically improves the
observatory detection efficiency and allows staged commissioning. The proposed
scheme eliminates structural and instrumentation crowding while the reduced
sizes of excavations require fewer support measures.


title:
Local Ward identities for collective excitations in fermionic systems
  with spontaneously broken symmetries

abstract:
  We derive Ward identities for fermionic systems exhibiting a gauge symmetry
that gets globally broken. In particular, we focus on the relation that
connects the gauge field response functions to the transverse susceptibilities
of the order parameter. We find that the long-wavelength and zero energy limit
of the former are related to the coefficients of a low-energy expansion of the
latter. We examine three physical cases: the superconductor, the N\'eel
antiferromagnet and the spiral magnet. In the case of a metallic spiral magnet
that completely breaks the SU(2) spin symmetry we explicitly show that the Ward
identities are fulfilled within the random phase approximation. We subsequently
derive microscopic expressions for the spin stiffnesses and spin wave
velocities, which can be plugged into low energy models to study the effect of
long-wavelength bosonic fluctuations on top of mean-field solutions.


title:
On the supports in the Humili\`ere completion and $\gamma$-coisotropic
  sets (with an Appendix joint with Vincent Humili\`ere)

abstract:
  The symplectic spectral metric on the set of Lagrangian submanifolds or
Hamiltonian maps can be used to define a completion of these spaces. For an
element of such a completion, we define its $\gamma$-support. We also define
the notion of $\gamma$-coisotropic set, and prove that a $\gamma$-support must
be $\gamma$-coisotropic toghether with many properties of the $\gamma$-support
and $\gamma$-coisotropic sets. We give examples of Lagrangians in the
completion having large $\gamma$-support and we study those (called "regular
Lagrangians") having small $\gamma$-support. We compare the notion of
$\gamma$-coisotropy with other notions of isotropy. In a joint Appendix with V.
Humili\`ere, we connect the $\gamma$-support with an extension of the notion of
the Birkhoff attractor of a dissipative map to higher dimension.


title:
Possible Implications of Relatively High Levels of Initial $^{60}$Fe in
  Iron Meteorites for the Non-Carbonaceous -- Carbonaceous Meteorite Dichotomy
  and Solar Nebula Formation

abstract:
  Cook et al. (2021) found that iron meteorites have an initial abundance ratio
of the short-lived isotope $^{60}$Fe to the stable isotope $^{56}$Fe of
$^{60}$Fe/$^{56}$Fe $\sim$ $(6.4 \pm 2.0) \times 10^{-7}$. This appears to
require the injection of live $^{60}$Fe from a Type II supernova (SN II) into
the presolar molecular cloud core, as the observed ratio is over a factor of
ten times higher than would be expected to be found in the ambient interstellar
medium (ISM) as a result of galactic chemical evolution. The supernova
triggering and injection scenario offers a ready explanation for an elevated
initial $^{60}$Fe level, and in addition provides a physical mechanism for
explaining the non-carbonaceous -- carbonaceous (NC-CC) dichotomy of
meteorites. The NC-CC scenario hypothesizes the solar nebula first accreted
material that was enriched in supernova-derived nuclides, and then later
accreted material depleted in supernova-derived nuclides. While the NC-CC
dichotomy refers to stable nuclides, not short-lived isotopes like $^{60}$Fe,
the SN II triggering hypothesis provides an explanation for the otherwise
unexplained change in nuclides being accreted by the solar nebula. Three
dimensional hydrodynamical models of SN II shock-triggered collapse show that
after triggering collapse of the presolar cloud core, the shock front sweeps
away the local ISM while accelerating the resulting protostar/disk to a speed
of several km/s, sufficient for the protostar/disk system to encounter within
$\sim$ 1 Myr the more distant regions of a giant molecular cloud complex that
might be expected to have a depleted inventory of supernova-derived nuclides.


title: An XES Extension for Uncertain Event Data

abstract:
  Event data, often stored in the form of event logs, serve as the starting
point for process mining and other evidence-based process improvements.
However, event data in logs are often tainted by noise, errors, and missing
data. Recently, a novel body of research has emerged, with the aim to address
and analyze a class of anomalies known as uncertainty-imprecisions quantified
with meta-information in the event log. This paper illustrates an extension of
the XES data standard capable of representing uncertain event data. Such an
extension enables input, output, and manipulation of uncertain data, as well as
analysis through the process discovery and conformance checking approaches
available in literature.


title: Individually-Fair Auctions for Multi-Slot Sponsored Search

abstract:
  We design fair sponsored search auctions that achieve a near-optimal tradeoff
between fairness and quality. Our work builds upon the model and auction design
of Chawla and Jagadeesan \cite{CJ22}, who considered the special case of a
single slot. We consider sponsored search settings with multiple slots and the
standard model of click through rates that are multiplicatively separable into
an advertiser-specific component and a slot-specific component. When similar
users have similar advertiser-specific click through rates, our auctions
achieve the same near-optimal tradeoff between fairness and quality as in
\cite{CJ22}. When similar users can have different advertiser-specific
preferences, we show that a preference-based fairness guarantee holds. Finally,
we provide a computationally efficient algorithm for computing payments for our
auctions as well as those in previous work, resolving another open direction
from \cite{CJ22}.


title: Shaping contactless forces through anomalous acoustic scattering

abstract:
  Waves impart momentum and exert force on obstacles in their path. The
transfer of wave momentum is a fundamental mechanism for contactless
manipulation, yet the rules of conventional scattering intrinsically limit the
radiation force based on the shape and the size of the manipulated object.
Here, we show that this intrinsic limit can be overcome for acoustic waves with
subwavelength-structured metasurfaces, where the force becomes controllable by
the arrangement of surface features, independent of the object's overall shape
and size. Harnessing such anomalous metasurface scattering, we demonstrate
complex actuation phenomena: self-guidance, where a metasurface object is
autonomously guided by an acoustic wave, and contactless pulling, where a
metasurface object is pulled by the wave. Our results show that bringing
metasurface physics, and its full arsenal of tools, to the domain of mechanical
manipulation opens the door to diverse actuation mechanisms that are beyond the
limits of traditional wave-matter interactions.


title:
EfficientFi: Towards Large-Scale Lightweight WiFi Sensing via CSI
  Compression

abstract:
  WiFi technology has been applied to various places due to the increasing
requirement of high-speed Internet access. Recently, besides network services,
WiFi sensing is appealing in smart homes since it is device-free,
cost-effective and privacy-preserving. Though numerous WiFi sensing methods
have been developed, most of them only consider single smart home scenario.
Without the connection of powerful cloud server and massive users, large-scale
WiFi sensing is still difficult. In this paper, we firstly analyze and
summarize these obstacles, and propose an efficient large-scale WiFi sensing
framework, namely EfficientFi. The EfficientFi works with edge computing at
WiFi APs and cloud computing at center servers. It consists of a novel deep
neural network that can compress fine-grained WiFi Channel State Information
(CSI) at edge, restore CSI at cloud, and perform sensing tasks simultaneously.
A quantized auto-encoder and a joint classifier are designed to achieve these
goals in an end-to-end fashion. To the best of our knowledge, the EfficientFi
is the first IoT-cloud-enabled WiFi sensing framework that significantly
reduces communication overhead while realizing sensing tasks accurately. We
utilized human activity recognition and identification via WiFi sensing as two
case studies, and conduct extensive experiments to evaluate the EfficientFi.
The results show that it compresses CSI data from 1.368Mb/s to 0.768Kb/s with
extremely low error of data reconstruction and achieves over 98% accuracy for
human activity recognition.


title:
Sat2lod2: A Software For Automated Lod-2 Modeling From Satellite-Derived
  Orthophoto And Digital Surface Model

abstract:
  Deriving LoD2 models from orthophoto and digital surface models (DSM)
reconstructed from satellite images is a challenging task. Existing solutions
are mostly system approaches that require complicated step-wise processes,
including not only heuristic geometric operations, but also high-level steps
such as machine learning-based semantic segmentation and building detection.
Here in this paper, we describe an open-source tool, called SAT2LOD2, built
based on a minorly modified version of our recently published work. SAT2LoD2 is
a fully open-source and GUI (Graphics User Interface) based software, coded in
Python, which takes an orthophoto and DSM as inputs, and outputs individual
building models, and it can additionally take road network shapefiles, and
customized classification maps to further improve the reconstruction results.
We further improve the robustness of the method by 1) intergrading building
segmentation based on HRNetV2 into our software; and 2) having implemented a
decision strategy to identify complex buildings and directly generate mesh to
avoid erroneous LoD2 reconstruction from a system point of view. The software
can process a moderate level of data (around 5000*5000 size of orthophoto and
DSM) using a PC with a graphics card supporting CUDA. Furthermore, the GUI is
self-contained and stores the intermediate processing results facilitating
researchers to learn the process easily and reuse intermediate files as needed.
The updated codes and software are available under this GitHub page:
https://github.com/GDAOSU/LOD2BuildingModel.


title:
Reproducibility and Baseline Reporting for Dynamic Multi-objective
  Benchmark Problems

abstract:
  Dynamic multi-objective optimization problems (DMOPs) are widely accepted to
be more challenging than stationary problems due to the time-dependent nature
of the objective functions and/or constraints. Evaluation of purpose-built
algorithms for DMOPs is often performed on narrow selections of dynamic
instances with differing change magnitude and frequency or a limited selection
of problems. In this paper, we focus on the reproducibility of simulation
experiments for parameters of DMOPs. Our framework is based on an extension of
PlatEMO, allowing for the reproduction of results and performance measurements
across a range of dynamic settings and problems. A baseline schema for dynamic
algorithm evaluation is introduced, which provides a mechanism to interrogate
performance and optimization behaviours of well-known evolutionary algorithms
that were not designed specifically for DMOPs. Importantly, by determining the
maximum capability of non-dynamic multi-objective evolutionary algorithms, we
can establish the minimum capability required of purpose-built dynamic
algorithms to be useful. The simplest modifications to manage dynamic changes
introduce diversity. Allowing non-dynamic algorithms to incorporate
mutated/random solutions after change events determines the improvement
possible with minor algorithm modifications. Future expansion to include
current dynamic algorithms will enable reproduction of their results and
verification of their abilities and performance across DMOP benchmark space.


title:
Investigating Spherical Epipolar Rectification for Multi-View Stereo 3D
  Reconstruction

abstract:
  Multi-view stereo (MVS) reconstruction is essential for creating 3D models.
The approach involves applying epipolar rectification followed by dense
matching for disparity estimation. However, existing approaches face challenges
in applying dense matching for images with different viewpoints primarily due
to large differences in object scale. In this paper, we propose a spherical
model for epipolar rectification to minimize distortions caused by differences
in principal rays. We evaluate the proposed approach using two aerial-based
datasets consisting of multi-camera head systems. We show through qualitative
and quantitative evaluation that the proposed approach performs better than
frame-based epipolar correction by enhancing the completeness of point clouds
by up to 4.05% while improving the accuracy by up to 10.23% using LiDAR data as
ground truth.


title:
A Novel Intrinsic Image Decomposition Method to Recover Albedo for
  Aerial Images in Photogrammetry Processing

abstract:
  Recovering surface albedos from photogrammetric images for realistic
rendering and synthetic environments can greatly facilitate its downstream
applications in VR/AR/MR and digital twins. The textured 3D models from
standard photogrammetric pipelines are suboptimal to these applications because
these textures are directly derived from images, which intrinsically embedded
the spatially and temporally variant environmental lighting information, such
as the sun illumination, direction, causing different looks of the surface,
making such models less realistic when used in 3D rendering under synthetic
lightings. On the other hand, since albedo images are less variable by
environmental lighting, it can, in turn, benefit basic photogrammetric
processing. In this paper, we attack the problem of albedo recovery for aerial
images for the photogrammetric process and demonstrate the benefit of albedo
recovery for photogrammetry data processing through enhanced feature matching
and dense matching. To this end, we proposed an image formation model with
respect to outdoor aerial imagery under natural illumination conditions; we
then, derived the inverse model to estimate the albedo by utilizing the typical
photogrammetric products as an initial approximation of the geometry. The
estimated albedo images are tested in intrinsic image decomposition,
relighting, feature matching, and dense matching/point cloud generation
results. Both synthetic and real-world experiments have demonstrated that our
method outperforms existing methods and can enhance photogrammetric processing.