title:
Multi-Material Blind Beam Hardening Correction Based on Non-Linearity
  Adjustment of Projections

abstract:
  Beam hardening (BH) is one of the major artifacts that severely reduces the
quality of Computed Tomography (CT) imaging. In a polychromatic X-ray beam,
since low-energy photons are more preferentially absorbed, the attenuation of
the beam is no longer a linear function of the absorber thickness. The existing
BH correction methods either require a given material, which might be
unfeasible in reality, or they require a long computation time. This work aims
to propose a fast and accurate BH correction method that requires no prior
knowledge of the materials and corrects first and higher-order BH artifacts. In
the first step, a wide sweep of the material is performed based on an
experimentally measured look-up table to obtain the closest estimate of the
material. Then the non-linearity effect of the BH is corrected by adding the
difference between the estimated monochromatic and the polychromatic simulated
projections of the segmented image. The estimated monochromatic projection is
simulated by selecting the energy from the polychromatic spectrum which
produces the lowest mean square error (MSE) with the acquired projection from
the scanner. The polychromatic projection is estimated by minimizing the
difference between the acquired projection and the weighted sum of the
simulated polychromatic projections using different spectra of different
filtration. To evaluate the proposed BH correction method, we have conducted
extensive experiments on the real-world CT data. Compared to the
state-of-the-art empirical BH correction method, the experiments show that the
proposed method can highly reduce the BH artifacts without prior knowledge of
the materials.


title:
Unsupervised Domain Adaptation across FMCW Radar Configurations Using
  Margin Disparity Discrepancy

abstract:
  Commercial radar sensing is gaining relevance and machine learning algorithms
constitute one of the key components that are enabling the spread of this radio
technology into areas like surveillance or healthcare. However, radar datasets
are still scarce and generalization cannot be yet achieved for all radar
systems, environment conditions or design parameters. A certain degree of fine
tuning is, therefore, usually required to deploy machine-learning-enabled radar
applications. In this work, we consider the problem of unsupervised domain
adaptation across radar configurations in the context of deep-learning human
activity classification using frequency-modulated continuous-wave. For that, we
focus on the theory-inspired technique of Margin Disparity Discrepancy, which
has already been proved successful in the area of computer vision. Our
experiments extend this technique to radar data, achieving a comparable
accuracy to fewshot supervised approaches for the same classification problem.


title:
A New Global Divergence Free and Pressure-Robust HDG Method for
  Tangential Boundary Control of Stokes Equations

abstract:
  In [ESAIM: M2AN, 54(2020), 2229-2264], we proposed an HDG method to
approximate the solution of a tangential boundary control problem for the
Stokes equations and obtained an optimal convergence rate for the optimal
control {that reflects its global regularity}. However, the error estimates
depend on the pressure, and the velocity is not divergence free. The importance
of pressure-robust numerical methods for fluids was addressed by John et al.
[SIAM Review, 59(2017), 492-544]. In this work, we devise a new HDG method to
approximate the solution of the Stokes tangential boundary control problem; the
HDG method is also of independent interest for solving the Stokes equations.
This scheme yields a $\mathbf{H}(\mathrm{div})$ conforming, globally divergence
free, and pressure-robust solution. To the best of our knowledge, this is the
first time such a numerical scheme has been obtained for an optimal boundary
control problem for the Stokes equations. We also provide numerical experiments
to show the performance of the new HDG method and the advantage over the non
pressure-robust scheme.


title:
Two conjectures for Macdonald polynomials: The stretching symmetry and
  Haglund's conjecture

abstract:
  We propose a conjecture which is a symmetry relation for the modified
Macdonald polynomials of stretched partitions. We prove the conjecture for
one-column or one-row shape in two ways: using the LLT expansion of the
modified Macdonald polynomials and using the Garsia--Haiman module. In
addition, we prove a conjecture of Haglund concerning multi-$t$-Macdonald
polynomials of two rows.


title: Yield-stress transition in suspensions of deformable droplets

abstract:
  Yield-stress materials, which require a sufficiently large forcing to flow,
are currently ill-understood theoretically. To gain insight into their yielding
transition, here we study numerically the rheology of a suspension of
deformable droplets under pressure-driven flow. We show that the suspension
displays yield-stress behaviour, with the droplets remaining motionless when
the applied body-force is below a critical value. In the non-flowing phase,
droplets jam to form an amorphous structure, whereas they order in the flowing
phase. Yielding is linked to a percolation transition in the contacts of
droplet-droplet overlaps, and requires suitable wetting boundary conditions and
strict conservation of the droplet area to exist. Close to the yielding
transition, we find strong oscillations in the droplet motion which closely
resemble those found experimentally in confined colloidal glasses under flow.
We show that even when droplets are static the underlying solvent moves by
permeation, so that the viscosity of the composite system is never truly
infinite, and, as we discuss, its precise value ceases to be a bulk material
property of the system.


title:
Mapping global dynamics of benchmark creation and saturation in
  artificial intelligence

abstract:
  Benchmarks are crucial to measuring and steering progress in artificial
intelligence (AI). However, recent studies raised concerns over the state of AI
benchmarking, reporting issues such as benchmark overfitting, benchmark
saturation and increasing centralization of benchmark dataset creation. To
facilitate monitoring of the health of the AI benchmarking ecosystem, we
introduce methodologies for creating condensed maps of the global dynamics of
benchmark creation and saturation. We curated data for 1688 benchmarks covering
the entire domains of computer vision and natural language processing, and show
that a large fraction of benchmarks quickly trended towards near-saturation,
that many benchmarks fail to find widespread utilization, and that benchmark
performance gains for different AI tasks were prone to unforeseen bursts. We
conclude that future work should focus on large-scale community collaboration
and on mapping benchmark performance gains to real-world utility and impact of
AI.


title: Small Errors Imply Large Instabilities

abstract:
  Numerical Analysts and scientists working in applications often observe that
once they improve their techniques to get a better accuracy, some instability
creeps in through the back door. This paper shows for a large class of
numerical methods that such a Trade-off Principle between error and stability
is unavoidable. The setting is confined to recovery of functions from data, but
it includes solving differential equations by writing such methods as a
recovery of functions under constraints imposed by differential operators and
boundary values. It is shown in particular that Kansa's Unsymmetric Collocation
Method sacrifices accuracy for stability, when compared to symmetric
collocation.


title:
The Role of Permanent and Induced Electrostatic Dipole Moments for
  Schottky Barriers in Janus MXY/Graphene Heterostructures: a First Principles
  Study

abstract:
  The Schottky barrier height ($E_{SBH}$) is a crucial factor in determining
the transport properties of semiconductor materials as it directly regulates
the carrier mobility in opto-electronics devices. In principle, van der Waals
(vdW) Janus heterostructures offer an appealing avenue to controlling the ESBH.
However, the underlying atomistic mechanisms are far from understood
conclusively, which prompts for further research in the topic. To this end,
here, we carry out an extensive first principles study of the electronic
properties and $E_{SBH}$ of several vdW Janus MXY/Graphene (M=Mo, W; X, Y=S,
Se, Te) heterostructures. The results of the simulations show that by changing
the composition and geometry of the heterostructure's interface, it is possible
to control its electrical contact, thence electron transport properties, from
Ohmic to Schottky with nearly one order of magnitude variations in the
$E_{SBH}$. Detailed analysis of the simulations enables rationalization of this
highly attractive property on the basis of the interplay between the permanent
dipole moment of the Janus MXY sheet and the induced one due to interfacial
charge redistribution at the MXY/Gr interface. Such an interplay is shown to be
highly effective in altering the electrostatic potential difference across the
vdW Janus heterostructure, determining its ESBH, thence Schottky (Ohmic)
contact type. These computational findings contribute guidelines to control
electrical contacts in Janus heterostructures towards rational design of
electrical contacts in nanoscale devices.


title: Practical Mission Planning for Optimized UAV-Sensor Wireless Recharging

abstract:
  Recharging Internet of Things devices using autonomous robots is an
attractive maintenance solution. Ensuring efficient and reliable performance of
autonomous power delivery vehicles is very challenging in dynamic environments.
Our work considers a hybrid Travelling Salesman Problem and Orienteering
Problem scenario where the optimization objective is to jointly minimize
discharged energy of the power delivery vehicle and maximize recharged energy
of devices. This is decomposed as an NP-hard nonconvex optimization and
nonlinear integer programming problem. Many studies have demonstrated
satisfactory performance of heuristic algorithms' ability to solve specific
routing problems, however very few studies explore online updating (i.e.,
mission re-planning `on the fly') for such hybrid scenarios. In this paper, we
present a novel lightweight and reliable mission planner that solves the
problem by combining offline search and online reevaluation. We propose Rapid
Online Metaheuristic-based Planner, ROMP, a multi-objective offline and online
mission planner that can incorporate real-time state information from the power
delivery vehicle and its local environment to deliver reliable, up-to-date and
near-optimal mission planning. We supplement Guided Local Search (via Google
OR-Tools) with a Black Hole-inspired algorithm. Our results show that the
proposed solver improves the solution quality offered by Guided Local Search in
most of the cases tested. We also demonstrate latency performance improvements
by applying a parallelization strategy.


title: Beyond no-go theorem' Weyl phonons

abstract:
  By using \emph{ab initio} calculations and symmetry analysis, we define a new
class of Weyl phonons, i.e., isolated Weyl phonons (IWPs), which are
characterized by Chern number $\pm$2 or $\pm$4 in their acoustic phononic
spectra and protected by the time inversion symmetry and point group
symmetries. More importantly, their particular topological feature make them
circumvent from the no-go theorem. Some high-symmetry points, behaving as
isolated Weyl points in the space groups (SGs) of the related phononic systems,
tend to form IWPs. As enumerated in Table I, the IWPs are located at the center
of three-dimensional Brillouin zone (BZ), and protected by the time-reversal
symmetry ($\cal T$) and the corresponding point group symmetries. Moreover, a
realistic chiral crystal material example of K$_2$Mg$_2$O$_3$ in SG 96, a
monopole IWP with Chern number -2 is found at the high-symmetry point $\Gamma$,
and in another material example of Nb$_3$Al$_2$N in SG 213, a monopole IWP with
Chern number +4 is confirmed at the point $\Gamma$. It is interesting that that
IWPs can not form the surface arcs in the surface BZ, which has not been
reported in the phononic systems to present. Our theoretical results not only
uncover a new class of Weyl phonons (IWPs), but also put forwards an effective
way to search the IWPs in spinless systems.


title:
On the rigidity of the Sasakian structure and characterization of
  cosymplectic manifolds

abstract:
  We introduce new metric structures on a smooth manifold (called "weak"
structures) that generalize the almost contact, Sasakian, cosymplectic, etc.
metric structures $(\varphi,\xi,\eta,g)$ and allow us to take a fresh look at
the classical theory. We demonstrate this statement by generalizing several
well-known results. We prove that any Sasakian structure is rigid, i.e., our
weak Sasakian structure is homothetically equivalent to a Sasakian structure.
We show that a weak almost contact structure with parallel tensor $\varphi$ is
a weak cosymplectic structure and give an example of such a structure on the
product of manifolds. We find conditions for a vector field to be a weak
contact infinitesimal transformation.


title:
Practical underwater quantum key distribution based on decoy-state BB84
  protocol

abstract:
  Polarization encoding quantum key distribution has been proven to be a
reliable method to build a secure communication system. It has already been
used in inter-city fiber channel and near-earth atmosphere channel, leaving
underwater channel the last barrier to conquer. Here we demonstrate a
decoy-state BB84 quantum key distribution system over a water channel with a
compact system design for future experiments in the ocean. In the system, a
multiple-intensity modulated laser module is designed to produce the light
pulses of quantum states, including signal state, decoy state and vacuum state.
The classical communication and synchronization are realized by wireless
optical transmission. Multiple filtering techniques and wavelength division
multiplexing are further used to avoid crosstalk of different light. We test
the performance of the system and obtain a final key rate of 245.6 bps with an
average QBER of 1.91% over a 2.4m water channel, in which the channel
attenuation is 16.35dB. Numerical simulation shows that the system can tolerate
up to 21.7dB total channel loss and can still generate secure keys in 277.9m
Jelov type 1 ocean channel.


title:
Interplay between Charge-Density-Wave, Superconductivity, and
  Ferromagnetism in CuIr2-xCrxTe4 Chalcogenides

abstract:
  We report the crystal structure, charge-density-wave (CDW), superconductivity
(SC), and ferromagnetism (FM) in CuIr2-xCrxTe4 chalcogenides. Powder x-ray
diffraction (PXRD) results reveal that CuIr2-xCrxTe4 series are distinguished
between two structural types and three different regions: (i) layered trigonal
structure region, (ii) mixed phase regions, (iii) spinel structure region.
Besides, Cr substitution for Ir site results in rich physical properties
including the collapse of CDW, the formation of dome-shaped like SC, and the
emergence of magnetism. Cr doping slightly elevates the superconducting
critical temperature (Tsc) to its highest Tsc = 2.9 K around x = 0.06. As x
increases from 0.3 to 0.4, the ferromagnetic Curie temperature (Tc) increases
from 175 to 260 K. However, the Tc remains unchanged in the spinel range from
1.9 to 2. This finding provides a comprehensive material platform for
investigating the interplay between CDW, SC, and FM multipartite quantum
states.


title: Domain Generalization using Pretrained Models without Fine-tuning

abstract:
  Fine-tuning pretrained models is a common practice in domain generalization
(DG) tasks. However, fine-tuning is usually computationally expensive due to
the ever-growing size of pretrained models. More importantly, it may cause
over-fitting on source domain and compromise their generalization ability as
shown in recent works. Generally, pretrained models possess some level of
generalization ability and can achieve decent performance regarding specific
domains and samples. However, the generalization performance of pretrained
models could vary significantly over different test domains even samples, which
raises challenges for us to best leverage pretrained models in DG tasks. In
this paper, we propose a novel domain generalization paradigm to better
leverage various pretrained models, named specialized ensemble learning for
domain generalization (SEDGE). It first trains a linear label space adapter
upon fixed pretrained models, which transforms the outputs of the pretrained
model to the label space of the target domain. Then, an ensemble network aware
of model specialty is proposed to dynamically dispatch proper pretrained models
to predict each test sample. Experimental studies on several benchmarks show
that SEDGE achieves significant performance improvements comparing to strong
baselines including state-of-the-art method in DG tasks and reduces the
trainable parameters by ~99% and the training time by ~99.5%.


title:
A Castelnuovo-Mumford regularity bound for threefolds with rational
  singularities

abstract:
  The purpose of this paper is to establish a Castelnuovo-Mumford regularity
bound for threefolds with mild singularities. Let $X$ be a non-degenerate
normal projective threefold in $\mathbb{P}^r$ of degree $d$ and codimension
$e$. We prove that if $X$ has rational singularities, then $\text{reg}(X) \leq
d-e+2$. Our bound is very close to a sharp bound conjectured by Eisenbud-Goto.
When $e=2$ and $X$ has Cohen-Macaulay Du Bois singularities, we obtain the
conjectured bound $\text{reg}(X) \leq d-1$, and we also classify the extremal
cases. To achieve these results, we bound the regularity of fibers of a generic
projection of $X$ by using Loewy length, and also bound the dimension of the
varieties swept out by secant lines through the singular locus of $X$.


title: Factor-augmented model for functional data

abstract:
  We propose modeling raw functional data as a mixture of a smooth function and
a high-dimensional factor component. The conventional approach to retrieving
the smooth function from the raw data is through various smoothing techniques.
However, the smoothing model is inadequate to recover the smooth curve or
capture the data variation in some situations. These include cases where there
is a large amount of measurement error, the smoothing basis functions are
incorrectly identified, or the step jumps in the functional mean levels are
neglected. A factor-augmented smoothing model is proposed to address these
challenges, and an iterative numerical estimation approach is implemented in
practice. Including the factor model component in the proposed method solves
the aforementioned problems since a few common factors often drive the
variation that cannot be captured by the smoothing model. Asymptotic theorems
are also established to demonstrate the effects of including factor structures
on the smoothing results. Specifically, we show that the smoothing coefficients
projected on the complement space of the factor loading matrix are
asymptotically normal. As a byproduct of independent interest, an estimator for
the population covariance matrix of the raw data is presented based on the
proposed model. Extensive simulation studies illustrate that these factor
adjustments are essential in improving estimation accuracy and avoiding the
curse of dimensionality. The superiority of our model is also shown in modeling
Australian temperature data.


title:
Modified Poisson-Boltzmann theory for polyelectrolytes in monovalent
  salt solutions with finite-size ions

abstract:
  We present a soft-potential-enhanced Poisson-Boltzmann (SPB) theory to
efficiently capture ion distributions and electrostatic potential around
rodlike charged macromolecules. The SPB model is calibrated with a
coarse-grained particle-based model for polyelectrolytes (PEs) in monovalent
salt solutions as well as compared to a full atomistic molecular dynamics
simulations with explicit solvent. We demonstrate that our modification enables
the SPB theory to accurately predict monovalent ion distributions around a
rodlike PE in a wide range of ion and charge distribution conditions in the
weak-coupling regime. These include excess salt concentrations up to 1 M, and
ion sizes ranging from small ions, such as Na or Cl, to softer and larger ions
with size comparable to the PE diameter. The work provides a simple way to
implement an enhancement that effectively captures the influence of ion size
and species into the PB theory in the context of PEs in aqueous salt solutions.


title:
Data-driven detector signal characterization with constrained bottleneck
  autoencoders

abstract:
  A common technique in high energy physics is to characterize the response of
a detector by means of models tunned to data which build parametric maps from
the physical parameters of the system to the expected signal of the detector.
When the underlying model is unknown it is difficult to apply this method, and
often, simplifying assumptions are made introducing modeling errors. In this
article, using a waveform toy model we present how deep learning in the form of
constrained bottleneck autoencoders can be used to learn the underlying unknown
detector response model directly from data. The results show that excellent
performance results can be achieved even when the signals are significantly
affected by random noise. The trained algorithm can be used simultaneously to
perform estimations on the physical parameters of the model, simulate the
detector response with high fidelity and to denoise detector signals.


title:
Representation, learning, and planning algorithms for geometric task and
  motion planning

abstract:
  We present a framework for learning to guide geometric task and motion
planning (GTAMP). GTAMP is a subclass of task and motion planning in which the
goal is to move multiple objects to target regions among movable obstacles. A
standard graph search algorithm is not directly applicable, because GTAMP
problems involve hybrid search spaces and expensive action feasibility checks.
To handle this, we introduce a novel planner that extends basic heuristic
search with random sampling and a heuristic function that prioritizes
feasibility checking on promising state action pairs. The main drawback of such
pure planners is that they lack the ability to learn from planning experience
to improve their efficiency. We propose two learning algorithms to address
this. The first is an algorithm for learning a rank function that guides the
discrete task level search, and the second is an algorithm for learning a
sampler that guides the continuous motionlevel search. We propose design
principles for designing data efficient algorithms for learning from planning
experience and representations for effective generalization. We evaluate our
framework in challenging GTAMP problems, and show that we can improve both
planning and data efficiency


title:
Attention-effective multiple instance learning on weakly stem cell
  colony segmentation

abstract:
  The detection of induced pluripotent stem cell (iPSC) colonies often needs
the precise extraction of the colony features. However, existing computerized
systems relied on segmentation of contours by preprocessing for classifying the
colony conditions were task-extensive. To maximize the efficiency in
categorizing colony conditions, we propose a multiple instance learning (MIL)
in weakly supervised settings. It is designed in a single model to produce weak
segmentation and classification of colonies without using finely labeled
samples. As a single model, we employ a U-net-like convolution neural network
(CNN) to train on binary image-level labels for MIL colonies classification.
Furthermore, to specify the object of interest we used a simple post-processing
method. The proposed approach is compared over conventional methods using
five-fold cross-validation and receiver operating characteristic (ROC) curve.
The maximum accuracy of the MIL-net is 95%, which is 15 % higher than the
conventional methods. Furthermore, the ability to interpret the location of the
iPSC colonies based on the image level label without using a pixel-wise ground
truth image is more appealing and cost-effective in colony condition
recognition.


title:
Practical No-box Adversarial Attacks with Training-free Hybrid Image
  Transformation

abstract:
  In recent years, the adversarial vulnerability of deep neural networks (DNNs)
has raised increasing attention. Among all the threat models, no-box attacks
are the most practical but extremely challenging since they neither rely on any
knowledge of the target model or similar substitute model, nor access the
dataset for training a new substitute model. Although a recent method has
attempted such an attack in a loose sense, its performance is not good enough
and computational overhead of training is expensive. In this paper, we move a
step forward and show the existence of a \textbf{training-free} adversarial
perturbation under the no-box threat model, which can be successfully used to
attack different DNNs in real-time. Motivated by our observation that
high-frequency component (HFC) domains in low-level features and plays a
crucial role in classification, we attack an image mainly by manipulating its
frequency components. Specifically, the perturbation is manipulated by
suppression of the original HFC and adding of noisy HFC. We empirically and
experimentally analyze the requirements of effective noisy HFC and show that it
should be regionally homogeneous, repeating and dense. Extensive experiments on
the ImageNet dataset demonstrate the effectiveness of our proposed no-box
method. It attacks ten well-known models with a success rate of
\textbf{98.13\%} on average, which outperforms state-of-the-art no-box attacks
by \textbf{29.39\%}. Furthermore, our method is even competitive to mainstream
transfer-based black-box attacks.


title: Modular Probabilistic Models via Algebraic Effects

abstract:
  Probabilistic programming languages (PPLs) allow programmers to construct
statistical models and then simulate data or perform inference over them. Many
PPLs restrict models to a particular instance of simulation or inference,
limiting their reusability. In other PPLs, models are not readily composable.
Using Haskell as the host language, we present an embedded domain specific
language based on algebraic effects, where probabilistic models are modular,
first-class, and reusable for both simulation and inference. We also
demonstrate how simulation and inference can be expressed naturally as
composable program transformations using algebraic effect handlers.


title:
Application of Lie Group-based Neural Network Method to Nonlinear
  Dynamical Systems

abstract:
  In this paper, a Lie group-based neural network method is proposed for
solving initial value problems of non linear dynamics. Due to its single-layer
structure (MLP), the approach is substantially cheaper than the multilayer
perceptron method used in literature. The higher performance ability of the
method is demonstrated by several examples.


title: The AstroSat Observatory

abstract:
  AstroSat is India's first Ultra-violet (UV) and X-ray astronomy observatory
in space. The satellite was launched by the Indian Space Research Organisation
on a Polar Satellite Launch Vehicle on 28 September 2015 from Sriharikota Range
north of Chennai on the eastern coast of India. AstroSat carries five
scientific instruments and one auxiliary instrument. Four of these consist of
co-aligned telescopes and detectors mounted on a common deck of the satellite
to observe stars and galaxies simultaneously in the near- and far-UV
wavelengths and a broad range of X-ray energies (0.3 to 80 keV). The fifth
instrument consists of three X-ray detectors and is mounted on a rotating
platform on a side that is oriented 90 degrees with respect to the other
instruments to scan the sky for X-ray transients. An auxiliary instrument
monitors the charged particle environment in the path of the satellite.


title: Optimal convergence rates of totally asynchronous optimization

abstract:
  Asynchronous optimization algorithms are at the core of modern machine
learning and resource allocation systems. However, most convergence results
consider bounded information delays and several important algorithms lack
guarantees when they operate under total asynchrony. In this paper, we derive
explicit convergence rates for the proximal incremental aggregated gradient
(PIAG) and the asynchronous block-coordinate descent (Async-BCD) methods under
a specific model of total asynchrony, and show that the derived rates are
order-optimal. The convergence bounds provide an insightful understanding of
how the growth rate of the delays deteriorates the convergence times of the
algorithms. Our theoretical findings are demonstrated by a numerical example.


title:
Differential Chaos Shift Keying-based Wireless Power Transfer over a
  Frequency Selective Channel

abstract:
  This paper studies the performance of a differential chaos shift keying
(DCSK)-based wireless power transfer (WPT) setup in a frequency selective
scenario. Particularly, by taking into account the nonlinearities of the energy
harvesting (EH) process and a generalized frequency selective Nakagami-m fading
channel, we derive closed-form analytical expressions for the harvested energy
in terms of the transmitted waveform and channel parameters. A simplified
closed-form expression for the harvested energy is also obtained for a
scenario, where the delay spread is negligible in comparison to the transmit
symbol duration. Nontrivial design insights are provided, where it is shown how
the power delay profile of the channel as well as the parameters of the
transmitted waveform affect the EH performance. Our results show that a
frequency selective channel is comparatively more beneficial for WPT compared
to a flat fading scenario. However, a significant delay spread negatively
impacts the energy transfer.


title:
Object-Based Visual Camera Pose Estimation From Ellipsoidal Model and
  3D-Aware Ellipse Prediction

abstract:
  In this paper, we propose a method for initial camera pose estimation from
just a single image which is robust to viewing conditions and does not require
a detailed model of the scene. This method meets the growing need of easy
deployment of robotics or augmented reality applications in any environments,
especially those for which no accurate 3D model nor huge amount of ground truth
data are available. It exploits the ability of deep learning techniques to
reliably detect objects regardless of viewing conditions. Previous works have
also shown that abstracting the geometry of a scene of objects by an ellipsoid
cloud allows to compute the camera pose accurately enough for various
application needs. Though promising, these approaches use the ellipses fitted
to the detection bounding boxes as an approximation of the imaged objects. In
this paper, we go one step further and propose a learning-based method which
detects improved elliptic approximations of objects which are coherent with the
3D ellipsoids in terms of perspective projection. Experiments prove that the
accuracy of the computed pose significantly increases thanks to our method.
This is achieved with very little effort in terms of training data acquisition
- a few hundred calibrated images of which only three need manual object
annotation. Code and models are released at
https://gitlab.inria.fr/tangram/3d-aware-ellipses-for-visual-localization


title:
Uni4Eye: Unified 2D and 3D Self-supervised Pre-training via Masked Image
  Modeling Transformer for Ophthalmic Image Classification

abstract:
  A large-scale labeled dataset is a key factor for the success of supervised
deep learning in computer vision. However, a limited number of annotated data
is very common, especially in ophthalmic image analysis, since manual
annotation is time-consuming and labor-intensive. Self-supervised learning
(SSL) methods bring huge opportunities for better utilizing unlabeled data, as
they do not need massive annotations. With an attempt to use as many as
possible unlabeled ophthalmic images, it is necessary to break the dimension
barrier, simultaneously making use of both 2D and 3D images. In this paper, we
propose a universal self-supervised Transformer framework, named Uni4Eye, to
discover the inherent image property and capture domain-specific feature
embedding in ophthalmic images. Uni4Eye can serve as a global feature
extractor, which builds its basis on a Masked Image Modeling task with a Vision
Transformer (ViT) architecture. We employ a Unified Patch Embedding module to
replace the origin patch embedding module in ViT for jointly processing both 2D
and 3D input images. Besides, we design a dual-branch multitask decoder module
to simultaneously perform two reconstruction tasks on the input image and its
gradient map, delivering discriminative representations for better convergence.
We evaluate the performance of our pre-trained Uni4Eye encoder by fine-tuning
it on six downstream ophthalmic image classification tasks. The superiority of
Uni4Eye is successfully established through comparisons to other
state-of-the-art SSL pre-training methods.


title: Algebras of Generalized Singular Integral Operators with Cauchy kernel

abstract:
  For bounded Lebesgue measurable functions $f,g,\phi$ and $\psi$ on the unit
circle, $P_{+}fP_{+}+P_{-}gP_{+} +P_{+}\phi P_{-}+P_{-}\psi P_{-}$ is called a
generalized singular integral operator (GSIO) on $L^{2}(\mathbb{T})$, where
$P_{+}$ is the Riesz projection, $P_{-}=I-P_{+}.$ In this paper, we relate
GSIOs to a number of operators, including Cauchy singular integral operator,
(dual) truncated Toeplitz operator, Foguel-Hankel operator, multiplication
operator, Toeplitz plus Hankel operator etc. We establish the short exact
sequences associated of the $C^{*}-$algebras generated by GSIOs with bounded or
quasi-continuous symbols. As a consequence we obtain the spectra of various
classes of GSIOs, the spectral inclusion theorem and comput the Fredholm index
of GSIOs. Moreover, we gave the necessary and sufficient conditions for
invertibility(Fredholmness) of GSIOs via Winer-Hopf factorization.


title:
PALI-NLP at SemEval-2022 Task 4: Discriminative Fine-tuning of Deep
  Transformers for Patronizing and Condescending Language Detection

abstract:
  Patronizing and condescending language (PCL) has a large harmful impact and
is difficult to detect, both for human judges and existing NLP systems. At
SemEval-2022 Task 4, we propose a novel Transformer-based model and its
ensembles to accurately understand such language context for PCL detection. To
facilitate comprehension of the subtle and subjective nature of PCL, two
fine-tuning strategies are applied to capture discriminative features from
diverse linguistic behaviour and categorical distribution. The system achieves
remarkable results on the official ranking, namely 1st in Subtask 1 and 5th in
Subtask 2. Extensive experiments on the task demonstrate the effectiveness of
our system and its strategies.


title: Fragmentation analysis of a bar with the Lip-field approach

abstract:
  The Lip-field approach is a new way to regularize softening material models.
It has already been tested in 1D quasistatic and 2D quasistatic: this paper
extends it to 1D dynamics, on the challenging problem of dynamic fragmentation.
The Lip-field approach formulates the mechanical problem to be solved as an
optimization problem, where the incremental potential to be minimized is the
non-regularized one. Spurious localization is prevented by imposing a Lipschitz
constraint on the damage field. The displacement and damage field at each time
step are obtained by a staggered algorithm, that is the displacement field is
computed for a fixed damage field, then the damage field is computed for a
fixed displacement field. Indeed, these two problems are convex, which is not
the case of the global problem where the displacement and damage fields are
sought at the same time. The incremental potential is obtained by equivalence
with a cohesive zone model, which makes material parameters calibration simple.
A non-regularized local damage equivalent to a cohesive zone model is also
proposed. It is used as a reference for the Lip-field approach, without the
need to implement displacement jumps.
  These approaches are applied to the brittle fragmentation of a 1D bar with
randomly perturbed material properties to accelerate spatial convergence. Both
explicit and implicit dynamic implementations are compared. Favorable
comparison to several analytical, numerical and experimental references serves
to validate the modeling approach.


title:
Electric dipole moments due to nuclear Schiff moment interactions: A
  reassessment of the atoms $^{129}$Xe, $^{199}$Hg, and the molecule
  $^{205}$TlF

abstract:
  We present relativistic many-body calculations of atomic and molecular
Schiff-moment interaction constants including interelectron correlation effects
using atomic Gaussian basis sets specifically optimized for the Schiff
interaction. Our present best results employing a Gaussian nuclear density
function are $\alpha_{\text{SM}} = (0.364 \pm 0.025) \times 10^{-17} \frac{e
\text{cm}}{e \text{fm}^3}$ for atomic $^{129}$Xe, $\alpha_{\text{SM}} = (-2.40
\pm 0.24) \times 10^{-17} \frac{e \text{cm}}{e \text{fm}^3}$ for atomic
$^{199}$Hg, and $W_{\text{SM}} = (39967\pm 3600)$ a.u. for the thallium nucleus
in the molecule $^{205}$TlF. We discuss agreements and discrepancies between
our present results and those from earlier calculations on the atoms $^{129}$Xe
and $^{199}$Hg. Using the most recent measurements of ${\cal{P,T}}$-odd
electric dipole moments and the present interaction constants reliable upper
bounds on the Schiff moments of the $^{199}$Hg and $^{205}$Tl nuclei are
determined in the context of a single-source assumption.


title:
Efficient and feasible inference for high-dimensional normal copula
  regression models

abstract:
  The composite likelihood (CL) is amongst the computational methods used for
the estimation of high-dimensional multivariate normal (MVN) copula models with
discrete responses. Its computational advantage, as a surrogate likelihood
method, is that is based on the independence likelihood for the univariate
regression and non-regression parameters and pairwise likelihood for the
correlation parameters, but the efficiency of estimating the univariate
regression and non-regression parameters can be low. For a high-dimensional
discrete response, we propose weighted versions of the composite likelihood
estimating equations and an iterative approach to determine good weight
matrices. The general methodology is applied to the MVN copula with univariate
ordinal regressions as the marginals. Efficiency calculations show that our
method is nearly as efficient as the maximum likelihood for fully specified MVN
copula models. Illustrations include simulations and real data applications
regarding longitudinal (low-dimensional) and time (high-dimensional) series
ordinal response data with covariates and it is shown that there is a
substantial gain in efficiency via the weighted CL method.


title: Proof of a conjecture of Batyrev and Juny on Gorenstein polytopes

abstract:
  A $d$-dimensional lattice polytope $P$ is Gorenstein if it has a multiple $r
P$ that is a reflexive polytope up to translation by a lattice vector. The
difference $d+1-r$ is called the degree of $P$. We show that a Gorenstein
polytope is a lattice pyramid if its dimension is at least three times its
degree. This was previously conjectured by Batyrev and Juny. We also present a
refined conjecture and prove it for IDP Gorenstein polytopes.


title: Tropical approximation to finish time of activity networks

abstract:
  We breakdown complex projects into activities and their logical dependencies.
We estimate the project finish time based on the activity durations and
relations. However, adverse events trigger delay cascades shifting the finish
time. Here I derive a tropical algebraic equation for the finish time of
activity networks, encapsulating the principle of linear superposition of
exogenous perturbations in the tropical sense. From the tropical algebraic
equation I derive the finish time distribution with explicit reference to the
distribution of exogenous delays and the network topology and geometry.


title:
Effective-range expansion of the $T_{cc}^+$ state at the complex
  $D^{*+}D^0$ threshold

abstract:
  Evaluation of the effective-range parameters for the $T_{cc}^+$ state in the
LHCb model is examined. The finite width of $D^*$ leads to a shift of the
expansion point into the complex plane to match analytical properties of the
expanded amplitude. We perform an analytic continuation of the three-body
scattering amplitude to the complex plane in a vicinity of the branch point and
develop a robust procedure for computation of the expansion coefficients. The
results yield a nearly-real scattering length, and two contributions to the the
effective range which have not been accounted before.


title:
Controllable Evaluation and Generation of Physical Adversarial Patch on
  Face Recognition

abstract:
  Recent studies have revealed the vulnerability of face recognition models
against physical adversarial patches, which raises security concerns about the
deployed face recognition systems. However, it is still challenging to ensure
the reproducibility for most attack algorithms under complex physical
conditions, which leads to the lack of a systematic evaluation of the existing
methods. It is therefore imperative to develop a framework that can enable a
comprehensive evaluation of the vulnerability of face recognition in the
physical world. To this end, we propose to simulate the complex transformations
of faces in the physical world via 3D-face modeling, which serves as a digital
counterpart of physical faces. The generic framework allows us to control
different face variations and physical conditions to conduct reproducible
evaluations comprehensively. With this digital simulator, we further propose a
Face3DAdv method considering the 3D face transformations and realistic physical
variations. Extensive experiments validate that Face3DAdv can significantly
improve the effectiveness of diverse physically realizable adversarial patches
in both simulated and physical environments, against various white-box and
black-box face recognition models.


title:
The drastic effect of the impurity scattering on the electronic and
  superconducting properties of Cu-doped FeSe

abstract:
  Non-magnetic impurities in iron-based superconductors can provide an
important tool to understand the pair symmetry and they can influence
significantly the transport and the superconducting behaviour. Here, we present
a study of the role of strong impurity potential in the Fe plane, induced by Cu
substitution, on the electronic and superconducting properties of single
crystals of FeSe. The addition of Cu quickly suppresses both the nematic and
superconducting states, and increases the residual resistivity due to enhanced
impurity scattering. Using magnetotransport data up to 35 T for a small amount
of Cu impurity, we detect a significant reduction in the mobility of the charge
carriers by a factor of ~3. While the electronic conduction is strongly
disrupted by Cu substitution, we identify additional signatures of anisotropic
scattering which manifest in linear resistivity at low temperatures and
$H^{1.6}$ dependence of magnetoresistance. The suppression of superconductivity
by Cu substitution is consistent with a sign-changing $s_{\pm}$ order
parameter. Additionally, in the presence of compressive strain, the
superconductivity is enhanced, similar to FeSe.


title: Vector-spread monomial ideals and Eliahou-Kervaire type resolutions

abstract:
  We introduce the class of vector-spread monomial ideals. This notion
generalizes that of $t$-spread ideals introduced by Ene, Herzog and Qureshi. In
particular, we focus on vector-spread strongly stable ideals, we compute their
Koszul cycles and describe their minimal free resolution. As a consequence the
graded Betti numbers and the Poincar\'e series are determined. Finally, we
consider a generalization of algebraic shifting theory for such a class of
ideals.


title:
Millisecond Time-Scale Measurements of Heat Transfer to an Open Bath of
  He II

abstract:
  We explore steady state and transient heat transfer from a narrow,
rectangular stainless steel heater strip cooled from one side by an open bath
of He~II. Setup validation is done by fitting the Kapitza heat transfer
expression $Q = a_\mathrm{K} \left( {T_\mathrm{s}}^{n_\mathrm{K}} -
{T_\mathrm{b}}^{n_\mathrm{K}} \right)$ to steady state measurements, finding
fit parameters within the expected range; $a_\mathrm{K}$ = 1316.8$\pm10\%$
$\mathrm{W}\mathrm{m}^{-2}\mathrm{K}^{-1}$, $n_\mathrm{K}$ = 2.528$\pm10\%$. We
find critical heat flux in line with estimates from literature, and the time
between a step in heating and the onset of film boiling follows the expected
$\propto Q^{-4}$ dependence. During the first millisecond after a step in
applied heating power density our measurements show a slower thermal rise time
than that found by a time-dependent one-dimensional model of our setup using
the steady state Kapitza heat transfer expression as the cooling boundary
condition. However, the results compare favourably with transient measurements
in literature. After the first millisecond, agreement between measurement and
model is excellent. We do not find conclusive evidence of an orientation
dependence of the Kapitza heat transfer mechanism, nor heat transfer
differences that can be attributed to local surface variations along the same
heater.


title: Droplet impact on asymmetric hydrophobic microstructures

abstract:
  Textured hydrophobic surfaces that repel liquid droplets unidirectionally are
found in nature such as butterfly wings and ryegrass leaves and are also
essential in technological processes such as self-cleaning and anti-icing.
However, droplet impact on such surfaces is not fully understood. Here, we
study, using a high-speed camera, droplet impact on surfaces with inclined
micropillars. We observed directional rebound at high impact speeds on surfaces
with dense arrays of pillars. We attribute this asymmetry to the difference in
wetting behavior of the structure sidewalls, causing slower retraction of the
contact line in the direction against the inclination compared to with the
inclination. The experimental observations are complemented with numerical
simulations to elucidate the detailed movement of the drops over the pillars.
These insights improve our understanding of droplet impact on hydrophobic
microstructures and may be a useful for designing structured surfaces for
controlling droplet mobility.


title: On the mean projection theorem for determinantal point processes

abstract:
  In this short note, we extend a mean projection theorem for discrete
determinantal point processes to the continuous case, thus strengthening a
known result in random linear algebra due to Ermakov and Zolotukhin. We also
give a new formula for the variance of the exterior power of the random
projection.


title:
A comparison of variational upwinding schemes for geophysical fluids,
  and their application to potential enstrophy conserving discretisations in
  space and time

abstract:
  Methods for upwinding the potential vorticity in a finite element
discretisation of the rotating shallow water equations are studied. These
include the well-known anticipated potential vorticity method (APVM),
streamwise upwind Petrov-Galerkin (SUPG) method, and a recent approach where
the trial functions are evaluated downstream within the reference element. In
all cases the upwinding scheme conserves both potential vorticity and energy,
since the antisymmetric structure of the equations is preserved. The APVM leads
to a symmetric definite correction to the potential enstrophy that is
dissipative and inconsistent, resulting in a turbulent state where the
potential enstrophy is more strongly damped than for the other schemes. While
the SUPG scheme is widely known to be consistent, since it modifies the test
functions only, the downwinded trial function formulation results in the
advection of downwind corrections. Results of the SUPG and downwinded trial
function schemes are very similar in terms of both potential enstrophy
conservation and turbulent spectra. The main difference between these schemes
is in the energy conservation and residual errors. If just two nonlinear
iterations are applied then the energy conservation errors are greatly improved
for the downwinded trial function formulation, reflecting a smaller residual
error than for the SUPG scheme.
  We also present a formulation by which potential enstrophy is exactly
conserved in time. The application of the APVM with exact temporal conservation
of potential enstrophy yields conservation errors that are significantly
smaller than those of the SUPG or downwinded trial function formulations using
a potential enstrophy dissipating time discretisation. Exact conservation of
the potential enstrophy in time allows for stable simulation in the absence of
dissipation, despite the uncontrolled aliasing of grid scale turbulence.


title: Compatible split systems on a multiset

abstract:
  A split system on a multiset $\mathcal M$ is a set of bipartitions of
$\mathcal M$. Such a split system $\mathfrak S$ is compatible if it can be
represented by a tree in such a way that the vertices of the tree are labelled
by the elements in $\mathcal M$, the removal of each edge in the tree yields a
bipartition in $\mathfrak S$ by taking the labels of the two resulting
components, and every bipartition in $\mathfrak S$ can be obtained from the
tree in this way. In this contribution, we present a novel characterization for
compatible split systems, and for split systems admitting a unique tree
representation. In addition, we show that a conjecture on compatibility stated
in 2008 holds for some large classes of split systems.


title:
Unusual phase formation in reactively sputtered La-Co-O perovskite thin
  films

abstract:
  La-based perovskites are a versatile class of materials that are of interest
for solid oxide fuel cells and electrocatalytic water splitting. During
fabrication of composition spread materials libraries of La-Co-based oxide
systems for the discovery of new catalytic materials, an unusual phase
formation phenomenon was observed: instead of the expected continuous
composition gradient, regions with homogeneous composition and single-phase
structure (La2O3 or stoichiometric La-perovskite) form. This phenomenon occurs
during reactive co-sputtering and is dependent on O2-flux and substrate
temperature, investigated from room temperature up to 700 C and is independent
of the used substrate. It can be described as a self-organized growth, where
excess transition metal cannot be incorporated into the growing film and the
forming single-phase regions. It is hypothesized that due to the high
reactivity of La and the significantly low formation energies of La2O3 and
La-perovskites, the reactive sputter deposition of La-based oxide films can
turn, regarding film growth, into a partial CVD-like process which results in
the unusual self-organized growth of single-phase regions. This phenomenon can
be leveraged for the exploration of multinary perovskite thin film libraries,
where the B-site atoms of La-perovskites are systematically substituted.


title: Improved bunching and longitudinal emittance control in an RFQ

abstract:
  A new approach to the longitudinal vane modulation profile in a Radio
Frequency Quadrupole (RFQ) has been discovered that significantly improves
bunching and longitudinal emittance control. This procedure has been
successfully tested in different RFQ models with satisfactory reduction of the
longitudinal emittance; thus, this advanced method can be implemented for any
RFQ. Highlights:
  Advanced longitudinal vane modulation profile that improves bunching and
longitudinal emittance control.
  Vane manipulation to avoid the nonlinear effects of the accelerating field
inside the cell.
  Analysis in the full time-domain. Keywords: Radiofrequency Quadrupoles,
Equipartitioning, Beam bunching.


title: Multitriangulations and tropical Pfaffians

abstract:
  The $k$-associahedron $Ass_k(n)$ is the simplicial complex of
$(k+1)$-crossing-free subgraphs of the complete graph with vertices on a
circle. Its facets are called $k$-triangulations.
  We explore the connection of $Ass_k(n)$ with the Pfaffian variety
$Pf_k(n)\subset {\mathbb K}^{\binom{[n]}2}$ of antisymmetric matrices of rank
$\le 2k$.
  First, we characterize the Gr\"obner cone $Grob_k(n)\subset{\mathbb
R}^{\binom{[n]}2}$ producing as initial ideal of $I(Pf_k(n))$ the
Stanley-Reisner ideal of $Ass_k(n)$ (that is, the monomial ideal generated by
$(k+1)$-crossings).
  We then look at the tropicalization of $Pf_k(n)$ and show that $Ass_k(n)$
embeds naturally as the intersection of $\operatorname{trop}(Pf_k(n))$ and
$Grob_k(n)$, and is contained in the totally positive part
$\operatorname{trop}^+( Pf_k(n))$ of it.
  We show that for $k=1$ and for each triangulation $T$ of the $n$-gon, the
projection of this embedding of $Ass_k(n)$ to the $n-3$ coordinates
corresponding to diagonals in $T$ gives a complete polytopal fan, realizing the
associahedron. This fan is linearly isomorphic to the $\mathbf g$-vector fan of
the cluster algebra of type $A$, shown to be polytopal by Hohlweg, Pilaud and
Stella in (2018).


title:
Systematic bias on parameterized tests of general relativity due to
  neglect of orbital eccentricity

abstract:
  Gravitational-wave observations provide a unique opportunity to test General
Relativity (GR) in the strong-field and highly-dynamical regime of the theory.
Parameterized tests of GR are one well-known approach for quantifying
violations of GR. This approach constrains deviations in the coefficients of
the post-Newtonian (PN) phasing formula, which describes the gravitational-wave
phase evolution of a compact binary as it inspirals. Current bounds from this
test using LIGO/Virgo observations assume that binaries are circularized by the
time they enter the detector frequency band. Here, we investigate the impact of
residual binary eccentricity on the parameterized tests. We study the
systematic biases in the parameter bounds when a phasing based on the circular
orbit assumption is employed for a system that has some small residual
eccentricity. We find that a systematic bias (for example, on the leading
Newtonian deformation parameter) becomes comparable to the statistical errors
for even moderate eccentricities of $\sim 0.04$ at $10$ Hz in LIGO/Virgo band.
This happens at an even lower value of orbital eccentricity ($\sim 0.005$ at
$10$ Hz) in the frequency band of third-generation (3G) detectors like Cosmic
Explorer. These results demonstrate that incorporating physical effects like
eccentricity in waveform models is important for accurately extracting science
results from future detectors.


title:
PRINCE: A Pruned AMP Integrated Deep CNN Method for Efficient Channel
  Estimation of Millimeter-wave and Terahertz Ultra-Massive MIMO Systems

abstract:
  Millimeter-wave (mmWave) and Terahertz (THz)-band communications exploit the
abundant bandwidth to fulfill the increasing data rate demands of 6G wireless
communications. To compensate for the high propagation loss with reduced
hardware costs, ultra-massive multiple-input multiple-output (UM-MIMO) with a
hybrid beamforming structure is a promising technology in the mmWave and THz
bands. However, channel estimation (CE) is challenging for hybrid UM-MIMO
systems, which requires recovering the high-dimensional channels from severely
few channel observations. In this paper, a Pruned Approximate Message Passing
(AMP) Integrated Deep Convolutional-neural-network (DCNN) CE (PRINCE) method is
firstly proposed, which enhances the estimation accuracy of the AMP method by
appending a DCNN network. Moreover, by truncating the insignificant feature
maps in the convolutional layers of the DCNN network, a pruning method
including training with regularization, pruning and refining procedures is
developed to reduce the network scale. Simulation results show that the PRINCE
achieves a good trade-off between the CE accuracy and significantly low
complexity, with normalized-mean-square-error (NMSE) of $-10$ dB at
signal-to-noise-ratio (SNR) as $10$ dB after eliminating $80\%$ feature maps.


title: Leading Isospin Breaking effects in nucleon and $\Delta$ masses

abstract:
  We present a lattice calculation of the leading corrections to the masses of
nucleons and $\Delta$ resonances. These are obtained in QCD+QED at $1$st order
in the Isospin Breaking parameters $\alpha_{EM}$, the electromagnetic coupling,
and $\frac{{\hat{m}}_d - {\hat{m}}_u}{\Lambda_{QCD}}$, coming from the mass
difference between $u$ and $d$ quarks.


title:
LEBP -- Language Expectation & Binding Policy: A Two-Stream Framework
  for Embodied Vision-and-Language Interaction Task Learning Agents

abstract:
  People always desire an embodied agent that can perform a task by
understanding language instruction. Moreover, they also want to monitor and
expect agents to understand commands the way they expected. But, how to build
such an embodied agent is still unclear. Recently, people can explore this
problem with the Vision-and-Language Interaction benchmark ALFRED, which
requires an agent to perform complicated daily household tasks following
natural language instructions in unseen scenes. In this paper, we propose LEBP
-- Language Expectation and Binding Policy Module to tackle the ALFRED. The
LEBP contains a two-stream process: 1) It first conducts a language expectation
module to generate an expectation describing how to perform tasks by
understanding the language instruction. The expectation consists of a sequence
of sub-steps for the task (e.g., Pick an apple). The expectation allows people
to access and check the understanding results of instructions before the agent
takes actual actions, in case the task might go wrong. 2) Then, it uses the
binding policy module to bind sub-steps in expectation to actual actions to
specific scenarios. Actual actions include navigation and object manipulation.
Experimental results suggest our approach achieves comparable performance to
currently published SOTA methods and can avoid large decay from seen scenarios
to unseen scenarios.


title: Speaker Identification Experiments Under Gender De-Identification

abstract:
  The present work is based on the COST Action IC1206 for De-identification in
multimedia content. It was performed to test four algorithms of voice
modifications on a speech gender recognizer to find the degree of modification
of pitch when the speech recognizer have the probability of success equal to
the probability of failure. The purpose of this analysis is to assess the
intensity of the speech tone modification, the quality, the reversibility and
not-reversibility of the changes made. Keywords DeIdentification; Speech
Algorithms


title: Phases and Time-Scales of Ignition and Burning of Live Fuels

abstract:
  Wildland fires impact ecosystems and communities worldwide. Many wildfires
burn in living or a mixture of living and senescent vegetation. Therefore, it
is necessary to understand the burning behavior of living fuels, in contrast to
just dead or dried fuels, to more effectively support fire management
decisions. In this study, the ignition and burning behaviors of needles placed
in convective heat flux were evaluated. The species included longleaf pine
(Pinus palustris), Douglas-fir (Pseudotsuga menziesii), western red cedar
(Thuja plicata), ponderosa pine (Pinus ponderosa), western larch (Larix
occidentalis), pacific yew (Taxus brevifolia), white spruce (Picea glauca), and
sagebrush (Artemisia tridentate). The ignition and burning behaviors were
related to live fuel moisture content (LFMC), pilot flame temperatures, and
convective heat fluxes. The different phases of ignition and burning were
captured using high-speed imaging. In general, four burning phases can be
observed: droplet ejection and burning, a transition phase, flaming combustion,
and smoldering combustion. Ejection and subsequent burning of droplets can
occur prior to sustained flaming ignition only in live fuels. For some species
(e.g., longleaf pine, ponderosa pine, white spruce) droplet ejection and
burning can reduce ignition times relative to dried fuel with lower LFMC. In
general, the transition phase tends to take longer than the flaming and droplet
phases (when these occur). During the transition phase, the fuels are heated
and pyrolysis occurs. Time-scales to ignition and the different phases of
ignition and burning vary more among live fuels than dead and dried fuels. This
conclusion indicates that other parameters, such as chemical composition and
structural morphology of the fuel, can significantly influence the burning of
live fuels.


title: Memory Efficient Continual Learning for Neural Text Classification

abstract:
  Learning text classifiers based on pre-trained language models has become the
standard practice in natural language processing applications. Unfortunately,
training large neural language models, such as transformers, from scratch is
very costly and requires a vast amount of training data, which might not be
available in the application domain of interest. Moreover, in many real-world
scenarios, classes are uncovered as more data is seen, calling for
class-incremental modelling approaches. In this work we devise a method to
perform text classification using pre-trained models on a sequence of
classification tasks provided in sequence. We formalize the problem as a
continual learning problem where the algorithm learns new tasks without
performance degradation on the previous ones and without re-training the model
from scratch. We empirically demonstrate that our method requires significantly
less model parameters compared to other state of the art methods and that it is
significantly faster at inference time. The tight control on the number of
model parameters, and so the memory, is not only improving efficiency. It is
making possible the usage of the algorithm in real-world applications where
deploying a solution with a constantly increasing memory consumption is just
unrealistic. While our method suffers little forgetting, it retains a
predictive performance on-par with state of the art but less memory efficient
methods.


title:
Fluctuation-driven plasticity allows for flexible rewiring of neuronal
  assemblies

abstract:
  Synaptic connections in neuronal circuits are modulated by pre- and
post-synaptic spiking activity. Heuristic models of this process of synaptic
plasticity can provide excellent fits to results from in-vitro experiments in
which pre- and post-synaptic spiking is varied in a controlled fashion.
However, the plasticity rules inferred from fitting such data are inevitably
unstable, in that given constant pre- and post-synaptic activity the synapse
will either fully potentiate or depress. This instability can be held in check
by adding additional mechanisms, such as homeostasis. Here we consider an
alternative scenario in which the plasticity rule itself is stable. When this
is the case, net potentiation or depression only occur when pre- and
post-synaptic activity vary in time, e.g. when driven by time-varying inputs.
We study how the features of such inputs shape the recurrent synaptic
connections in models of neuronal circuits. In the case of oscillatory inputs,
the resulting structure is strongly affected by the phase relationship between
drive to different neurons. In large networks, distributed phases tend to lead
to hierarchical clustering. Our results may be of relevance for understanding
the effect of sensory-driven inputs, which are by nature time-varying, on
synaptic plasticity, and hence on learning and memory.


title:
Electrical Vehicle Fleet Routing Accounting for Dynamic Battery
  Degradation

abstract:
  The increasing uptake of electrical vehicles (EVs) has increased the
awareness of battery degradation costs and how they can be minimized. However,
from a planning perspective it is difficult to integrate battery degradation
models into existing route planning models and to assess how policies that aim
at reducing battery degradation affect route planning costs and degradation
across the fleet. In this paper, a simple transportation vehicle routing
problem (VRP) is formulated as a mixed-integer nonlinear problem (MINLP), with
a modification that allows monitoring the maximum and minimum
depth-of-discharge (DoD) of the entire fleet. This allows us to measure the
battery health degradation during the online optimization process. The results
show that accounting for the impact of different route characteristics on
battery degradation can have an impact on the route planning of the entire
fleet as well as the battery degradation for all vehicles. The latter is
achieved by forcing vehicles to adapt to certain DoD boundaries in the long
term.


title: 3D Dense Face Alignment with Fused Features by Aggregating CNNs and GCNs

abstract:
  In this paper, we propose a novel multi-level aggregation network to regress
the coordinates of the vertices of a 3D face from a single 2D image in an
end-to-end manner. This is achieved by seamlessly combining standard
convolutional neural networks (CNNs) with Graph Convolution Networks (GCNs). By
iteratively and hierarchically fusing the features across different layers and
stages of the CNNs and GCNs, our approach can provide a dense face alignment
and 3D face reconstruction simultaneously for the benefit of direct feature
learning of 3D face mesh. Experiments on several challenging datasets
demonstrate that our method outperforms state-of-the-art approaches on both 2D
and 3D face alignment tasks.


title: Robustness of Aharonov-Bohm cages in quantum walks

abstract:
  It was recently shown that Aharonov-Bohm (AB) cages exist for quantum walks
(QW) on certain tilings -- such as the diamond chain or the dice (or
$\mathcal{T}_3$) lattice -- for a proper choice of coins. In this article, we
probe the robustness of these AB cages to various perturbations. When the cages
are destroyed, we analyze the leakage mechanism and characterize the resulting
dynamics. Quenched disorder typically breaks the cages and leads to an
exponential decay of the wavefunction similar to Anderson localization.
Dynamical disorder or repeated measurements destroy phase coherence and turn
the QW into a classical random walk with diffusive behavior. Combining static
and dynamical disorder in a specific way leads to subdiffusion with an
anomalous exponent controlled by the quenched disorder distribution.
Introducing interaction to a second walker can also break the cages and restore
a ballistic motion for a "molecular" bound-state.


title:
HAIDA: Biometric technological therapy tools for neurorehabilitation of
  Cognitive Impairment

abstract:
  Dementia, and specially Alzheimer s disease (AD) and Mild Cognitive
Impairment (MCI) are one of the most important diseases suffered by elderly
population. Music therapy is one of the most widely used non-pharmacological
treatment in the field of cognitive impairments, given that music influences
their mood, behavior, the decrease of anxiety, as well as facilitating
reminiscence, emotional expressions and movement. In this work we present
HAIDA, a multi-platform support system for Musical Therapy oriented to
cognitive impairment, which includes not only therapy tools but also
non-invasive biometric analysis, speech, activity and hand activity. At this
moment the system is on use and recording the first sets of data.


title: The shear viscosity and self-diffusion in ordinary water

abstract:
  The paper is devoted to a detailed analysis of the two important transport
processes - the kinematic shear viscosity and the self-diffusion - for all
states of liquid water from the triple point to the critical point. Our
approach to the shear viscosity is grounded on friction effects between the two
nearest molecular layers shifting relative to each other. In this relation, the
nature of the shear viscosity in water is fully similar to that in argon. The
contribution, caused by interlayer displacements of molecules, is assumed to be
negligibly small. The behavior of the kinematic shear viscosities of water is
investigated in detail in the two characteristic directions: the vapor-liquid
coexistence curve and isotherms. As for argon, it is assumed that the
self-diffusion in water is formed by the two main contributions, caused by the
molecular transport by nano-scale vortex hydrodynamic modes and collective
intermixing of molecules on molecular scales. The mechanism of the second type
is with good accuracy described by the Einstein formula with a hard-core radius
of a molecule determined from analysis of the shear viscosity of water.


title: Normal and Visibility Estimation of Human Face from a Single Image

abstract:
  Recent work on the intrinsic image of humans starts to consider the
visibility of incident illumination and encodes the light transfer function by
spherical harmonics. In this paper, we show that such a light transfer function
can be further decomposed into visibility and cosine terms related to surface
normal. Such decomposition allows us to recover the surface normal in addition
to visibility. We propose a deep learning-based approach with a reconstruction
loss for training on real-world images. Results show that compared with
previous works, the reconstruction of human face from our method better reveals
the surface normal and shading details especially around regions where
visibility effect is strong.


title:
HADES RV Programme with HARPS-N at TNG XV. Planetary occurrence rates
  around early-M dwarfs

abstract:
  We present the complete Bayesian statistical analysis of the HArps-n red
Dwarf Exoplanet Survey (HADES), which monitored the radial velocities of a
large sample of M dwarfs with HARPS-N at TNG, over the last 6 years. The
targets were selected in a narrow range of spectral types from M0 to M3, $0.3$
M$_\odot < M_\star < 0.71$ M$_\odot$, in order to study the planetary
population around a well-defined class of host stars. We take advantage of
Bayesian statistics to derive an accurate estimate of the detectability
function of the survey. Our analysis also includes the application of Gaussian
Process approach to take into account stellar activity induced radial velocity
variations, and improve the detection limits, around the most-observed and
most-active targets. The Markov chain Monte Carlo and Gaussian process
technique we apply in this analysis has proven very effective in the study of
M-dwarf planetary systems, helping the detection of most of the HADES planets.
From the detectability function we can calculate the occurrence rate of small
mass planets around early-M dwarfs, either taking into account only the 11
already published HADES planets or adding also the 5 new planetary candidates
discovered in this analysis, and compare them with the previous estimates of
planet occurrence around M-dwarf or Solar-type stars: considering only the
confirmed planets, we find the highest frequency for low-mass planets ($1$
M$_\oplus < m_p \sin i < 10$ M$_\oplus$) with periods $10$ d$ < P < 100$ d,
$f_\text{occ} = 85^{+5}_{-19}\%$, while for short-period planets ($1$ d$ < P <
10$ d) we find a frequency of $f_\text{occ} = 10.3^{+8.4}_{-3.3}\%$,
significantly lower than for later-M dwarfs. These results, and their
comparison with other surveys focused on different stellar types, confirms the
central role that stellar mass plays in the formation and evolution of
planetary systems.


title: ArithsGen: Arithmetic Circuit Generator for Hardware Accelerators

abstract:
  Generators of arithmetic circuits can automatically deliver various
implementations of arithmetic circuits that show different tradeoffs between
the key circuit parameters (delay, area, power consumption). However, existing
(freely-)available generators are limited if more complex circuits with a
hierarchical structure and additional architecture optimization are requested.
Furthermore, they support only a few output formats. In order to overcome the
above-mentioned limitations, we developed a new generator of arithmetic
circuits called ArithsGen. ArithsGen can generate specific architectures of
signed and unsigned adders and multipliers using basic building elements such
as wires and gates. Compared to existing generators, the user can, for example,
specify the type of adders used in multipliers. The tool supports various
outputs formats (Verilog, BLIF, C/C++, or integer netlists). ArithsGen was
evaluated in the synthesis and optimization of generic customizable accurate
and approximate adders and multipliers. Furthermore, we used the circuits
generated by ArithsGen as seeds for a tool developed to automatically create
approximate implementations of arithmetic circuits. We show that different
initial circuits (generated by ArithsGen) significantly impact the properties
of these approximate implementations. The tool is available online at
https://github.com/ehw-fit/ariths-gen.


title: Gaussian random fields on non-separable Banach spaces

abstract:
  We study Gaussian random fields on certain Banach spaces and investigate
conditions for their existence. Our results apply inter alia to spaces of Radon
measures and H\"older functions. In the former case, we are able to define
Gaussian white noise on the space of measures directly, avoiding, e.g., an
embedding into a negative-order Sobolev space. In the latter case, we
demonstrate how H\"older regularity of the samples is controlled by that of the
covariance kernel and, thus, show a connection to the Theorem of
Kolmogorov-Chentsov.


title:
Slangvolution: A Causal Analysis of Semantic Change and Frequency
  Dynamics in Slang

abstract:
  Languages are continuously undergoing changes, and the mechanisms that
underlie these changes are still a matter of debate. In this work, we approach
language evolution through the lens of causality in order to model not only how
various distributional factors associate with language change, but how they
causally affect it. In particular, we study slang, which is an informal
language that is typically restricted to a specific group or social setting. We
analyze the semantic change and frequency shift of slang words and compare them
to those of standard, nonslang words. With causal discovery and causal
inference techniques, we measure the effect that word type (slang/nonslang) has
on both semantic change and frequency shift, as well as its relationship to
frequency, polysemy and part of speech. Our analysis provides some new insights
in the study of semantic change, e.g., we show that slang words undergo less
semantic change but tend to have larger frequency shifts over time.


title: Cohen-Macaulay Binomial edge ideals in terms of blocks with whiskers

abstract:
  For a graph $G$, Bolognini et al. have shown $J_{G}$ is strongly unmixed
$\Rightarrow$ $J_{G}$ is Cohen-Macaulay $\Rightarrow$ $G$ is accessible, where
$J_{G}$ denotes the binomial edge ideals of $G$. Accessible and strongly
unmixed properties are purely combinatorial. We give some motivations to focus
only on blocks with whiskers for the characterization of all $G$ with
Cohen-Macaulay $J_{G}$. We show that accessible and strongly unmixed properties
of $G$ depend only on the corresponding properties of its blocks with whiskers
and vice versa. Also, we give an infinite class of graphs whose binomial edge
ideals are Cohen-Macaulay, and from that, we classify all $r$-regular
$r$-connected graphs such that attaching some special whiskers to it, the
binomial edge ideals become Cohen-Macaulay. Finally, we define a new class of
graphs, called \textit{strongly $r$-cut-connected} and prove that the binomial
edge ideal of any strongly $r$-cut-connected accessible graph having at most
three cut vertices is Cohen-Macaulay.


title: The Nehari problem for the Paley--Wiener space of a disc

abstract:
  There is a bounded Hankel operator on the Paley--Wiener space of a disc in
$\mathbb{R}^2$ which does not arise from a bounded symbol.


title:
Multiple intermodal-vectorial four-wave-mixing bands generated by
  selective excitation of orthogonally polarized LP01 and LP11 modes in a
  birefringent fiber

abstract:
  This study investigated the nonlinear frequency conversions between the six
polarization modes of a two-mode birefringent fiber. The aim was to demonstrate
that the selective excitation of different combinations of linearly polarized
spatial modes at the pump wavelength initiates distinct intermodal vector
four-wave mixing processes. In particular, this study shows that exciting two
orthogonally polarized LP01 and LP11 modes can result in simultaneous
generation of up to three pairs of different spatial modes of orthogonal
polarizations at different wavelengths. The role of the phase birefringence of
the spatial modes in the phase-matching of such a four-wave mixing process was
explained. Moreover, the theoretical predictions were verified through
numerical simulations based on coupled nonlinear Schrodinger equations and also
confirmed experimentally in a commercially available birefringent fiber.


title: On White Noise Solutions of mSQG Equations on $\mathbb{R}^{2}$

abstract:
  In this paper, we show existence of white noise solutions for weak
formulations of modified Surface Quasi-Geostrophic (mSQG) equations. Based on
previous results (\cite{FS}) on white noise solutions for mSQG equations on the
torus $\mathbb{T}^{2}$, we show a similar result for the whole space
$\mathbb{R}^{2}$ by letting the volume of the torus go to infinity and applying
compactness methods (Skorokhod's theorem).


title: Direct measurement of the aerotactic response in a bacterial suspension

abstract:
  Aerotaxis is the ability of motile cells to navigate towards oxygen. A key
question is the dependence of the aerotactic velocity with the local oxygen
concentration $c$. Here we combine simultaneous bacteria tracking and local
oxygen concentration measurements using Ruthenium encapsulated in micelles to
characterise the aerotactic response of \emph{Burkholderia contaminans}, a
motile bacterium ubiquitous in the environment and present in living organisms.
In our experiments, an oxygen gradient is produced by the bacterial respiration
in a sealed glass capillary permeable to oxygen at one end, producing a
bacterial band travelling towards the oxygen source. We compute the aerotactic
response $\chi(c)$ both at the population scale, from the drift velocity in the
bacterial band, and at the bacterial scale, from the angular modulation of the
run times. Both methods are consistent with a power-law $\chi \propto c^{-2}$,
in good agreement with existing models based on the biochemistry of bacterial
membrane receptors.


title: Uniform estimates for friable polynomials over finite fields

abstract:
  We establish new estimates for the number of $m$-friable polynomials of
degree $n$ over a finite field $\mathbb{F}_q$, where the main term involves the
number of $m$-friable permutations on $n$ elements. The range of $m$ in our
results is $n \ge m \ge (1+\varepsilon)\log_q n$ and is optimal as these
numbers are not comparable for smaller $m$. For $m \ge 4\log_q n$ the error
term in our estimates decays faster than in all previous works.
  Our estimates imply that the probability that a random polynomial $f_n$ is
$m$-friable is asymptotic to the probability that a random permutation $\pi_n$
is $m$-friable, uniformly for $m\ge (2+\varepsilon)\log_q n$ as $q^n \to
\infty$. This should be viewed as an unconditional analogue of a result of
Hildebrand (1984), saying that under the Riemann Hypothesis, the probability
that a number $\le x$ is $y$-friable is of the order of magnitude of the
Dickman $\rho$ function at $\log x/\log y$, uniformly for $\log y \ge
(2+\varepsilon) \log\log x$. As opposed to Hildebrand's result, we actually
obtain an asymptotic result.
  As an application of our estimates, we determine the rate of decay in the
asymptotic formula for the expected degree of the largest prime factor of a
random polynomial. This improves a previous estimate of Knopfmakher and
Manstavi\v{c}ius (1997).


title:
Symmetry of the Flows of Newtonian and Non-Newtonian Fluids in the
  Diverging and Converging Plane Channels

abstract:
  The results studying various laminar flow regimes in diverging and converging
plain channels (diffuser and confusor) with a small opening angle of channels
(diverging and converging angles) are presented. The results are obtained for a
viscous incompressible fluid by numerical simulation based on solving the
Navier-Stokes equations. The paper presents the results concerning the change
in the nature of flows from stationary - symmetric to stationary - asymmetric
and to non-stationary in the diffuser and confusor in dependence on the
Reynolds number. The ranges of existence of these flow regimes in plane
diffusers and confusors depending on the Reynolds number for Newtonian, pseudo
plastic and dilatants fluids with the Ostwald-de Waele power law for viscosity
are numerically found. The transitions of flow regimes in the diffuser from
symmetric steady state to the asymmetric one and to the asymmetric unsteady
mode in dependence on the Reynolds number are shown. The values of Reynolds
number that determine the existence ranges of these flow modes in the cases of
Newtonian and non-Newtonian fluids are given.


title: Single-pixel imaging based on weight sort of the Hadamard basis

abstract:
  Single-pixel imaging (SPI) is very popular in subsampling applications, but
the random measurement matrices it typically uses will lead to measurement
blindness as well as difficulties in calculation and storage, and will also
limit the further reduction in sampling rate. The deterministic Hadamard basis
has become an alternative choice due to its orthogonality and structural
characteristics. There is evidence that sorting the Hadamard basis is
beneficial to further reduce the sampling rate, thus many orderings have
emerged, but their relations remain unclear and lack a unified theory. Given
this, here we specially propose a concept named selection history, which can
record the Hadamard spatial folding process, and build a model based on it to
reveal the formation mechanisms of different orderings and to deduce the mutual
conversion relationship among them. Then, a weight ordering of the Hadamard
basis is proposed. Both numerical simulation and experimental results have
demonstrated that with this weight sort technique, the sampling rate,
reconstruction time and matrix memory consumption are greatly reduced in
comparison to traditional sorting methods. Therefore, we believe that this
method may pave the way for real-time single-pixel imaging.

categories: eess.IV cs.IT eess.SP math.IT physics.optics


title: Ray Tracing-Guided Design of Plenoptic Cameras

abstract:
  The design of a plenoptic camera requires the combination of two dissimilar
optical systems, namely a main lens and an array of microlenses. And while the
construction process of a conventional camera is mainly concerned with focusing
the image onto a single plane, in the case of plenoptic cameras there can be
additional requirements such as a predefined depth of field or a desired range
of disparities in neighboring microlens images. Due to this complexity, the
manual creation of multiple plenoptic camera setups is often a time-consuming
task. In this work we assume a simulation framework as well as the main lens
data given and present a method to calculate the remaining aperture, sensor and
microlens array parameters under different sets of constraints. Our ray
tracing-based approach is shown to result in models outperforming their
pendants generated with the commonly used paraxial approximations in terms of
image quality, while still meeting the desired constraints. Both the
implementation and evaluation setup including 30 plenoptic camera designs are
made publicly available.


title:
Creating Realistic Ground Truth Data for the Evaluation of Calibration
  Methods for Plenoptic and Conventional Cameras

abstract:
  Camera calibration methods usually consist of capturing images of known
calibration patterns and using the detected correspondences to optimize the
parameters of the assumed camera model. A meaningful evaluation of these
methods relies on the availability of realistic synthetic data. In previous
works concerned with conventional cameras the synthetic data was mainly created
by rendering perfect images with a pinhole camera and subsequently adding
distortions and aberrations to the renderings and correspondences according to
the assumed camera model. This method can bias the evaluation since not every
camera perfectly complies with an assumed model. Furthermore, in the field of
plenoptic camera calibration there is no synthetic ground truth data available
at all. We address these problems by proposing a method based on backward ray
tracing to create realistic ground truth data that can be used for an unbiased
evaluation of calibration methods for both types of cameras.


title: Simulation of Plenoptic Cameras

abstract:
  Plenoptic cameras enable the capturing of spatial as well as angular color
information which can be used for various applications among which are image
refocusing and depth calculations. However, these cameras are expensive and
research in this area currently lacks data for ground truth comparisons. In
this work we describe a flexible, easy-to-use Blender model for the different
plenoptic camera types which is on the one hand able to provide the ground
truth data for research and on the other hand allows an inexpensive assessment
of the cameras usefulness for the desired applications. Furthermore we show
that the rendering results exhibit the same image degradation effects as real
cameras and make our simulation publicly available.


title:
ASET: Ad-hoc Structured Exploration of Text Collections [Extended
  Abstract]

abstract:
  In this paper, we propose a new system called ASET that allows users to
perform structured explorations of text collections in an ad-hoc manner. The
main idea of ASET is to use a new two-phase approach that first extracts a
superset of information nuggets from the texts using existing extractors such
as named entity recognizers and then matches the extractions to a structured
table definition as requested by the user based on embeddings. In our
evaluation, we show that ASET is thus able to extract structured data from
real-world text collections in high quality without the need to design
extraction pipelines upfront.


title: Complete combinatorial characterization of greedy-drawable trees

abstract:
  A (Euclidean) greedy drawing of a graph is a drawing in which, for any two
vertices $s,t$ ($s \neq t$), there is a neighbor vertex of $s$ that is closer
to $t$ than to $s$ in the Euclidean distance. Greedy drawings are important in
the context of message routing in networks, and graph classes that admit greedy
drawings have been actively studied. N\"{o}llenburg and Prutkin (Discrete
Comput. Geom., 58(3), pp.543-579, 2017) gave a characterization of
greedy-drawable trees in terms of an inequality system that contains a
non-linear equation. Using the characterization, they gave a linear-time
recognition algorithm for greedy-drawable trees of maximum degree $\leq 4$.
However, a combinatorial characterization of greedy-drawable trees of maximum
degree 5 was left open. In this paper, we give a combinatorial characterization
of greedy-drawable trees of maximum degree $5$, which leads to a complete
combinatorial characterization of greedy-drawable trees. Furthermore, we give a
characterization of greedy-drawable pseudo-trees. It turns out that
greedy-drawable pseudo-trees admit a simpler characterization than
greedy-drawable trees.


title:
Nested Named Entity Recognition as Latent Lexicalized Constituency
  Parsing

abstract:
  Nested named entity recognition (NER) has been receiving increasing
attention. Recently, (Fu et al, 2021) adapt a span-based constituency parser to
tackle nested NER. They treat nested entities as partially-observed
constituency trees and propose the masked inside algorithm for partial
marginalization. However, their method cannot leverage entity heads, which have
been shown useful in entity mention detection and entity typing. In this work,
we resort to more expressive structures, lexicalized constituency trees in
which constituents are annotated by headwords, to model nested entities. We
leverage the Eisner-Satta algorithm to perform partial marginalization and
inference efficiently. In addition, we propose to use (1) a two-stage strategy
(2) a head regularization loss and (3) a head-aware labeling loss in order to
enhance the performance. We make a thorough ablation study to investigate the
functionality of each component. Experimentally, our method achieves the
state-of-the-art performance on ACE2004, ACE2005 and NNE, and competitive
performance on GENIA, and meanwhile has a fast inference speed.


title: Quantum neural networks force fields generation

abstract:
  Accurate molecular force fields are of paramount importance for the efficient
implementation of molecular dynamics techniques at large scales. In the last
decade, machine learning methods have demonstrated impressive performances in
predicting accurate values for energy and forces when trained on finite size
ensembles generated with ab initio techniques. At the same time, quantum
computers have recently started to offer new viable computational paradigms to
tackle such problems. On the one hand, quantum algorithms may notably be used
to extend the reach of electronic structure calculations. On the other hand,
quantum machine learning is also emerging as an alternative and promising path
to quantum advantage. Here we follow this second route and establish a direct
connection between classical and quantum solutions for learning neural network
potentials. To this end, we design a quantum neural network architecture and
apply it successfully to different molecules of growing complexity. The quantum
models exhibit larger effective dimension with respect to classical
counterparts and can reach competitive performances, thus pointing towards
potential quantum advantages in natural science applications via quantum
machine learning.

categories: quant-ph cs.LG physics.chem-ph physics.comp-ph


title: Curvatures on homogeneous Finsler spaces

abstract:
  The main aim of this article is to calculate explicit formula for S-curvature
in homogeneous generalized m-Kropina metric. Further, we also deduce mean
Berwald curvature for homogeneous generalized m-Kropina metric from
S-curvature.


title: Inadequately Pre-trained Models are Better Feature Extractors

abstract:
  Pre-training has been a popular learning paradigm in deep learning era,
especially in annotation-insufficient scenario. Better ImageNet pre-trained
models have been demonstrated, from the perspective of architecture, by
previous research to have better transferability to downstream tasks. However,
in this paper, we found that during the same pre-training process, models at
middle epochs, which is inadequately pre-trained, can outperform fully trained
models when used as feature extractors (FE), while the fine-tuning (FT)
performance still grows with the source performance. This reveals that there is
not a solid positive correlation between top-1 accuracy on ImageNet and the
transferring result on target data. Based on the contradictory phenomenon
between FE and FT that better feature extractor fails to be fine-tuned better
accordingly, we conduct comprehensive analyses on features before softmax layer
to provide insightful explanations. Our discoveries suggest that, during
pre-training, models tend to first learn spectral components corresponding to
large singular values and the residual components contribute more when
fine-tuning.


title:
Enhancement of Superconductivity upon reduction of carrier density in
  proximitized graphene

abstract:
  The superconducting transition temperature (Tc) of a single layer graphene
coupled to an Indium oxide (InO) film, a low carrier-density superconductor, is
found to increase with decreasing carrier density and is largest close to the
average charge neutrality point in graphene. Such an effect is very surprising
in conventional BCS superconductors. We study this phenomenon both
experimentally and theoretically. Our analysis suggests that the InO film
induces random electron and hole-doped puddles in the graphene. The Josephson
effect across these regions of opposite polarity enhances the Josephson
coupling between the superconducting clusters in InO, along with the overall Tc
of the bilayer heterostructure. This enhancement is most effective when the
chemical potential of the system is tuned between the charge neutrality points
of the electron and hole-doped regions.


title: Structure-Aware Flow Generation for Human Body Reshaping

abstract:
  Body reshaping is an important procedure in portrait photo retouching. Due to
the complicated structure and multifarious appearance of human bodies, existing
methods either fall back on the 3D domain via body morphable model or resort to
keypoint-based image deformation, leading to inefficiency and unsatisfied
visual quality. In this paper, we address these limitations by formulating an
end-to-end flow generation architecture under the guidance of body structural
priors, including skeletons and Part Affinity Fields, and achieve
unprecedentedly controllable performance under arbitrary poses and garments. A
compositional attention mechanism is introduced for capturing both visual
perceptual correlations and structural associations of the human body to
reinforce the manipulation consistency among related parts. For a comprehensive
evaluation, we construct the first large-scale body reshaping dataset, namely
BR-5K, which contains 5,000 portrait photos as well as professionally retouched
targets. Extensive experiments demonstrate that our approach significantly
outperforms existing state-of-the-art methods in terms of visual performance,
controllability, and efficiency. The dataset is available at our website:
https://github.com/JianqiangRen/FlowBasedBodyReshaping.


title:
A preparative mass spectrometer to deposit intact large native protein
  complexes

abstract:
  Electrospray ion-beam deposition (ES-IBD) is a versatile tool to study
structure and reactivity of molecules from small metal clusters to large
protein assemblies. It brings molecules gently into the gas phase where they
can be accurately manipulated and purified, followed by controlled deposition
onto various substrates. In combination with imaging techniques, direct
structural information of well-defined molecules can be obtained, which is
essential to test and interpret results from indirect mass spectrometry
techniques. To date, ion-beam deposition experiments are limited to a small
number of custom instruments worldwide, and there are no commercial
alternatives. Here we present a module that adds ion-beam deposition
capabilities to a popular commercial MS platform (Thermo
Scientific$^{\mathrm{TM}}$ Q Exactive$^{\mathrm{TM}}$ UHMR). This combination
significantly reduces the overhead associated with custom instruments, while
benefiting from established high performance and reliability. We present
current performance characteristics including beam intensity, landing-energy
control, and deposition spot size for a broad range of molecules. In
combination with atomic force microscopy (AFM) and transmission electron
microscopy (TEM), we distinguish near-native from unfolded proteins and show
retention of native shape of protein assemblies after dehydration and
deposition. Further, we use an enzymatic assay to quantify activity of an
non-covalent protein complex after deposition an a dry surface. Together, these
results indicate a great potential of ES-IBD for applications in structural
biology, but also outline the challenges that need to be solved for it to reach
its full potential.


title:
Generalized quantum Vlasov equation for particle creation effects and
  unitary dynamics

abstract:
  The loss of time-translational invariance caused by a time-dependent external
agent leads to particle creation effects in quantum field theory. This
phenomenon results in ambiguities when selecting the quantum vacuum of the
canonical quantization. In this work we analyze how the time evolution of the
number of created particles depends on these ambiguities when external agents
are spatially homogeneous. In particular, we generalize the standard quantum
Vlasov equation in order to accommodate in its formulation the possibility of
having different choices of vacuum, including adiabatic vacua. This study leads
us to propose a new physical criterion stronger than the unitary implementation
of the dynamics in order to reduce the quantization ambiguities.


title:
Anomalous gauge couplings vis-$\grave{a}$-vis $(g-2)_\mu$ and flavor
  observables

abstract:
  We reassess non-standard triple gauge couplings in the light of the recent
$(g-2)_\mu$ measurement at FNAL, the new lattice theory result of $(g-2)_\mu$
and the updated measurements of several $B$-decay modes. In the framework of
SMEFT, three bosonic dimension-6 operators are invoked to parametrize physics
beyond the Standard Model and their contributions to such low-energy
observables computed. Constraints on the corresponding Wilson coefficients are
then derived from fits to the current experimental bounds on the observables
and compared with the most stringent ones available from the 13 TeV LHC data in
the $W^+ W^-$ and $W^\pm Z$ production channels.