title: Text to artistic image generation

abstract:
  Painting is one of the ways for people to express their ideas, but what if
people with disabilities in hands want to paint? To tackle this challenge, we
create an end-to-end solution that can generate artistic images from text
descriptions. However, due to the lack of datasets with paired text description
and artistic images, it is hard to directly train an algorithm which can create
art based on text input. To address this issue, we split our task into three
steps: (1) Generate a realistic image from a text description by using Dynamic
Memory Generative Adversarial Network (arXiv:1904.01310), (2) Classify the
image as a genre that exists in the WikiArt dataset using Resnet (arXiv:
1512.03385), (3) Select a style that is compatible with the genre and transfer
it to the generated image by using neural artistic stylization network
(arXiv:1705.06830).


title: Family of CV states of definite parity and their metrological power

abstract:
  We introduce a family of new continuous variable states of definite parity
originating from even single mode squeezed vacuum state by subtracting an
arbitrary number of photons from it.


title:
DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in
  Time Series Data

abstract:
  Accurate forecasting of extreme values in time series is critical due to the
significant impact of extreme events on human and natural systems. This paper
presents DeepExtrema, a novel framework that combines a deep neural network
(DNN) with generalized extreme value (GEV) distribution to forecast the block
maximum value of a time series. Implementing such a network is a challenge as
the framework must preserve the inter-dependent constraints among the GEV model
parameters even when the DNN is initialized. We describe our approach to
address this challenge and present an architecture that enables both
conditional mean and quantile prediction of the block maxima. The extensive
experiments performed on both real-world and synthetic data demonstrated the
superiority of DeepExtrema compared to other baseline methods.


title:
Interior Structure and Complexity Growth Rate of Holographic
  Superconductor from M-Theory

abstract:
  We study the interior dynamics of a top-down holographic superconductor from
M-theory. The condense of the charged scalar hair necessarily removes the inner
Cauchy horizon and the spacetime ends at a spacelike singularity. Although
there is a smooth superconducting phase transition at the critical temperature,
the onset of superconductivity is accompanied by intricate interior dynamics,
including the collapse of the Einstein-Rosen bridge, the Josephson oscillations
of the condensate, and the final Kasner singularity. We obtain analytically the
transformation rule for the alternation of different Kasner epochs. Thanks to
the nonlinear couplings of the top-down theory, there is generically a
never-ending chaotic alternation of Kasner epochs towards the singularity. We
compute the holographic complexity using both the complexity-action and the
complexity-volume dualities. In contrast to the latter, the complexity growth
rate from the complexity-action duality has a discontinuity at the critical
temperature, characterizing the sudden change of the internal structure before
and after the superconducting phase transition.


title: Geometrical Modelling and Numerical Analysis of Dislocaion Mechanics

abstract:
  This study undertakes the mathematical modelling and numerical analysis of
dislocations within the framework of differential geometry. The fundamental
configurations, i.e. reference, intermediate and current configurations, are
expressed as the Riemann-Cartan manifold, which equips the Riemannian metric
and Weitzenb\"ock connection. The torsion 2-form on the intermediate
configuration is obtained through the Hodge duality of the dislocation density
and the corresponding bundle isomorphism is subjected to the Helmholtz
decomposition. This analysis introduces the boundary condition for plastic
deformation. Cartan first structure equation and stress equilibrium equation
are solved numerically using weak form variational expressions and isogeometric
analysis. The numerical analysis carried out for this study reveals the
distribution of plastic deformation fields around screw and edge dislocations
for the first time. It also demonstrates stress fields around dislocations of
which the distant fields show full agreement with the classical Volterra
theory, while at the same time eliminating the singularity otherwise introduced
at the dislocation by classical methods. The stress fields include several
characteristic features due to the geometrical nonlinearity included therein.
We also demonstrate that free surfaces affect both plastic and elastic
deformation, but in different ways. The mathematical framework of this study is
applicable to an arbitrary configuration of dislocations.

categories: math.NA cond-mat.mtrl-sci cs.NA math-ph math.MP


title: Cross-modal Contrastive Learning for Speech Translation

abstract:
  How can we learn unified representations for spoken utterances and their
written text? Learning similar representations for semantically similar speech
and text is important for speech translation. To this end, we propose ConST, a
cross-modal contrastive learning method for end-to-end speech-to-text
translation. We evaluate ConST and a variety of previous baselines on a popular
benchmark MuST-C. Experiments show that the proposed ConST consistently
outperforms the previous methods on, and achieves an average BLEU of 29.4. The
analysis further verifies that ConST indeed closes the representation gap of
different modalities -- its learned representation improves the accuracy of
cross-modal speech-text retrieval from 4% to 88%. Code and models are available
at https://github.com/ReneeYe/ConST.


title: TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network

abstract:
  Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable
interest for its ability in achieving three-dimensional reconstruction along
the elevation direction from multiple observations. In recent years, compressed
sensing (CS) technique has been introduced into TomoSAR considering for its
super-resolution ability with limited samples. Whereas, the CS-based methods
suffer from several drawbacks, including weak noise resistance, high
computational complexity and complex parameter fine-tuning. Among the different
CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a
robust reconstruction approach, however, the parameters in the ISTA algorithm
are manually chosen, which usually requires a time-consuming fine-tuning
process to achieve the best performance. Aiming at efficient TomoSAR imaging, a
novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed
towards the TomoSAR imaging problem in this paper, and the key parameters of
ISTA are learned from training data via deep learning to avoid complex
parameter fine-tuning and significantly relieves the training burden. In
addition, experiments verify that it is feasible to use traditional CS
algorithms as training labels, which provides a tangible supervised training
method to achieve better 3D reconstruction performance even in the absence of
labeled data in real applications.


title:
Multi-Graph based Multi-Scenario Recommendation in Large-scale Online
  Video Services

abstract:
  Recently, industrial recommendation services have been boosted by the
continual upgrade of deep learning methods. However, they still face de-biasing
challenges such as exposure bias and cold-start problem, where circulations of
machine learning training on human interaction history leads algorithms to
repeatedly suggest exposed items while ignoring less-active ones. Additional
problems exist in multi-scenario platforms, e.g. appropriate data fusion from
subsidiary scenarios, which we observe could be alleviated through graph
structured data integration via message passing.
  In this paper, we present a multi-graph structured multi-scenario
recommendation solution, which encapsulates interaction data across scenarios
with multi-graph and obtains representation via graph learning. Extensive
offline and online experiments on real-world datasets are conducted where the
proposed method demonstrates an increase of 0.63% and 0.71% in CTR and Video
Views per capita on new users over deployed set of baselines and outperforms
regular method in increasing the number of outer-scenario videos by 25% and
video watches by 116%, validating its superiority in activating cold videos and
enriching target recommendation.


title: A Deep Learning Approach to Dst Index Prediction

abstract:
  The disturbance storm time (Dst) index is an important and useful measurement
in space weather research. It has been used to characterize the size and
intensity of a geomagnetic storm. A negative Dst value means that the Earth's
magnetic field is weakened, which happens during storms. In this paper, we
present a novel deep learning method, called the Dst Transformer, to perform
short-term, 1-6 hour ahead, forecasting of the Dst index based on the solar
wind parameters provided by the NASA Space Science Data Coordinated Archive.
The Dst Transformer combines a multi-head attention layer with Bayesian
inference, which is capable of quantifying both aleatoric uncertainty and
epistemic uncertainty when making Dst predictions. Experimental results show
that the proposed Dst Transformer outperforms related machine learning methods
in terms of the root mean square error and R-squared. Furthermore, the Dst
Transformer can produce both data and model uncertainty quantification results,
which can not be done by the existing methods. To our knowledge, this is the
first time that Bayesian deep learning has been used for Dst index forecasting.


title:
Incommensurate magnetic order in Sm$_3$BWO$_9$ with the distorted kagome
  lattice

abstract:
  We investigate the magnetic ground state of Sm$_3$BWO$_9$ with the distorted
kagome lattice. A magnetic phase transition is identified at $T_N=0.75$ K from
the temperature dependence of specific heat. From nuclear magnetic resonance
(NMR) measurements, an incommensurate magnetic order is shown by the
double-horn type spectra under a $c$-axis magnetic field. While, absence of
line splitting is observed for field oriented within the $ab$-plane, indicating
the incommensurate modulation of the internal field strictly along $c$-axis.
From the spin dynamics, the critical slowing down behavior is observed at in
the temperature dependence of $1/T_1$ with $\mu_0H\perp c$-axis, which is
completely absent in that with $\mu_0H||c$-axis. This result suggests the
incommensurate order to be of spin density wave (SDW) type instead of the
helical configuration.With the field intensity increasing, the magnetic order
is suppressed gradually. Different from the distorted kagome antiferromagnets
with the anisotropic coupling in isosceles triangles, the incommensurate order
is realized in Sm$_3$BWO$_9$ of regular triangles with two different side
length. The possible physical mechanism for this newly discovered behavior is
further discussed.


title: Depletion of Resources by a Population of Diffusing Species

abstract:
  Depletion of natural and artificial resources is a fundamental problem and a
potential cause of economic crises, ecological catastrophes, and death of
living organisms. Understanding the depletion process is crucial for its
further control and optimized replenishment of resources. In this paper, we
investigate a stock depletion by a population of species that undergo an
ordinary diffusion and consume resources upon each encounter with the stock. We
derive the exact form of the probability density of the random depletion time,
at which the stock is exhausted. The dependence of this distribution on the
number of species, the initial amount of resources, and the geometric setting
is analyzed. Future perspectives and related open problems are discussed.


title:
Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline
  Reinforcement Learning

abstract:
  Dynamic mechanism design has garnered significant attention from both
computer scientists and economists in recent years. By allowing agents to
interact with the seller over multiple rounds, where agents' reward functions
may change with time and are state dependent, the framework is able to model a
rich class of real world problems. In these works, the interaction between
agents and sellers are often assumed to follow a Markov Decision Process (MDP).
We focus on the setting where the reward and transition functions of such an
MDP are not known a priori, and we are attempting to recover the optimal
mechanism using an a priori collected data set. In the setting where the
function approximation is employed to handle large state spaces, with only mild
assumptions on the expressiveness of the function class, we are able to design
a dynamic mechanism using offline reinforcement learning algorithms. Moreover,
learned mechanisms approximately have three key desiderata: efficiency,
individual rationality, and truthfulness. Our algorithm is based on the
pessimism principle and only requires a mild assumption on the coverage of the
offline data set. To the best of our knowledge, our work provides the first
offline RL algorithm for dynamic mechanism design without assuming uniform
coverage.


title:
Gravitational-wave evolution of new-born magnetars with different
  deformed structure

abstract:
  Weak and continuous gravitational-wave (GW) radiation can be produced by
newborn magnetars with deformed structure and is expected to be detected by the
Einstein telescope (ET) in the near future. In this work we assume that the
deformed structure of a nascent magnetar is not caused by a single mechanism
but by multiple time-varying quadrupole moments such as those present in
magnetically-induced deformation, starquake-induced ellipticity, and accretion
column-induced deformation. The magnetar loses its angular momentum through
accretion, magnetic dipole radiation, and GW radiation. Within this scenario,
we calculate the evolution of GWs from a newborn magnetar by considering the
above three deformations. We find that the GW evolution depends on the physical
parameters of the magnetar (e.g., period and surface magnetic field), the
adiabatic index, and the fraction of poloidal magnetic energy to the total
magnetic energy. In general the GW radiation from a magnetically-induced
deformation is dominant if the surface magnetic field of the magnetar is large,
but the GW radiation from magnetar starquakes is more efficient when there is a
larger adiabatic index if all other magnetar parameters remain the same. We
also find that the GW radiation is not very sensitive to different magnetar
equations of state.


title:
Origins of multi-sublattice magnetism and superexchange interactions in
  double-double perovskite CaMnCrSbO6

abstract:
  Studies on double-double perovskite CaMnCrSbO6 (space group : P42n) suggest
ferrimagnetic order (TC~49 K), with opposite alignment of Mn2+ and Cr3+ spins.
The unit cell comprises of two different Mn atoms, denoted as Mn(1) and Mn(2)
which have tetrahedral and planar coordination of oxygen atoms, respectively,
while the Cr atom is in the centre of orthorhombically distorted octahedron.
The density functional theory, Wannier function analysis, and mean-field
calculations are used to investigate this compound. The super-exchange
strengths obtained from DFT are an order of magnitude smaller than those
reported for various magnetic perovskites and double perovskites. The nearest
neighbour (along c-direction) Mn(1)-O-Mn(2) superexchange interactions are
found to be anti-ferromagnetic and smaller than the next nearest neighbour
Cr-O-O-Cr super-superexchange interactions, which are found to be
ferromagnetic. The in-plane Mn(1)-O-Mn(2) superexchange, also
antiferromagnetic, is much smaller due to greater separation between the Mn
atoms. The Mn(1)-O-Cr and Mn(2)-O-Cr superexchange interactions are found to be
antiferromagnetic. The signs of exchange interactions from DFT predict that
while the Cr3+ spins can be arranged ferromagnetically, the alternate Mn2+
spins have to be arranged antiferromagnetically. Due to difference in ligand
symmetry, the Mn(2)-O-Cr superexchange is weaker than the Mn(1)-O-Cr
superexchange, thus effectively resulting in ferrimagnetism, as observed
experimentally too. The superexchange mechanisms have been analysed in terms of
the hopping integrals between the Wannier orbitals of the transition metal ions
and oxygen. Our mean field calculations reveal that assuming a set of four
magnetic sub-lattice for Mn2+ spins and a single magnetic lattice for Cr3+
spins yields a much improved TC, unlike the use of the two magnetic sublattice
model which yields a much higher TC.


title: Learning to Solve Vehicle Routing Problems: A Survey

abstract:
  This paper provides a systematic overview of machine learning methods applied
to solve NP-hard Vehicle Routing Problems (VRPs). Recently, there has been a
great interest from both machine learning and operations research communities
to solve VRPs either by pure learning methods or by combining them with the
traditional hand-crafted heuristics. We present the taxonomy of the studies for
learning paradigms, solution structures, underlying models, and algorithms. We
present in detail the results of the state-of-the-art methods demonstrating
their competitiveness with the traditional methods. The paper outlines the
future research directions to incorporate learning-based solutions to overcome
the challenges of modern transportation systems.


title: Assistive Recipe Editing through Critiquing

abstract:
  There has recently been growing interest in the automatic generation of
cooking recipes that satisfy some form of dietary restrictions, thanks in part
to the availability of online recipe data. Prior studies have used pre-trained
language models, or relied on small paired recipe data (e.g., a recipe paired
with a similar one that satisfies a dietary constraint). However, pre-trained
language models generate inconsistent or incoherent recipes, and paired
datasets are not available at scale. We address these deficiencies with
RecipeCrit, a hierarchical denoising auto-encoder that edits recipes given
ingredient-level critiques. The model is trained for recipe completion to learn
semantic relationships within recipes. Our work's main innovation is our
unsupervised critiquing module that allows users to edit recipes by interacting
with the predicted ingredients; the system iteratively rewrites recipes to
satisfy users' feedback. Experiments on the Recipe1M recipe dataset show that
our model can more effectively edit recipes compared to strong
language-modeling baselines, creating recipes that satisfy user constraints and
are more correct, serendipitous, coherent, and relevant as measured by human
judges.


title: COGMEN: COntextualized GNN based Multimodal Emotion recognitioN

abstract:
  Emotions are an inherent part of human interactions, and consequently, it is
imperative to develop AI systems that understand and recognize human emotions.
During a conversation involving various people, a person's emotions are
influenced by the other speaker's utterances and their own emotional state over
the utterances. In this paper, we propose COntextualized Graph Neural Network
based Multimodal Emotion recognitioN (COGMEN) system that leverages local
information (i.e., inter/intra dependency between speakers) and global
information (context). The proposed model uses Graph Neural Network (GNN) based
architecture to model the complex dependencies (local and global information)
in a conversation. Our model gives state-of-the-art (SOTA) results on IEMOCAP
and MOSEI datasets, and detailed ablation experiments show the importance of
modeling information at both levels.


title: Declaration-based Prompt Tuning for Visual Question Answering

abstract:
  In recent years, the pre-training-then-fine-tuning paradigm has yielded
immense success on a wide spectrum of cross-modal tasks, such as visual
question answering (VQA), in which a visual-language (VL) model is first
optimized via self-supervised task objectives, e.g., masked language modeling
(MLM) and image-text matching (ITM), and then fine-tuned to adapt to downstream
task (e.g., VQA) via a brand-new objective function, e.g., answer prediction.
The inconsistency of the objective forms not only severely limits the
generalization of pre-trained VL models to downstream tasks, but also requires
a large amount of labeled data for fine-tuning. To alleviate the problem, we
propose an innovative VL fine-tuning paradigm (named Declaration-based Prompt
Tuning, abbreviated as DPT), which jointly optimizes the objectives of
pre-training and fine-tuning of VQA model, boosting the effective adaptation of
pre-trained VL models to the downstream task. Specifically, DPT reformulates
the objective form of VQA task via (1) textual adaptation, which converts the
given questions into declarative sentence-form for prompt-tuning, and (2) task
adaptation, which optimizes the objective function of VQA problem in the manner
of pre-training phase. Experimental results on GQA dataset show that DPT
outperforms the fine-tuned counterpart by a large margin regarding accuracy in
both fully-supervised (2.68%) and zero-shot/few-shot (over 31%) settings. All
the data and codes will be available to facilitate future research.


title:
MMINR: Multi-frame-to-Multi-frame Inference with Noise Resistance for
  Precipitation Nowcasting with Radar

abstract:
  Precipitation nowcasting based on radar echo maps is essential in
meteorological research. Recently, Convolutional RNNs based methods dominate
this field, but they cannot be solved by parallel computation resulting in
longer inference time. FCN based methods adopt a multi-frame-to-single-frame
inference (MSI) strategy to avoid this problem. They feedback into the model
again to predict the next time step to get multi-frame nowcasting results in
the prediction phase, which will lead to the accumulation of prediction errors.
In addition, precipitation noise is a crucial factor contributing to high
prediction errors because of its unpredictability. To address this problem, we
propose a novel Multi-frame-to-Multi-frame Inference (MMI) model with Noise
Resistance (NR) named MMINR. It avoids error accumulation and resists
precipitation noise\'s negative effect in parallel computation. NR contains a
Noise Dropout Module (NDM) and a Semantic Restore Module (SRM). NDM
deliberately dropout noise simple yet efficient, and SRM supplements semantic
information of features to alleviate the problem of semantic information
mistakenly lost by NDM. Experimental results demonstrate that MMINR can attain
competitive scores compared with other SOTAs. The ablation experiments show
that the proposed NDM and SRM can solve the aforementioned problems.


title:
Soft and Hard Constrained Parametric Generative Schemes for Encoding and
  Synthesizing Airfoils

abstract:
  Traditional airfoil parametric technique has significant limitation in modern
aerodynamic optimization design.There is a strong demand for developing a
parametric method with good intuitiveness, flexibility and representative
accuracy. In this paper, two parametric generative schemes based on deep
learning methods are proposed to represent the complicate design space under
specific constraints. 1. Soft-constrained scheme: The CVAE-based model trains
geometric constraints as part of the network and can provide constrained
airfoil synthesis; 2. Hard-constrained scheme: The VAE-based model serves to
generate diverse airfoils, while an FFD-based technique projects the generated
airfoils to the final airfoils satisfying the given constraints. The
statistical results show that the reconstructed airfoils are accurate and
smooth without extra filters. The soft constrained scheme tend to synthesize
and explore airfoils efficiently and effectively, concentrating to the
reference airfoil in both geometry space and objective space. The constraints
will loose for a little bit because the inherent property of the model. The
hard constrained scheme tend to generate and explore airfoils in a wider range
for both geometry space and objective space, and the distribution in objective
space is closer to normal distribution. The synthesized airfoils through this
scheme strictly conform with constraints, though the projection may produce
some odd airfoil shapes.


title:
Modeling Gamma-ray burst Afterglow observations with an Off-axis Jet
  emission

abstract:
  Gamma-ray bursts (GRBs) are fascinating extragalactic objects. They represent
a fantastic opportunity to investigate unique properties not exhibited in other
sources. Multi-wavelength afterglow observations from some short- and
long-duration GRBs reveal an atypical long-lasting emission that evolves
differently from the canonical afterglow light curves favoring the off-axis
emission. We present an analytical synchrotron afterglow scenario, and the
hydrodynamical evolution of an off-axis top-hat jet decelerated in a stratified
surrounding environment. The analytical synchrotron afterglow model is shown
during the coasting, deceleration (off- and on-axis emission), and the
post-jet-break decay phases, and the hydrodynamical evolution is computed by
numerical simulations showing the time evolution of the Doppler factor, the
half-opening angle, the bulk Lorentz factor, and the deceleration radius. We
show that numerical simulations are in good agreement with those derived with
our analytical approach. We apply the current synchrotron model and describe
successfully the delayed non-thermal emission observed in a sample of long and
short GRBs with evidence of off-axis emission. Furthermore, we provide
constraints on the possible afterglow emission by requiring the
multi-wavelength upper limits derived for the closest Swift-detected GRBs and
promising gravitational-wave events.


title: KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning

abstract:
  While hyper-parameters (HPs) are important for knowledge graph (KG) learning,
existing methods fail to search them efficiently. To solve this problem, we
first analyze the properties of different HPs and measure the transfer ability
from small subgraph to the full graph. Based on the analysis, we propose an
efficient two-stage search algorithm KGTuner, which efficiently explores HP
configurations on small subgraph at the first stage and transfers the
top-performed configurations for fine-tuning on the large full graph at the
second stage. Experiments show that our method can consistently find better HPs
than the baseline algorithms within the same time budget, which achieves
{9.1\%} average relative improvement for four embedding models on the
large-scale KGs in open graph benchmark.


title:
Fracture of silicate glasses: Micro-cavities and correlations between
  atomic-level properties

abstract:
  We use large-scale simulations to investigate the dynamic fracture of silica
and sodium-silicate glasses under uniaxial tension. The stress-strain curves
demonstrate that silica glass is brittle whereas the glasses rich in Na show
pronounced ductility. A strong composition dependence is also seen in the crack
velocity which is on the order of 1800 m/s for glasses with low Na
concentration and decreases to 700 m/s if the concentration is high. We find
that during the fracture of Na-rich glasses very irregular cavities as large as
3-4 nm form ahead of the crack front, indicating the presence of nanoductility
in these glasses. Before fracture occurs, the local composition, structure, and
mechanical properties are heterogeneous in space and show a strong dependence
on the applied strain. Further analysis of the correlations between these local
properties allows to obtain a better microscopic understanding of the
deformation and fracture of glasses and how the local heating close to the
crack tip, up to several hundred degrees, permits the structure to relax.


title:
Rate-Splitting Multiple Access for 6G -- Part II: Interplay with
  Integrated Sensing and Communications

abstract:
  This letter is the second part of a three-part tutorial focusing on
rate-splitting multiple access (RSMA) for 6G. As Part II of the tutorial, this
letter addresses the interplay between RSMA and integrated radar sensing and
communications (ISAC). In particular, we introduce a general RSMAassisted ISAC
architecture, where the ISAC platform has a dual capability to simultaneously
communicate with downlink users and probe detection signals to a moving target.
Then, the metrics of radar sensing and communications are respectively
introduced, followed by a RSMA-assisted ISAC waveform design example which
jointly minimizes the Cramer-Rao bound (CRB) of target estimation and maximizes
the minimum fairness rate (MFR) amongst communication users subject to the
per-antenna power constraint. The superiority of RSMA-assisted ISAC is verifed
through simulation results in both terrestrial and satellite scenarios. RSMA is
demonstrated to be a powerful multiple access and interference management
strategy for ISAC, and provides a better communication-sensing trade-off
compared with the conventional benchmark strategies. Consequently, RSMA is a
promising technology for next generation multiple access (NGMA) and future
networks such as 6G and beyond.


title:
Quartz Whispering-Gallery-Mode Resonator with Microfluidic Chip as
  Sensor for Permittivity Measurement of Liquids

abstract:
  Studies of biological solutions require high measurement accuracy, the
ability to detect low changes in substance concentration, and small amounts of
the liquid under test. A microwave complex permittivity measurement technique
based on a high-quality-factor whispering-gallery-mode resonator with a
microfluidic chip allows small amounts of dielectric liquids to be investigated
with high accuracy. An existing technique based on a sapphire resonator does
not provide for a low-concentration detection limit for substances with low
molecular weight. Here, we present an advanced technique based on a quartz
dielectric resonator. The detection limit obtained for glucose in water
solution was found to be about one order of magnitude lower for the quartz
resonator cell than for the sapphire-resonator-based measurement cell. The
limit is about or lower than the concentration of glucose in human blood. This
fact means that the technique can be used as the sensor for investigations of
biological solutions in the microwave range. A study of glucose, lactalbumin,
and bovine serum albumin was successfully performed in the Ka -band using the
developed sensor.


title:
Experimental Study of Concise Representations of Concepts and
  Dependencies

abstract:
  In this paper we are interested in studying concise representations of
concepts and dependencies, i.e., implications and association rules. Such
representations are based on equivalence classes and their elements, i.e.,
minimal generators, minimum generators including keys and passkeys, proper
premises, and pseudo-intents. All these sets of attributes are significant and
well studied from the computational point of view, while their statistical
properties remain to be studied. This is the purpose of this paper to study
these singular attribute sets and in parallel to study how to evaluate the
complexity of a dataset from an FCA point of view. In the paper we analyze the
empirical distributions and the sizes of these particular attribute sets. In
addition we propose several measures of data complexity relying on these
attribute sets in considering real-world and related randomized datasets.


title:
Paired associative stimulation demonstrates alterations in motor
  cortical synaptic plasticity in patients with hepatic encephalopathy

abstract:
  Introduction: Hepatic encephalopathy (HE) is a potentially reversible brain
dysfunction as result of liver failure. Altered synaptic plasticity is supposed
to play a major role in the pathophysiology of HE. Here, we used paired
associative stimulation with an intra-stimulus interval of 25ms (PAS25), a
non-invasive transcranial magnetic stimulation (TMS) protocol, to study
synaptic plasticity of the motor cortex in patients with manifest HE. Materials
and methods: Twenty HE-patients and twenty-three healthy controls were enrolled
in the study. Motor evoked potential (MEP) amplitudes were used as measure for
cortical excitability. Time course of MEP amplitude changes after PAS25 were
compared between the groups. Results: MEP-amplitudes significantly increased
after PAS25 in the control group indicating PAS25-induced synaptic plasticity
in healthy subjects. On the other hand, MEP-amplitudes within the HE-group did
not change and were significantly lower than in the control group, indicating
no plasticity induction. Discussion: The reduced synaptic plasticity in HE
might represent a valuable link between pathological changes on the molecular
level and early clinical symptoms of the disease. The reduced plasticity may be
caused by disturbances in the glutamatergic neurotransmission, due to ammonia
overload. Conclusion: Our study shows a reduced synaptic plasticity over the
primary motor cortex in HE. Further research is needed to determine the
relation between altered plasticity and both motor and non-motor symptoms of
the disease.


title: Optimal Algorithms for Mean Estimation under Local Differential Privacy

abstract:
  We study the problem of mean estimation of $\ell_2$-bounded vectors under the
constraint of local differential privacy. While the literature has a variety of
algorithms that achieve the asymptotically optimal rates for this problem, the
performance of these algorithms in practice can vary significantly due to
varying (and often large) hidden constants. In this work, we investigate the
question of designing the protocol with the smallest variance. We show that
PrivUnit (Bhowmick et al. 2018) with optimized parameters achieves the optimal
variance among a large family of locally private randomizers. To prove this
result, we establish some properties of local randomizers, and use
symmetrization arguments that allow us to write the optimal randomizer as the
optimizer of a certain linear program. These structural results, which should
extend to other problems, then allow us to show that the optimal randomizer
belongs to the PrivUnit family.
  We also develop a new variant of PrivUnit based on the Gaussian distribution
which is more amenable to mathematical analysis and enjoys the same optimality
guarantees. This allows us to establish several useful properties on the exact
constants of the optimal error as well as to numerically estimate these
constants.


title:
A quantitative variational analysis of the staircasing phenomenon for a
  second order regularization of the Perona-Malik functional

abstract:
  We consider the Perona-Malik functional in dimension one, namely an integral
functional whose Lagrangian is convex-concave with respect to the derivative,
with a convexification that is identically zero. We approximate and regularize
the functional by adding a term that depends on second order derivatives
multiplied by a small coefficient. We investigate the asymptotic behavior of
minima and minimizers as this small parameter vanishes. In particular, we show
that minimizers exhibit the so-called staircasing phenomenon, namely they
develop a sort of microstructure that looks like a piecewise constant function
at a suitable scale. Our analysis relies on Gamma-convergence results for a
rescaled functional, blow-up techniques, and a characterization of local
minimizers for the limit problem. This approach can be extended to more general
models.


title:
Alignahead: Online Cross-Layer Knowledge Extraction on Graph Neural
  Networks

abstract:
  Existing knowledge distillation methods on graph neural networks (GNNs) are
almost offline, where the student model extracts knowledge from a powerful
teacher model to improve its performance. However, a pre-trained teacher model
is not always accessible due to training cost, privacy, etc. In this paper, we
propose a novel online knowledge distillation framework to resolve this
problem. Specifically, each student GNN model learns the extracted local
structure from another simultaneously trained counterpart in an alternating
training procedure. We further develop a cross-layer distillation strategy by
aligning ahead one student layer with the layer in different depth of another
student model, which theoretically makes the structure information spread over
all layers. Experimental results on five datasets including PPI,
Coauthor-CS/Physics and Amazon-Computer/Photo demonstrate that the student
performance is consistently boosted in our collaborative training framework
without the supervision of a pre-trained teacher model. In addition, we also
find that our alignahead technique can accelerate the model convergence speed
and its effectiveness can be generally improved by increasing the student
numbers in training. Code is available:
https://github.com/GuoJY-eatsTG/Alignahead


title:
Homological mirror symmetry of indecomposable Cohen-Macaulay modules for
  some degenerate cusp singularities

abstract:
  Burban-Drozd showed that the degenerate cusp singularities have tame
Cohen-Macaulay representation type, and classified all indecomposable
Cohen-Macaulay modules over them. One of their main example is the non-isolated
singularity $W=xyz$. On the other hand, Abouzaid-Auroux-Efimov-Katzarkov-Orlov
showed that $W=xyz$ is mirror to a pair of pants. In this paper, we investigate
homological mirror symmetry of these indecomposable Cohen-Macaulay modules for
$xyz$.
  Namely, we show that closed geodesics (with a flat $\mathbb{C}$-bundle) of a
hyperbolic pair of pants have a one-to-one correspondence with indecomposable
Cohen-Macaulay modules for $xyz$ with multiplicity one that are locally free on
the punctured spectrum. In particular, this correspondence is established first
by a geometric $A_{\infty}$-functor from the Fukaya category of the pair of
pants to the matrix factorization category of $xyz$, and next by the
correspondence between Cohen-Macaulay modules and matrix factorizations due to
Eisenbud. For the latter, we compute explicit Macaulayfications of modules from
Burban-Drozd's classification and find a canonical form of the corresponding
matrix factorizations. In the sequel, we will show that indecomposable modules
with higher multiplicity correspond to twisted complexes of closed geodesics.
  We also find mirror images of rank $1$ indecomposable Cohen-Macaulay modules
(of band type) over the singularity $W = x^{3} + y^{2} - xyz$ as closed loops
in the orbifold sphere $\mathbb{P}^1_{3,2,\infty}$.


title:
Decisive Roles of Intersite Coulomb Interactions in Charge Ordered
  Systems

abstract:
  Using {\it ab initio} approaches for extended Hubbard interactions coupled to
phonons, we reveal that the intersite Coulomb interaction plays decisive roles
in determining various distinctive phases of the paradigmatic charge ordered
materials of Ba$_{1-x}$K$_x A$O$_3$ ($A=$ Bi and Sb). We demonstrated that all
their salient doping dependent experiment features such as breathing
instabilities, anomalous phonon dispersions, and transition between
charge-density wave and superconducting states can be accounted very well if
self-consistently obtained nearest neighbor Hubbard interaction are included,
thus establishing a minimal criterion for reliable descriptions of spontaneous
charge orders in solids.


title:
BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented
  Dialog

abstract:
  A typical end-to-end task-oriented dialog system transfers context into
dialog state, and upon which generates a response, which usually faces the
problem of error propagation from both previously generated inaccurate dialog
states and responses, especially in low-resource scenarios. To alleviate these
issues, we propose BORT, a back and denoising reconstruction approach for
end-to-end task-oriented dialog system. Squarely, to improve the accuracy of
dialog states, back reconstruction is used to reconstruct the original input
context from the generated dialog states since inaccurate dialog states cannot
recover the corresponding input context. To enhance the denoising capability of
the model to reduce the impact of error propagation, denoising reconstruction
is used to reconstruct the corrupted dialog state and response. Extensive
experiments conducted on MultiWOZ 2.0 and CamRest676 show the effectiveness of
BORT. Furthermore, BORT demonstrates its advanced capabilities in the zero-shot
domain and low-resource scenarios.


title:
Manipulating Interlayer Excitons for Ultra-pure Near-infrared Quantum
  Light Generation

abstract:
  Interlayer excitons (IXs) formed at the interface of atomically-thin
semiconductors possess various novel properties. In a parallel development,
nanoscale strain engineering has emerged as an effective means for creating 2D
quantum light sources. Exploring the intersection of these two exciting areas,
where strain and defects are exploited for the manipulation of IX toward the
emergence of new functionalities, is currently at a nascent stage. Here, using
MoS2/WSe2 heterostructure as a model system, we demonstrate how strain,
defects, and layering can be utilized to create defect-bound IXs capable of
bright, robust, and tunable quantum light emission in the technologically
important near-infrared spectral range. We were able to achieve ultra-high
single-photon purity with g(2)(0) = 0.01. Our strategy of creating
site-controlled QEs from the defect-bound IXs represents a paradigm shift in 2D
quantum photonics research, from engineering intralayer exciton in monolayer
structures towards IXs at the interface of 2D heterostructures.


title:
dPRO: A Generic Profiling and Optimization System for Expediting
  Distributed DNN Training

abstract:
  Distributed training using multiple devices (i.e., GPU servers) has been
widely adopted for learning DNN models over large datasets. However, the
performance of large-scale distributed training tends to be far from linear
speed-up in practice. Given the complexity of distributed systems, it is
challenging to identify the root cause(s) of inefficiency and exercise
effective performance optimizations when unexpected low training speed occurs.
To date, there exists no software tool which diagnoses performance issues and
helps expedite distributed DNN training, while the training can be run using
different machine learning frameworks. This paper proposes dPRO, a toolkit that
includes: (1) an efficient profiler that collects runtime traces of distributed
DNN training across multiple frameworks, especially fine-grained communication
traces, and constructs global data flow graphs including detailed communication
operations for accurate replay; (2) an optimizer that effectively identifies
performance bottlenecks and explores optimization strategies (from computation,
communication and memory aspects) for training acceleration. We implement dPRO
on multiple deep learning frameworks (PyTorch, TensorFlow, MXNet) and
representative communication schemes (AllReduce and Parameter Server
architecture). Extensive experiments show that dPRO predicts performance of
distributed training in various settings with<5% errors in most cases and finds
optimization strategies with up to87.1%speed-up over the baselines.


title:
Scheduling Coflows with Precedence Constraints for Minimizing the Total
  Weighted Completion Time in Identical Parallel Networks

abstract:
  Coflow is a recently proposed network abstraction for data-parallel computing
applications. This paper considers scheduling coflows with precedence
constraints in identical parallel networks, such as to minimize the total
weighted completion time of coflows. The identical parallel network is an
architecture based on multiple network cores running in parallel. In the
divisible coflow scheduling problem, the proposed algorithm achieves
$(6-\frac{2}{m})\mu$ and $(5-\frac{2}{m})\mu$ approximate ratios for arbitrary
release time and zero release time, respectively, where $m$ is the number of
network cores and $\mu$ is the coflow number of the longest path in the
precedence graph. In the indivisible coflow scheduling problem, the proposed
algorithm achieves $(7-\frac{2}{m})\mu$ and $(6-\frac{2}{m})\mu$ approximate
ratios for arbitrary release time and zero release time, respectively. In the
single network core scheduling problem, we propose a $5\mu$-approximation
algorithm with arbitrary release times, and a $4\mu$-approximation without
release time. Moreover, the proposed algorithm can be modified to solve the
coflows of multi-stage jobs scheduling problem. In multi-stage jobs, coflow is
transferred between servers to enable starting of next stage. This means that
there are precedence constraints between coflows of job. Our result represents
an improvement upon the previous best approximation ratio of $O(\tilde{\mu}
\log(N)/ \log(\log(N)))$ where $\tilde{\mu}$ is the maximum number of coflows
in a job and $N$ is the number of servers.


title: Speaker Recognition in the Wild

abstract:
  In this paper, we propose a pipeline to find the number of speakers, as well
as audios belonging to each of these now identified speakers in a source of
audio data where number of speakers or speaker labels are not known a priori.
We used this approach as a part of our Data Preparation pipeline for Speech
Recognition in Indic Languages
(https://github.com/Open-Speech-EkStep/vakyansh-wav2vec2-experimentation). To
understand and evaluate the accuracy of our proposed pipeline, we introduce two
metrics: Cluster Purity, and Cluster Uniqueness. Cluster Purity quantifies how
"pure" a cluster is. Cluster Uniqueness, on the other hand, quantifies what
percentage of clusters belong only to a single dominant speaker. We discuss
more on these metrics in section \ref{sec:metrics}. Since we develop this
utility to aid us in identifying data based on speaker IDs before training an
Automatic Speech Recognition (ASR) model, and since most of this data takes
considerable effort to scrape, we also conclude that 98\% of data gets mapped
to the top 80\% of clusters (computed by removing any clusters with less than a
fixed number of utterances -- we do this to get rid of some very small clusters
and use this threshold as 30), in the test set chosen.


title:
Quantum spin liquid phase in the Shastry-Sutherland model detected by an
  improved level spectroscopic method

abstract:
  We study the spin-$1/2$ two-dimensional Shastry-Sutherland spin model by
exact diagonalization of clusters with periodic boundary conditions. We develop
an improved level spectroscopic technique using energy gaps between states with
different quantum numbers. The crossing points of some of the relative
(composite) gaps have much weaker finite-size drifts than the normally used
gaps defined only with respect to the ground state, thus allowing precise
determination of quantum critical points even with small clusters. Our results
support the picture of a spin liquid phase intervening between the well known
plaquette-singlet and antiferromagnetic ground states, with phase boundaries in
almost perfect agreement with a recent density matrix renormalization group
study, where much larger cylindrical lattices were used [J. Yang et al., Phys.
Rev. B {\bf 105}, L060409 (2022)]. The method of using composite low-energy
gaps to reduce scaling corrections has potentially broad applications in
numerical studies of quantum critical phenomena.


title:
New Techniques Based On Odd-Edge Total Colorings In Topological
  Cryptosystem

abstract:
  For building up twin-graphic lattices towards topological cryptograph, we
define four kinds of new odd-magic-type colorings: odd-edge graceful-difference
total coloring, odd-edge edge-difference total coloring, odd-edge edge-magic
total coloring, and odd-edge felicitous-difference total coloring in this
article. Our RANDOMLY-LEAF-ADDING algorithms are based on adding randomly
leaves to graphs for producing continuously graphs admitting our new
odd-magic-type colorings. We use complex graphs to make caterpillar-graphic
lattices and complementary graphic lattices, such that each graph in these new
graphic lattices admits a uniformly $W$-magic total coloring. On the other
hands, finding some connections between graphic lattices and integer lattices
is an interesting research, also, is important for application in the age of
quantum computer. We set up twin-type $W$-magic graphic lattices (as public
graphic lattices vs private graphic lattices) and $W$-magic graphic-lattice
homomorphism for producing more complex topological number-based strings.


title:
Towards hybrid inflation with $n_s=1$ in light of Hubble tension and
  primordial gravitational waves

abstract:
  Recently, it has been found that complete resolution of the Hubble tension
might point to a scale-invariant Harrison-Zeldovich spectrum of primordial
scalar perturbation, i.e. $n_s=1$ for $H_0\sim 73$km/s/Mpc. We show that for
well-known slow-roll models, if inflation ends by a waterfall instability with
respect to another field in the field space while inflaton is still at a deep
slow-roll region, $n_s$ can be lifted to $n_s= 1$. An extra bonus of our result
is that with pre-recombination early dark energy, chaotic $\phi^2$ inflation,
ruled out by Planck+BICEP/Keck in standard $\Lambda$CDM, can be revived, which
is now well within testable region of upcoming cosmic microwave background
B-mode experiments.


title: On the Non-Flatness Nature of Noncommutative Minkowski Spacetime

abstract:
  In the framework of twisted-diffeomorphism approach to noncommutative gravity
with canonical/Moyal-Weyl type noncommutative (NC) coordinate structure, we
show that the NC Minkowski spacetime parametrized either with spherical polar
coordinates or with parabolic coordinates has nontrivial NC corrections to
Riemann curvature tensor, Ricci tensor and curvature scalar. Apparently, there
are no such corrections if we choose rectilinear coordinates or even
cylindrical coordinates, for which the metric in the commutative-counterpart is
dependent on at most one coordinate only. We present both first order and
second order calculations. The emergent curvature corrections might seem to
raise the question of whether the statement of curvature is
coordinate-dependent, but note, for example, that the transformation from
spherical polar system to Cartesian system is not a diffeomorphism as such
since its non-injective nature makes it a local diffeomorphism. In other words,
if the flat-spacetime metric tensor in the commutative case depends on more
than one curvilinear coordinates, the introduction of noncommutativity among
these coordinates can possibly make the spacetime curved. It is worth remarking
that such a curvature emerges in the context of NC Minkowski spacetime in the
absence of any gauge or matter fields. It is purely an NC geometric effect.


title: Nilpotent quandles

abstract:
  A nilpotent quandle is a quandle whose inner automorphism group is nilpotent.
Such quandles have been called reductive in previous works, but it turns out
that their behaviour is in fact very close to nilpotency for groups. In
particular, we show that it is easy to characterise generating sets of such
quandles, and that they have the Hopf property. We also show how to construct
free nilpotent quandles from free nilpotent groups. We then use the properties
of nilpotent quandles to describe a simple presentation of their associated
group, and we use this to recover the classification of abelian quandles by
Lebed and Mortier [LM21]. We also study reduced quandles, and we show that the
reduced fundamental quandle is equivalent, as an invariant of links, to the
reduced peripheral system, sharpening a previous result of Hughes [Hug11].
Finally, we give a characterisation of nilpotency in terms of the associated
invariants of braids.


title:
Exploiting Correspondences with All-pairs Correlations for Multi-view
  Depth Estimation

abstract:
  Multi-view depth estimation plays a critical role in reconstructing and
understanding the 3D world. Recent learning-based methods have made significant
progress in it. However, multi-view depth estimation is fundamentally a
correspondence-based optimization problem, but previous learning-based methods
mainly rely on predefined depth hypotheses to build correspondence as the cost
volume and implicitly regularize it to fit depth prediction, deviating from the
essence of iterative optimization based on stereo correspondence. Thus, they
suffer unsatisfactory precision and generalization capability. In this paper,
we are the first to explore more general image correlations to establish
correspondences dynamically for depth estimation. We design a novel iterative
multi-view depth estimation framework mimicking the optimization process, which
consists of 1) a correlation volume construction module that models the pixel
similarity between a reference image and source images as all-to-all
correlations; 2) a flow-based depth initialization module that estimates the
depth from the 2D optical flow; 3) a novel correlation-guided depth refinement
module that reprojects points in different views to effectively fetch relevant
correlations for further fusion and integrate the fused correlation for
iterative depth update. Without predefined depth hypotheses, the fused
correlations establish multi-view correspondence in an efficient way and guide
the depth refinement heuristically. We conduct sufficient experiments on
ScanNet, DeMoN, ETH3D, and 7Scenes to demonstrate the superiority of our method
on multi-view depth estimation and its best generalization ability.


title:
Type IV Radio Bursts and Associated Active Regions in the Sunspot Cycle
  24

abstract:
  In this article, the association of solar radio type IV bursts with active
region location on the Sun is studied for the solar cycle 24. The active
regions associated with moving and stationary type IV bursts are categorised as
close to disk center and far from disk center regions based on their location
on the solar surface (i.e, $\leq 45^{\circ}$ or $\geq 45^{\circ}$,
respectively). The location of the active regions associated with type IV
bursts accompanied with coronal mass ejections (CMEs) are also studied. We
found that $\approx 30-40 \%$ of the active regions are located far from disk
center for all the bursts. It is found that most of the active regions
associated with stationary type IV bursts are close to disk center ($\approx
60-70 \%$). The active regions associated with moving type IV bursts are more
evenly distributed across the surface, i.e $\approx 56 \%$ and $\approx 44 \%$,
close to disk center and far from disk center regions, respectively. Most of
the burst having active region close to disk center indicate that these bursts
can be used to obtain physical properties such as electron density and magnetic
fields of the coronal mass ejections responsible for geomagnetic storms.


title: Vector Field Visualization of Single-Qubit State Tomography

abstract:
  As the variety of commercially available quantum computers continues to
increase so does the need for tools that can characterize, verify and validate
these computers. This work explores using quantum state tomography for
characterizing the performance of individual qubits and develops a vector field
visualization for presentation of the results. The proposed protocol is
demonstrated in simulation and on quantum computing hardware developed by IBM.
The results identify qubit performance features that are not reflected in the
standard models of this hardware, indicating opportunities to improve the
accuracy of these models. The proposed qubit evaluation protocol is provided as
free open-source software to streamline the task of replicating the process on
other quantum computing devices.


title:
cuHARM : a new GPU accelerated GR-MHD code and its application to ADAF
  disks

abstract:
  We introduce a new GPU-accelerated general-relativistic magneto-hydrodynamic
(GR-MHD) code based on HARM which we call cuHARM. The code is written in CUDA-C
and uses OpenMP to parallelize multi-GPU setups. Our code allows us to run high
resolution simulations of accretion disks and the formation and structure of
jets without the need of multi-node supercomputer infrastructure. A $256^3$
simulation is well within the reach of an Nvidia DGX-V100 server, with the
computation being a factor about 10 times faster if only the CPU was used. We
use this code to examine several disk structures all in the SANE state. We find
that: (i) increasing the magnetic field, while in the SANE state does not
affect the mass accretion rate; (ii) Simultaneous increase of the disk size and
the magnetic field, while keeping the ratio of energies fixed, lead to the
destruction of the jet once the magnetic flux through the horizon decrease
below a certain limit. This demonstrates that the existence of the jet is not a
linear function of the initial magnetic field strength; (iii) the structure of
the jet is a weak function of the adiabatic index of the gas, with relativistic
gas tend to have a wider jet.


title:
Generating Simple Directed Social Network Graphs for Information
  Spreading

abstract:
  Online social networks are a dominant medium in everyday life to stay in
contact with friends and to share information. In Twitter, users can connect
with other users by following them, who in turn can follow back. In recent
years, researchers studied several properties of social networks and designed
random graph models to describe them. Many of these approaches either focus on
the generation of undirected graphs or on the creation of directed graphs
without modeling the dependencies between reciprocal (i.e., two directed edges
of opposite direction between two nodes) and directed edges. We propose an
approach to generate directed social network graphs that creates reciprocal and
directed edges and considers the correlation between the respective degree
sequences.
  Our model relies on crawled directed graphs in Twitter, on which information
w.r.t. a topic is exchanged or disseminated. While these graphs exhibit a high
clustering coefficient and small average distances between random node pairs
(which is typical in real-world networks), their degree sequences seem to
follow a $\chi^2$-distribution rather than power law. To achieve high
clustering coefficients, we apply an edge rewiring procedure that preserves the
node degrees.
  We compare the crawled and the created graphs, and simulate certain
algorithms for information dissemination and epidemic spreading on them. The
results show that the created graphs exhibit very similar topological and
algorithmic properties as the real-world graphs, providing evidence that they
can be used as surrogates in social network analysis. Furthermore, our model is
highly scalable, which enables us to create graphs of arbitrary size with
almost the same properties as the corresponding real-world networks.


title: Observation of the Boson Peak in a 2D Material

abstract:
  The boson peak is an excess in the phonon density of states relative to the
Debye Model, which occurs at frequencies below the Debye limit. It is present
in most amorphous materials and, as was recently shown, can sometimes be found
also in crystals. Here we present first experimental evidence of the boson peak
in a 2D material, namely 2D silica (SiO$_2$). The measurements were obtained by
helium atom scattering. A dispersionless boson peak is seen at $6 \pm 0.5$ meV
(1.5$ \pm$ 0.15 THz) and $-6 \pm 1.5$ meV ($-1.5 \pm 0.4$ THz), with reasonable
evidence for a double excitation at $\pm 12 \pm 2.5$ meV ($2.9 \pm 0.6$ THz).


title:
Testing the Amp\`ere-Maxwell law on the photon mass and
  Lorentz-Poincar\'e symmetry violation with MMS multi-spacecraft data

abstract:
  The photon is commonly believed being the only free massless particle.
Deviations from the Amp\`ere-Maxwell law, due to a photon mass, real for the de
Broglie-Proca theory, or effective for the Lorentz-Poincar\'e Symmetry
Violation (LSV) in the Standard-Model Extension (SME) were sought in six years
of MMS satellite data. Surprisingly, in a minority of cases (but mostly in the
solar wind: 8.6\% for the modulus and 16.8 \% for the Cartesian components for
top-quality burst data and best tetrahedron configurations), deviations have
been found. After error analysis, the photon mass would be $1.74 \times
10^{-53}$ kg while the LSV parameter $|\vec{k}^{\rm AF}|$ value would be $4.95
\times 10^{-11}$ m$^{-1}$. These values are compared with current limits and
discussed.

categories: hep-ph hep-th physics.plasm-ph physics.space-ph


title: Square-free values of $\mathbf{n^2+n+1}$

abstract:
  In this paper we show that there exist infinitely many square-free numbers of
the form $n^2+n+1$. We achieve this by deriving an asymptotic formula by
improving the reminder term from previous results.


title: Plasma physics of the intracluster medium

abstract:
  This Chapter provides a brief tutorial on some aspects of plasma physics that
are fundamental to understanding the dynamics and energetics of the
intracluster medium (ICM). The tutorial is split into two parts: one that
focuses on the thermal plasma component -- its stability, viscosity,
conductivity, and ability to amplify magnetic fields to dynamical strengths via
turbulence and other plasma processes; and one that focuses on the non-thermal
population of charged particles known as cosmic rays -- their acceleration,
re-acceleration, and transport throughout the cluster volume. Observational
context is woven throughout the narrative, from constraints on the strength and
geometry of intracluster magnetic fields and the effective viscosity of the
ICM, to examples of radio halos, radio relics, and cluster shocks that can test
theories of particle acceleration. The promise of future X-ray missions to
probe intracluster turbulence and discover the impact of small-scale plasma
physics, coupled with sensitive, high-resolution radio observations of
synchrotron-emitting plasma that reveal the properties of intracluster magnetic
fields and particle-acceleration mechanisms, are likely to establish galaxy
clusters as the premier cosmic laboratories for deciphering the fundamental
physics of hot, dilute plasmas.


title:
FastRE: Towards Fast Relation Extraction with Convolutional Encoder and
  Improved Cascade Binary Tagging Framework

abstract:
  Recent work for extracting relations from texts has achieved excellent
performance. However, most existing methods pay less attention to the
efficiency, making it still challenging to quickly extract relations from
massive or streaming text data in realistic scenarios. The main efficiency
bottleneck is that these methods use a Transformer-based pre-trained language
model for encoding, which heavily affects the training speed and inference
speed. To address this issue, we propose a fast relation extraction model
(FastRE) based on convolutional encoder and improved cascade binary tagging
framework. Compared to previous work, FastRE employs several innovations to
improve efficiency while also keeping promising performance. Concretely, FastRE
adopts a novel convolutional encoder architecture combined with dilated
convolution, gated unit and residual connection, which significantly reduces
the computation cost of training and inference, while maintaining the
satisfactory performance. Moreover, to improve the cascade binary tagging
framework, FastRE first introduces a type-relation mapping mechanism to
accelerate tagging efficiency and alleviate relation redundancy, and then
utilizes a position-dependent adaptive thresholding strategy to obtain higher
tagging accuracy and better model generalization. Experimental results
demonstrate that FastRE is well balanced between efficiency and performance,
and achieves 3-10x training speed, 7-15x inference speed faster, and 1/100
parameters compared to the state-of-the-art models, while the performance is
still competitive.


title:
ChASE -- A Distributed Hybrid CPU-GPU Eigensolver for Large-scale
  Hermitian Eigenvalue Problems

abstract:
  As modern massively parallel clusters are getting larger with beefier compute
nodes, traditional parallel eigensolvers, such as direct solvers, struggle
keeping the pace with the hardware evolution and being able to scale
efficiently due to additional layers of communication and synchronization. This
difficulty is especially important when porting traditional libraries to
heterogeneous computing architectures equipped with accelerators, such as
Graphics Processing Unit (GPU). Recently, there have been significant
scientific contributions to the development of filter-based subspace
eigensolver to compute partial eigenspectrum. The simpler structure of these
type of algorithms makes for them easier to avoid the communication and
synchronization bottlenecks typical of direct solvers. The Chebyshev
Accelerated Subspace Eigensolver (ChASE) is a modern subspace eigensolver to
compute partial extremal eigenpairs of large-scale Hermitian eigenproblems with
the acceleration of a filter based on Chebyshev polynomials. In this work, we
extend our previous work on ChASE by adding support for distributed hybrid
CPU-multi-GPU computing architectures. Our tests show that ChASE achieves very
good scaling performance up to 144 nodes with 526 NVIDIA A100 GPUs in total on
dense eigenproblems of size up to $360$k.


title: Hydrodynamic backreaction force of cosmological bubble expansion

abstract:
  As a promising probe for the new physics beyond the standard model of
particle physics in the early Universe, the predictions for the stochastic
gravitational wave background from a cosmological first-order phase transition
heavily rely on the bubble wall velocity determined by the bubble expansion
dynamics. The bubble expansion dynamics is governed by the competition between
the driving force from the effective potential difference and the backreaction
force from a sum of the thermal and friction forces induced by the temperature
jumping and out-of-equilibrium effects across the bubble wall, respectively.
Recently, a thermodynamic evaluation for the difference
$\Delta[(\bar{\gamma}^2-1)w]$ in the vicinity of the bubble wall is proposed to
account for this backreaction force in a local thermal equilibrium from the
enthalpy $w$ and the Lorentz factor $\bar{\gamma}\equiv(1-\bar{v}^2)^{-1/2}$ of
the wall-frame fluid velocity $\bar{v}$. However, this proposal has neglected
the hydrodynamic contribution from the sound shell with a non-vanishing fluid
velocity profile. In this paper, we propose a complete hydrodynamic evaluation
on the backreaction force for a non-runaway bubble expansion, whose wall
contribution exactly matches the previous thermodynamic evaluation.


title:
Qualitative Properties for a System Coupling Scaled Mean Curvature Flow
  and Diffusion

abstract:
  We consider a system consisting of a geometric evolution equation for a
hypersurface and a parabolic equation on this evolving hypersurface. More
precisely, we discuss mean curvature flow scaled with a term that depends on a
quantity defined on the surface coupled to a diffusion equation for that
quantity. Several properties of solutions are analyzed. Emphasis is placed on
to what extent the surface in our setting qualitatively evolves similar as for
the usual mean curvature flow. To this end, we show that the surface area is
strictly decreasing but give an example of a surface that exists for infinite
times nevertheless. Moreover, mean convexity is conserved whereas convexity is
not. Finally, we construct an embedded hypersurface that develops a
self-intersection in the course of time. Additionally, a formal explanation of
how our equations can be interpreted as a gradient flow is included.


title:
Bott-Chern cohomology and the Hartogs extension theorem for
  pluriharmonic functions

abstract:
  Let $X$ be a cohomologically $(n-1)$-complete complex manifolds of dimension
$n\geq 2$. We prove a vanishing result for the Bott-Chern cohomology of $X$
with compact support, which combined with the well-known technique of
Ehrenpreis implies a Hartogs type extension theorem for pluriharmonic functions
on $X$.


title: Orbital equivalence classes of finite coverings of geodesic flows

abstract:
  Let $M$ be a closed 3-manifold admitting a finite cover of index n along the
fibers over the unit tangent bundle of a closed surface. We prove that if n is
odd, there is only one Anosov flow on M up to orbital equivalence, and if n is
even, there are two orbital equivalence classes of Anosov flows on M.


title: Are GAN-based Morphs Threatening Face Recognition?

abstract:
  Morphing attacks are a threat to biometric systems where the biometric
reference in an identity document can be altered. This form of attack presents
an important issue in applications relying on identity documents such as border
security or access control. Research in generation of face morphs and their
detection is developing rapidly, however very few datasets with morphing
attacks and open-source detection toolkits are publicly available. This paper
bridges this gap by providing two datasets and the corresponding code for four
types of morphing attacks: two that rely on facial landmarks based on OpenCV
and FaceMorpher, and two that use StyleGAN 2 to generate synthetic morphs. We
also conduct extensive experiments to assess the vulnerability of four
state-of-the-art face recognition systems, including FaceNet, VGG-Face,
ArcFace, and ISV. Surprisingly, the experiments demonstrate that, although
visually more appealing, morphs based on StyleGAN 2 do not pose a significant
threat to the state to face recognition systems, as these morphs were
outmatched by the simple morphs that are based facial landmarks.


title:
Galaxy cluster A2142: halo boundary, `red sequence', properties of
  galaxies according to SDSS

abstract:
  The paper presents the results of studying the dynamics of galaxies,
properties of early-type galaxies, properties of galaxies with the quenched
star formation (QGs) in the A 2142 based on the archival data from the Sloan
Digital Sky Survey. We found the observed halo boundary, the splashback radius
$R_{sp}$, which is equal to 4.12 Mpc ($M_r < -20\,.\!\!^{\rm m}3$) and 4.06 Mpc
($M_r < -21\,.\!\!^{\rm m}3$) over the integral distribution of the number of
galaxies as a function of the squared distance from the center. We have studied
how early-type galaxies are distributed in the center and in the outskirts of
the cluster ($R/R_{200}$ < 3, $M_r < -20\,.\!\!^{\rm m}3$) and plotted the red
sequence in the form of (g - r) = $(-0.024\pm0.001)M_r + (0.441\pm0.005$).
Among all the cluster galaxies, the galaxies with the quenched star formation (
$- 12\,yr^{- 1} < \log sSFR < - 10.75\,yr^{- 1}$) make up about one third. We
have found that the fraction of QGs beyond the splashback $R_{sp}$ is the same
as in the field at the same z with coordinates of the center of $16\,.\!\!^{\rm
h}5$, $31^{\circ}$ and a size of $300'$. For galaxies with the stellar masses
$\log M_*/M_{\odot}$ = [10.5;11.0] (this is the main mass range of QGs), after
entering the cluster, there is a decrease in the radii $R_{90,r}$ by about 30\%
when moving towards the center.


title:
Non-concentration phenomenon reaction-diffusion systems with mass
  dissipation

abstract:
  Reaction-diffusion systems with mass dissipation are known to possess blow-up
solutions in higher dimensions when the nonlinearities have super quadratic
growth rates. In dimension one, it has been shown recently that one can have
global existence of bounded solutions if nonlinearities are at most cubic. For
the cubic intermediate sum condition, i.e. nonlinearities might have
arbitrarily high growth rates, an additional entropy inequality had to be
imposed. In this article, we remove this extra entropy assumption completely
and obtain global boundedness for reaction-diffusion systems with cubic
intermediate sum condition. The novel idea is to show a non-concentration
phenomenon for mass dissipating systems, that is the mass dissipation implies a
dissipation in a Morrey space $\mathsf{M}^{1,\delta}(\Omega)$ for some
$\delta>0$. As far as we are concerned, it is the first time such a bound is
derived for mass dissipating reaction-diffusion systems. Extensions include
global existence mass controlled systems with slightly-super cubic intermediate
sum condition.


title:
GWFish: A simulation software to evaluate parameter-estimation
  capabilities of gravitational-wave detector networks

abstract:
  An important step in the planning of future gravitational-wave (GW) detectors
and of the networks they will form is the estimation of their detection and
parameter-estimation capabilities, which is the basis of science-case studies.
Several future GW detectors have been proposed or are under development, which
might also operate and observe in parallel. These detectors include
terrestrial, lunar, and space-borne detectors. In this paper, we present
GWFish, a new software to simulate GW detector networks and to calculate
measurement uncertainties based on the Fisher-matrix approximation. GWFish
models the impact of detector motion on PE and makes it possible to analyze
multiband scenarios, i.e., observation of a GW signal by different detectors in
different frequency bands. We showcase a few examples for the Einstein
Telescope (ET) including the sky-localization of binary neutron stars, and ET's
capability to measure the polarization of GWs.


title:
Performance of a spaghetti calorimeter prototype with tungsten absorber
  and garnet crystal fibres

abstract:
  A spaghetti calorimeter (SPACAL) prototype with scintillating crystal fibres
was assembled and tested with electron beams of energy from 1 to 5 GeV. The
prototype comprised radiation-hard Cerium-doped Gd$_3$Al$_2$Ga$_3$O$_{12}$
(GAGG:Ce) and Y$_3$Al$_5$O$_{12}$ (YAG:Ce) embedded in a pure tungsten
absorber. The energy resolution was studied as a function of the incidence
angle of the beam and found to be of the order of $10\% / \sqrt{E} \oplus1\%$,
in line with the LHCb Shashlik technology. The time resolution was measured
with metal channel dynodes photomultipliers placed in contact with the fibres
or coupled via a light guide, additionally testing an optical tape to glue the
components. Time resolution of a few tens of picosecond was achieved for all
the energies reaching down to (18.5 $\pm$ 0.2) ps at 5 GeV.


title:
The Discovery of a Planetary Companion Interior to Hot Jupiter WASP-132
  b

abstract:
  Hot Jupiters are generally observed to lack close planetary companions, a
trend that has been interpreted as evidence for high-eccentricity migration. We
present the discovery and validation of WASP-132 c (TOI-822.02), a 1.85 $\pm$
0.10 $R_{\oplus}$ planet on a 1.01 day orbit interior to the hot Jupiter
WASP-132 b. Transiting Exoplanet Survey Satellite (TESS) and ground-based
follow-up observations, in conjunction with vetting and validation analysis,
enable us to rule out common astrophysical false positives and validate the
observed transit signal produced by WASP-132 c as a planet. Running the
validation tools \texttt{vespa} and \texttt{triceratops} on this signal yield
false positive probabilities of $9.02 \times 10^{-5}$ and 0.0107, respectively.
Analysis of archival CORALIE radial velocity data leads to a 3$\sigma$ upper
limit of 28.23 ms$^{-1}$ on the amplitude of any 1.01-day signal, corresponding
to a 3$\sigma$ upper mass limit of 37.35 $M_{\oplus}$. Dynamical simulations
reveal that the system is stable within the 3$\sigma$ uncertainties on
planetary and orbital parameters for timescales of $\sim$100 Myr. The existence
of a planetary companion near the hot Jupiter WASP-132 b makes the giant
planet's formation and evolution via high-eccentricity migration highly
unlikely. Being one of just a handful of nearby planetary companions to hot
Jupiters, WASP-132 c carries with it significant implications for the formation
of the system and hot Jupiters as a population.


title: PMBM-based SLAM Filters in 5G mmWave Vehicular Networks

abstract:
  Radio-based vehicular simultaneous localization and mapping (SLAM) aims to
localize vehicles while mapping the landmarks in the environment. We propose a
sequence of three Poisson multi-Bernoulli mixture (PMBM) based SLAM filters,
which handle the entire SLAM problem in a theoretically optimal manner. The
complexity of the three proposed SLAM filters is progressively reduced while
sustaining high accuracy by deriving SLAM density approximation with the
marginalization of nuisance parameters (either vehicle state or data
association). Firstly, the PMBM SLAM filter serves as the foundation, for which
we provide the first complete description based on a Rao-Blackwellized particle
filter. Secondly, the Poisson multi-Bernoulli (PMB) SLAM filter is based on the
standard reduction from PMBM to PMB, but involves a novel interpretation based
on auxiliary variables and a relation to Bethe free energy. Finally, using the
same auxiliary variable argument, we derive a marginalized PMB SLAM filter,
which avoids particles and is instead implemented with a low-complexity
cubature Kalman filter. We evaluate the three proposed SLAM filters in
comparison with the probability hypothesis density (PHD) SLAM filter in 5G
mmWave vehicular networks and show the computation-performance trade-off
between them.


title:
Mortensen Observer for a class of variational inequalities -Lost
  equivalence with stochastic filtering approaches

abstract:
  We address the problem of deterministic sequential estimation for a nonsmooth
dynamics in R governed by a variational inequality, as illustrated by the
Skorokhod problem with a reflective boundary condition at 0. For smooth
dynamics, Mortensen introduced an energy for the likelihood that the state
variable produces-up to perturbations disturbances-a given observation in a
finite time interval, while reaching a given target state at the final time.
The Mortensen observer is the minimiser of this energy. For dynamics given by a
variational inequality and therefore not reversible in time, we study the
definition of a Mortensen estimator. On the one hand, we address this problem
by relaxing the boundary constraint of the synthetic variable and then
proposing an approximated variant of the Mortensen estimator that uses the
resulting nonlinear smooth dynamics. On the other hand, inspired by the smooth
dynamics approach, we study the vanishing viscosity limit of the
Hamilton-Jacobi equation satisfied by the Hopf-Cole transform of the solution
of the robust Zakai equation. We prove a stability result that allows us to
interpret the limiting solution as the value function associated with a control
problem rather than an estimation problem. In contrast to the case of smooth
dynamics, here the zero-noise limit of the robust form of the Zakai equation
cannot be understood from the Bellman equation of the value function arising in
Mortensen's deterministic estimation. This may unveil a violation of
equivalence for non-reversible dynamics between the Mortensen approach and the
low noise stochastic approach for nonsmooth dynamics.


title: Hardy-Littlewood-type theorems for Fourier transforms in $\R^d$

abstract:
  We obtain Fourier inequalities in the weighted $L_p$ spaces for any
$1<p<\infty$ involving the Hardy-Ces\`aro and Hardy-Bellman operators. We
extend these results to product Hardy spaces for $p\le 1$. Moreover,
boundedness of the Hardy-Ces\`aro and Hardy-Bellman operators in various spaces
(Lebesgue, Hardy, BMO) is discussed. One of our main tools is an appropriate
version of the Hardy-Littlewood-Paley inequality $ \|\widehat{f}\|_{L_{p',q}}
\lesssim \left\|f\right\|_{L_{p,q}}$.


title:
Rigorous derivation of the macroscopic equations for the lattice
  Boltzmann method via the corresponding Finite Difference scheme

abstract:
  Lattice Boltzmann schemes are efficient numerical methods to solve a broad
range of problems under the form of conservation laws. However, they suffer
from a chronic lack of clear theoretical foundations. In particular, the
consistency analysis is still an open issue. We propose a rigorous derivation
of the macroscopic equations for any lattice Boltzmann scheme under acoustic
scaling. This is done by passing from a kinetic (lattice Boltzmann) to a
macroscopic (Finite Difference) point of view at a fully discrete level in
order to eliminate the non-conserved moments relaxing away from the
equilibrium. We rewrite the lattice Boltzmann scheme as a multi-step Finite
Difference scheme on the conserved variables, as introduced in our previous
contribution. We then perform the usual consistency analysis for Finite
Difference by exploiting its precise characterization using matrices of Finite
Difference operators. Though we present the derivation until second-order under
acoustic scaling, we provide all the elements to extend it to higher orders and
to other scalings, since the kinetic-macroscopic connection is conducted at the
fully discrete level. Finally, we show that our strategy yields, in a
mathematically rigorous setting, the same results as previous works in the
literature.


title:
Unmanned Aerial Vehicles Meet Reflective Intelligent Surfaces to Improve
  Coverage and Secrecy

abstract:
  The high configurability and low cost of Reflective Intelligent Surfaces
(RISs) made them a promising solution for enhancing the capabilities of Beyond
Fifth-Generation (B5G) networks. Recent works proposed to mount RISs on
Unmanned Aerial Vehicles (UAVs), combining the high network configurability
provided by RIS with the mobility brought by UAVs. However, the RIS represents
an additional weight that impacts the battery lifetime of the UAV. Furthermore,
the practicality of the resulting link in terms of communication channel
quality and security have not been assessed in detail. In this paper, we
highlight all the essential features that need to be considered for the
practical deployment of RIS-enabled UAVs. We are the first to show how the RIS
size and its power consumption impact the UAV flight time. We then assess how
the RIS size, carrier frequency, and UAV flying altitude affects the path loss.
Lastly, we propose a novel particle swarm-based approach to maximize coverage
and improve the confidentiality of transmissions in a cellular scenario with
the support of RISs carried by UAVs.


title:
View-labels Are Indispensable: A Multifacet Complementarity Study of
  Multi-view Clustering

abstract:
  Consistency and complementarity are two key ingredients for boosting
multi-view clustering (MVC). Recently with the introduction of popular
contrastive learning, the consistency learning of views has been further
enhanced in MVC, leading to promising performance. However, by contrast, the
complementarity has not received sufficient attention except just in the
feature facet, where the Hilbert Schmidt Independence Criterion (HSIC) term or
the independent encoder-decoder network is usually adopted to capture
view-specific information. This motivates us to reconsider the complementarity
learning of views comprehensively from multiple facets including the feature-,
view-label- and contrast- facets, while maintaining the view consistency. We
empirically find that all the facets contribute to the complementarity
learning, especially the view-label facet, which is usually neglected by
existing methods. Based on this, we develop a novel \underline{M}ultifacet
\underline{C}omplementarity learning framework for
\underline{M}ulti-\underline{V}iew \underline{C}lustering (MCMVC), which fuses
multifacet complementarity information, especially explicitly embedding the
view-label information. To our best knowledge, it is the first time to use
view-labels explicitly to guide the complementarity learning of views. Compared
with the SOTA baseline, MCMVC achieves remarkable improvements, e.g., by
average margins over $5.00\%$ and $7.00\%$ respectively in complete and
incomplete MVC settings on Caltech101-20 in terms of three evaluation metrics.


title: Tensor network investigation of the hard-square model

abstract:
  Using the corner-transfer matrix renormalization group to contract the tensor
network that describes its partition function, we investigate the nature of the
phase transitions of the hard-square model, one of the exactly solved models of
statistical physics for which Baxter has found an integrable manifold. The
motivation is twofold: assess the power of tensor networks for such models, and
probe the 2D classical analog of a 1D quantum model of hard-core bosons that
has recently attracted significant attention in the context of experiments on
chains of Rydberg atoms. Accordingly, we concentrate on two planes in the 3D
parameter space spanned by the activity and the coupling constants in the two
diagonal directions. We first investigate the only case studied so far with
Monte Carlo simulations, the case of opposite coupling constants. We confirm
that, away and not too far from the integrable 3-state Potts point, the
transition out of the period-3 phase appears to be unique in the Huse-Fisher
chiral universality class, albeit with significantly different exponents as
compared to Monte Carlo. We also identify two additional phase transitions not
reported so far for that model, a Lifshitz disorder line, and an Ising
transition for large enough activity. To make contact with 1D quantum models of
Rydberg atoms, we then turn to a plane where the ferromagnetic coupling is kept
fixed, and we show that the resulting phase diagram is very similar, the only
difference being that the Ising transition becomes first-order through a
tricritical Ising point, in agreement with Baxter's prediction that this plane
should contain a tricritical Ising point, and in remarkable, almost
quantitative agreement with the phase diagram of the 1D quantum version of the
model.


title: A General Relativistic Pendulum: Isochronous vs. Geodesic Motion

abstract:
  General Relativity (GR) is shown to be a complete theory with respect to the
isochrony of the pendulum. This guarantees that time can be measured with a
mechanical clock within the theory itself as a matter of principle. The proper
and coordinate oscillation frequencies of a simple pendulum are computed as a
function of 3 coordinate parameters: the distance of the fulcrum to the gravity
source, its length and the Schwarzschild radius. Novel GR effects appear in the
Schwarzschild coordinates as divergences, zeroes and other instabilities of the
oscillation frequencies that are absent in Newtonian gravity. The anomalies in
the proper and coordinate frequencies occur under extreme conditions: either
the fulcrum or the pendulum mass or both remain inside the Event Horizon.


title:
Encoding of direct 4D printing of isotropic single-material system for
  double-curvature and multimodal morphing

abstract:
  The ability to morph flat sheets into complex 3D shapes is extremely useful
for fast manufacturing and saving materials while also allowing volumetrically
efficient storage and shipment and a functional use. Direct 4D printing is a
compelling method to morph complex 3D shapes out of as-printed 2D plates.
However, most direct 4D printing methods require multi-material systems
involving costly machines. Moreover, most works have used an open-cell design
for shape shifting by encoding a collection of 1D rib deformations, which
cannot remain structurally stable. Here, we demonstrate the direct 4D printing
of an isotropic single-material system to morph 2D continuous bilayer plates
into doubly curved and multimodal 3D complex shapes whose geometry can also be
locked after deployment. We develop an inverse-design algorithm that integrates
extrusion-based 3D printing of a single-material system to directly morph a raw
printed sheet into complex 3D geometries such as a doubly curved surface with
shape locking. Furthermore, our inverse-design tool encodes the localized
shape-memory anisotropy during the process, providing the processing conditions
for a target 3D morphed geometry. Our approach could be used for conventional
extrusion-based 3D printing for various applications including biomedical
devices, deployable structures, smart textiles, and pop-up Kirigami structures.


title: One Picture is Worth a Thousand Words: A New Wallet Recovery Process

abstract:
  We introduce a new wallet recovery process. Our solution associates 1) visual
passwords: a photograph of a secretly picked object (Chabanne et al., 2013)
with 2) ImageNet classifiers transforming images into binary vectors and, 3)
obfuscated fuzzy matching (Galbraith and Zobernig, 2019) for the storage of
visual passwords/retrieval of wallet seeds. Our experiments show that the
replacement of long seed phrases by a photograph is possible.


title:
Secure Communication in Multifunctional Active Intelligent Reflection
  Surface-assisted Systems

abstract:
  In this paper, the secure performance of multiuser multiple-input
single-output wireless communications systems assisted by a multifunctional
active intelligent reflection surface (IRS) is investigated. The active IRS can
simultaneously reflect and amplify the incident signals and emit artificial
noise to combat potential wiretapping. We minimize the total system power
consumption by designing the phase, amplitude, and IRS mode selection of the
active IRS elements, as well as the precoder and artificial noise vector of the
base station (BS). The design is formulated as a non-convex optimization
problem guaranteeing communication security. To tackle the problem, this paper
proposes an iterative alternating algorithm to obtain an effective sub-optimal
solution. The simulation results show that the proposed scheme offers superior
secure performance over all the considered baseline schemes, especially when
the number of eavesdropper antennas is more than that of the BS.


title: Orbits of automorphism group of trinomial hypersurfaces

abstract:
  Trinomial hypersurfaces form a natural class of affine algebraic varieties
closely connected with varieties admitting a torus action of complexity one. We
investigate orbits of the automorphism group on these hypersurfaces. We prove
that each nonrigid trinomial variety has finite number of orbits. We
investigate singular orbits and this gives us description of all orbits for
some classes of flexible trinomial hypersurfaces. Also we obtain a description
of orbits for a class of hypersurfaces having a unique variable with power one
in the equation.


title: Momentum dependent gap in holographic superconductors revisited

abstract:
  We reconsider the angular dependence in gap structure of holographic
superconductors, which has not been treated carefully so far. For the vector
field model, we show that the normalizable ground state is in the p-wave state
because s-wave state is not normalizable. On the other hand, in the scalar
order model, the ground state is in the $s$-wave. The angle dependent gap
function is explicitly constructed in these models. We also suggest the
modified ansatz of the vector order which enables to discuss the order
$p_{x}\pm ip_{y}$ gap, which has not been possible so far. We have also
analytically investigated the critical temperature and the behavior of the gap
near there. Interestingly, for the fixed conformal dimension of the Cooper pair
operator, the critical temperature in vector model is higher than that of the
scalar model.


title:
Quantum Control for Time-dependent Noise by Inverse Geometric
  Optimization

abstract:
  Quantum systems are exceedingly difficult to engineer because they are
sensitive to various types of noises. In particular, time-dependent noises are
frequently encountered in experiments but how to overcome them remains a
challenging problem. In this work, we extend and apply the recently proposed
robust control technique of inverse geometric optimization to time-dependent
noises by working it in the filter-function formalism. The basic idea is to
parameterize the control filter function geometrically and minimize its overlap
with the noise spectral density. This then effectively reduces the noise
susceptibility of the controlled system evolution. We show that the proposed
method can produce high-quality robust pulses for realizing desired quantum
evolutions under realistic noise models, and thus will find practical
applications for current physical platforms.


title:
Tests of the parametrizations of Fragmentation Functions using data on
  inclusive pion and kaon production in unpolarized $pp$ collisions from the
  STAR collaboration and at the NICA project

abstract:
  The goal of this study is threefold: (a) to check which, if any, of the
published versions of the pion and kaon fragmentation functions is compatible
with the STAR data on semi-inclusive pion and kaon production in proton-proton
collisions; (b) to try to obtain a new version of the fragmentation functions
(CKL) which agrees better with the STAR data than the published versions; (c)
on the basis of these to make reliable predictions for the $p_T$ spectra of the
pions and kaons in inclusive pion and kaon production at the future NICA
proton-proton collider. The calculations are carried out in leading order of
quantum chromodynamics, using the well tested CJ15 parton distributions. We
consider the following pion and kaon fragmentation functions -- DSEHS-14,
DSEHS-17, LSS-15, HKNS-07 and AKK-08. Our analysis shows that almost all sets
of fragmentation functions provide a comparably good fit to STAR data, the only
exception being the kaon FFs in the AKK-08 versions, so they are not considered
for the NICA predictions for kaons. The NICA measurements at low $0.4 \leqslant
p_T \leqslant 0.5\, {\rm GeV}/c$ turn out to be rather sensitive to the choice
of fragmentation function both for charged pion and kaons. Also, measurements
of $\sigma^{\pi^+ -\pi^-}$ would distinguish CKL for pions from the other sets.
Unfortunately, this does not hold for kaons.


title:
A Simple Contrastive Learning Objective for Alleviating Neural Text
  Degeneration

abstract:
  The cross-entropy objective has proved to be an all-purpose training
objective for autoregressive language models (LMs). However, without
considering the penalization of problematic tokens, LMs trained using
cross-entropy exhibit text degeneration. To address this, unlikelihood training
has been proposed to force unlikely tokens to be assigned a low probability by
a LM. But unlikelihood does not consider the relationship between the label
tokens and the unlikely token candidates, thus showing marginal improvements in
degeneration. We propose a new contrastive token learning objective that
inherits the advantages of cross-entropy and unlikelihood training and avoids
their limitations. The key idea is to force a LM to generate high probabilities
for label tokens at each step while low probabilities of negative candidates.
Comprehensive experiments on language modeling and open-domain dialogue
generation tasks show that the proposed contrastive token objective yields less
repetitive texts, with a higher generation quality than unlikelihood training,
achieving the new state-of-the-art performance.


title:
Removable singularities for solutions of the fractional Heat Equation in
  time varying domains

abstract:
  In this paper we study removable singularities for solutions of the
fractional heat equation in time varying domains. We introduce associated
capacities and we study some of its metric and geometric properties.


title:
SDEs with no strong solution arising from a problem of stochastic
  control

abstract:
  We study a two-dimensional stochastic differential equation, which has a weak
solution but no strong solution. We show that this SDE shares notable
properties with Tsirelson's example of a one-dimensional SDE with no strong
solution. In contrast to Tsirelson's equation, which has a non-Markovian drift,
we consider a strong Markov martingale with Markovian diffusion coefficient. We
show that there is no strong solution of the SDE and that the natural
filtration of a weak solution is generated by a Brownian motion. We also
discuss an application of our results to a stochastic control problem for
martingales with fixed quadratic variation in a radially symmetric environment.


title: Improved Quantum Computing with the Higher-order Trotter Decomposition

abstract:
  It is crucial to simulate the controlled system evolution on a classical
computer for designing precise quantum control. However, computing the time
evolution operator is resource-consuming, especially when the total Hamiltonian
is not allowed to diagonalize. In this work, we mitigate this issue by
substituting the time evolution segments with their Trotter decompositions,
which reduces the propagator into a combination of single-qubit operations and
fixed-time system evolutions. The resulting procedure can provide substantial
speed gain with acceptable costs in the propagator error. As demonstration, we
apply the proposed strategy to improve the efficiency of the gradient ascent
pulse engineering (GRAPE) algorithm for searching optimal control fields.
Furthermore, we show that the higher-order Trotter decompositions can provide
efficient ansatzes for the variational quantum algorithm (VQA), leading to
improved performance on solving the ground-state problem. The strategy
presented here is also applicable for many other quantum optimization and
simulation tasks.