title:
Magnetically tunable Dirac and Weyl fermions in the Zintl materials
  family

abstract:
  Recent classification efforts encompassing crystalline symmetries have
revealed rich possibilities for solid-state systems to support a tapestry of
exotic topological states. However, finding materials that realize such states
remains a daunting challenge. Here we show how the interplay of topology,
symmetry, and magnetism combined with doping and external electric and magnetic
field controls can be used to drive the previously unreported SrIn$_2$As$_2$
materials family into a variety of topological phases. Our first-principles
calculations and symmetry analysis reveal that SrIn$_2$As$_2$ is a dual
topological insulator with $Z_2=(1;000)$ and mirror Chern number $C_M= -1$. Its
isostructural and isovalent antiferromagnetic cousin EuIn$_2$As$_2$ is found to
be an axion insulator with $Z_4= 2$. The broken time-reversal symmetry via Eu
doping in Sr$_{1-x}$Eu$_x$In$_2$As$_2$ results in a higher-order or topological
crystalline insulator state depending on the orientation of the magnetic easy
axis. We also find that antiferromagnetic EuIn$_2$P$_2$ is a trivial insulator
with $Z_4= 0$, and that it undergoes a magnetic field-driven transition to an
ideal Weyl fermion or nodal fermion state with $Z_4= 1$ with applied magnetic
field. Our study identifies Sr$_{1-x}$Eu$_x$In$_2$(As, P)$_2$ as a new tunable
materials platform for investigating the physics and applications of Weyl and
nodal fermions in the scaffolding of crystalline and axion insulator states.


title:
A Simple Non-Stationary Mean Ergodic Theorem, with Bonus Weak Law of
  Large Numbers

abstract:
  This brief pedagogical note re-proves a simple theorem on the convergence, in
$L_2$ and in probability, of time averages of non-stationary time series to the
mean of expectation values. The basic condition is that the sum of covariances
grows sub-quadratically with the length of the time series. I make no claim to
originality; the result is widely, but unevenly, spread bit of folklore among
users of applied probability. The goal of this note is merely to even out that
distribution.


title: Group inverse for anti-triangular block operator matrices

abstract:
  We present the existence of the group inverse and its representation for the
block operator matrix $\left( \begin{array}{cc} E&I\\ F&0 \end{array} \right)$
under the condition $FEF^{\pi}=0$. The group inverse for the anti-triangular
block matrices with two identical subblocks under the same condition is thereby
investigated. These extend the results of Zou, Chen and Mosi\'c (Studia Scient.
Math. Hungar., 54(2017), 489--508), and Cao, Zhang and Ge (J. Appl. Math.
Comput., 46(2014), 169--179).


title: GPU Computation of the Euler Characteristic Curve for Imaging Data

abstract:
  Persistent homology is perhaps the most popular and useful tool offered by
topological data analysis, with point-cloud data being the most common setup.
Its older cousin, the Euler characteristic curve (ECC) is less expressive, but
far easier to compute. It is particularly suitable for analyzing imaging data,
and is commonly used in fields ranging from astrophysics to biomedical image
analysis. These fields are embracing GPU computations to handle increasingly
large datasets. We therefore propose an optimized GPU implementation of ECC
computation for 2D and 3D grayscale images. The goal of this paper is twofold.
First, we offer a practical tool, illustrating its performance with thorough
experimentation, but also explain its inherent shortcomings. Second, this
simple algorithm serves as a perfect backdrop for highlighting basic GPU
programming techniques that make our implementation so efficient, and some
common pitfalls we avoided. This is intended as a step towards a wider usage of
GPU programming in computational geometry and topology software. We find this
is particularly important as geometric and topological tools are used in
conjunction with modern, GPU-accelerated machine learning frameworks.


title: Deep Point Cloud Simplification for High-quality Surface Reconstruction

abstract:
  The growing size of point clouds enlarges consumptions of storage,
transmission, and computation of 3D scenes. Raw data is redundant, noisy, and
non-uniform. Therefore, simplifying point clouds for achieving compact, clean,
and uniform points is becoming increasingly important for 3D vision and
graphics tasks. Previous learning based methods aim to generate fewer points
for scene understanding, regardless of the quality of surface reconstruction,
leading to results with low reconstruction accuracy and bad point distribution.
In this paper, we propose a novel point cloud simplification network (PCS-Net)
dedicated to high-quality surface mesh reconstruction while maintaining
geometric fidelity. We first learn a sampling matrix in a feature-aware
simplification module to reduce the number of points. Then we propose a novel
double-scale resampling module to refine the positions of the sampled points,
to achieve a uniform distribution. To further retain important shape features,
an adaptive sampling strategy with a novel saliency loss is designed. With our
PCS-Net, the input non-uniform and noisy point cloud can be simplified in a
feature-aware manner, i.e., points near salient features are consolidated but
still with uniform distribution locally. Experiments demonstrate the
effectiveness of our method and show that we outperform previous simplification
or reconstruction-oriented upsampling methods.


title:
Birth and Death of One-dimensional Domains in Cylindrically Confined
  Liquid Crystals

abstract:
  Nematic liquid crystal is a partially ordered matter that has been a popular
model system to study a variety of topological defects. In this work utilizing
spontaneously twisting achiral liquid crystals, we introduce a one-dimensional
(1D) model system to investigate how domains and topological defects come and
go, reminiscing Kibble-Zurek mechanism. Because of the unusual elastic
properties, lyotropic chromonic LCs form a double-twist (DT) structure in a
cylindrical capillary with degenerate planar anchoring. Consequently, the
domains of different handedness coexist with equal probabilities, forming the
domain-wall-like defects between them. We experimentally measure the
length-distribution of the domains and the time evolution of the distribution,
which seem to follow a three-parameter log-normal distribution. Inspired by the
domain formation from droplet coalescence during isotropic-nematic phase
transition, we propose that the random coalescence within a chain of normally
distributed domains may lead to the log-normally distributed domains. The
elastic energy of LC drives the time evolution of the domain-size distribution,
which requires further investigation. The introduced model system allows the
extensive study of 1D model domains and provides prospective ideas for the
physics behind their formation and annihilation.


title:
Energy-Efficient Power Control and Beamforming for Reconfigurable
  Intelligent Surface-Aided Uplink IoT Networks

abstract:
  Recently, reconfigurable intelligent surface (RIS), a planar metasurface
consisting of a large number of low-cost reflecting elements, has received much
attention due to its ability to improve both the spectrum and energy
efficiencies by reconfiguring the wireless propagation environment. In this
paper, we propose a RIS phase shift and BS beamforming optimization technique
that minimizes the uplink transmit power of a RIS-aided IoT network. Key idea
of the proposed scheme, referred to as Riemannian conjugate gradient-based
joint optimization (RCG-JO), is to jointly optimize the RIS phase shifts and
the BS beamforming vectors using the Riemannian conjugate gradient technique.
By exploiting the product Riemannian manifold structure of the sets of
unit-modulus phase shifts and unit-norm beamforming vectors, we convert the
nonconvex uplink power minimization problem into the unconstrained problem and
then find out the optimal solution on the product Riemannian manifold. From the
performance analysis and numerical evaluations, we demonstrate that the
proposed RCG-JO technique achieves $94\%$ reduction of the uplink transmit
power over the conventional scheme without RIS.


title:
deepNIR: Datasets for generating synthetic NIR images and improved fruit
  detection system using deep learning techniques

abstract:
  This paper presents datasets utilised for synthetic near-infrared (NIR) image
generation and bounding-box level fruit detection systems. It is undeniable
that high-calibre machine learning frameworks such as Tensorflow or Pytorch,
and large-scale ImageNet or COCO datasets with the aid of accelerated GPU
hardware have pushed the limit of machine learning techniques for more than
decades. Among these breakthroughs, a high-quality dataset is one of the
essential building blocks that can lead to success in model generalisation and
the deployment of data-driven deep neural networks. In particular, synthetic
data generation tasks often require more training samples than other supervised
approaches. Therefore, in this paper, we share the NIR+RGB datasets that are
re-processed from two public datasets (i.e., nirscene and SEN12MS) and our
novel NIR+RGB sweet pepper(capsicum) dataset. We quantitatively and
qualitatively demonstrate that these NIR+RGB datasets are sufficient to be used
for synthetic NIR image generation. We achieved Frechet Inception Distance
(FID) of 11.36, 26.53, and 40.15 for nirscene1, SEN12MS, and sweet pepper
datasets respectively. In addition, we release manual annotations of 11 fruit
bounding boxes that can be exported as various formats using cloud service.
Four newly added fruits [blueberry, cherry, kiwi, and wheat] compound 11 novel
bounding box datasets on top of our previous work presented in the deepFruits
project [apple, avocado, capsicum, mango, orange, rockmelon, strawberry]. The
total number of bounding box instances of the dataset is 162k and it is ready
to use from cloud service. For the evaluation of the dataset, Yolov5 single
stage detector is exploited and reported impressive
mean-average-precision,mAP[0.5:0.95] results of[min:0.49, max:0.812]. We hope
these datasets are useful and serve as a baseline for the future studies.


title:
Mass and spin of Kerr black holes in terms of observational quantities:
  The dragging effect on the redshift

abstract:
  In this work, we elaborate on the development of a general relativistic
formalism that allows one to analytically express the mass and spin parameters
of the Kerr black hole in terms of observational data: the total redshift and
blueshift of photons emitted by geodesic massive particles revolving the black
hole and their orbital parameters. Thus, we present concise closed formulas for
the mass and spin parameters of the Kerr black hole in terms of few directly
observed quantities in the case of equatorial circular orbits either when the
black hole is static or is moving with respect to a distant observer.
Furthermore, we incorporate the gravitational dragging effect generated by the
rotating nature of the Kerr black hole into the analysis and elucidate its
non-trivial contribution to the expression for the light bending parameter and
the frequency shifts of photons emitted by orbiting particles that renders
simple symmetric expressions for the kinematic redshift and blueshift. We also
incorporate the dependency of the frequency shift on the azimuthal angle, a
fact that allows one to express the total redshift/blueshift along any point of
the orbit of the revolving particle for the cases when the black hole is both
static or moving with respect to us. These formulas allow one to compute the
Kerr black hole parameters by applying this general relativistic formalism to
astrophysical systems like the megamaser accretion disks orbiting supermassive
black holes at the core of active galactic nuclei. Our results open a new
window to implement parameter estimation studies to constrain black hole
variables, and they can be generalized to black hole solutions beyond Einstein
gravity.


title: Semantic-aligned Fusion Transformer for One-shot Object Detection

abstract:
  One-shot object detection aims at detecting novel objects according to merely
one given instance. With extreme data scarcity, current approaches explore
various feature fusions to obtain directly transferable meta-knowledge. Yet,
their performances are often unsatisfactory. In this paper, we attribute this
to inappropriate correlation methods that misalign query-support semantics by
overlooking spatial structures and scale variances. Upon analysis, we leverage
the attention mechanism and propose a simple but effective architecture named
Semantic-aligned Fusion Transformer (SaFT) to resolve these issues.
Specifically, we equip SaFT with a vertical fusion module (VFM) for cross-scale
semantic enhancement and a horizontal fusion module (HFM) for cross-sample
feature fusion. Together, they broaden the vision for each feature point from
the support to a whole augmented feature pyramid from the query, facilitating
semantic-aligned associations. Extensive experiments on multiple benchmarks
demonstrate the superiority of our framework. Without fine-tuning on novel
classes, it brings significant performance gains to one-stage baselines,
lifting state-of-the-art results to a higher level.


title:
Snowmass 2021 Accelerator Frontier White Paper: Near Term Applications
  driven by Advanced Accelerator Concepts

abstract:
  While the long-term vision of the advanced accelerator community is aimed at
addressing the challenges of future collider technology, it is critical that
the community takes advantage of the opportunity to make large societal impact
through its near-term applications. In turn, enabling robust applications
strengthens the quality, control, and reliability of the underlying accelerator
infrastructure. The white paper contributions that are solicited here will
summarize the near-term applications ideas presented by the advanced
accelerator community, assessing their potential impact, discussing scientific
and technical readiness of concepts, and providing a timeline for
implementation.


title: CodeReviewer: Pre-Training for Automating Code Review Activities

abstract:
  Code review is an essential part to software development lifecycle since it
aims at guaranteeing the quality of codes. Modern code review activities
necessitate developers viewing, understanding and even running the programs to
assess logic, functionality, latency, style and other factors. It turns out
that developers have to spend far too much time reviewing the code of their
peers. Accordingly, it is in significant demand to automate the code review
process. In this research, we focus on utilizing pre-training techniques for
the tasks in the code review scenario. We collect a large-scale dataset of real
world code changes and code reviews from open-source projects in nine of the
most popular programming languages. To better understand code diffs and
reviews, we propose CodeReviewer, a pre-trained model that utilizes four
pre-training tasks tailored specifically for the code review senario. To
evaluate our model, we focus on three key tasks related to code review
activities, including code change quality estimation, review comment generation
and code refinement. Furthermore, we establish a high-quality benchmark dataset
based on our collected data for these three tasks and conduct comprehensive
experiments on it. The experimental results demonstrate that our model
outperforms the previous state-of-the-art pre-training approaches in all tasks.
Further analysis show that our proposed pre-training tasks and the multilingual
pre-training dataset benefit the model on the understanding of code changes and
reviews.


title:
DeepAD: A Robust Deep Learning Model of Alzheimer's Disease Progression
  for Real-World Clinical Applications

abstract:
  The ability to predict the future trajectory of a patient is a key step
toward the development of therapeutics for complex diseases such as Alzheimer's
disease (AD). However, most machine learning approaches developed for
prediction of disease progression are either single-task or single-modality
models, which can not be directly adopted to our setting involving multi-task
learning with high dimensional images. Moreover, most of those approaches are
trained on a single dataset (i.e. cohort), which can not be generalized to
other cohorts. We propose a novel multimodal multi-task deep learning model to
predict AD progression by analyzing longitudinal clinical and neuroimaging data
from multiple cohorts. Our proposed model integrates high dimensional MRI
features from a 3D convolutional neural network with other data modalities,
including clinical and demographic information, to predict the future
trajectory of patients. Our model employs an adversarial loss to alleviate the
study-specific imaging bias, in particular the inter-study domain shifts. In
addition, a Sharpness-Aware Minimization (SAM) optimization technique is
applied to further improve model generalization. The proposed model is trained
and tested on various datasets in order to evaluate and validate the results.
Our results showed that 1) our model yields significant improvement over the
baseline models, and 2) models using extracted neuroimaging features from 3D
convolutional neural network outperform the same models when applied to
MRI-derived volumetric features.


title: On the dynamics of shallow ice sheets. Modelling and analysis

abstract:
  We formulate a model describing the evolution of thickness in a shallow ice
sheet lying over a lithosphere. The model is thus governed by a set of
variational inequalities that involve nonlinearities in the time derivative and
in the elliptic term. Solutions are constructed via a finite difference scheme
in time.


title: TMS: A Temporal Multi-scale Backbone Design for Speaker Embedding

abstract:
  Speaker embedding is an important front-end module to explore discriminative
speaker features for many speech applications where speaker information is
needed. Current SOTA backbone networks for speaker embedding are designed to
aggregate multi-scale features from an utterance with multi-branch network
architectures for speaker representation. However, naively adding many branches
of multi-scale features with the simple fully convolutional operation could not
efficiently improve the performance due to the rapid increase of model
parameters and computational complexity. Therefore, in the most current
state-of-the-art network architectures, only a few branches corresponding to a
limited number of temporal scales could be designed for speaker embeddings. To
address this problem, in this paper, we propose an effective temporal
multi-scale (TMS) model where multi-scale branches could be efficiently
designed in a speaker embedding network almost without increasing computational
costs. The new model is based on the conventional TDNN, where the network
architecture is smartly separated into two modeling operators: a
channel-modeling operator and a temporal multi-branch modeling operator. Adding
temporal multi-scale in the temporal multi-branch operator needs only a little
bit increase of the number of parameters, and thus save more computational
budget for adding more branches with large temporal scales. Moreover, in the
inference stage, we further developed a systemic re-parameterization method to
convert the TMS-based model into a single-path-based topology in order to
increase inference speed. We investigated the performance of the new TMS method
for automatic speaker verification (ASV) on in-domain and out-of-domain
conditions. Results show that the TMS-based model obtained a significant
increase in the performance over the SOTA ASV models, meanwhile, had a faster
inference speed.


title: Snowmass White Paper: String Perturbation Theory

abstract:
  The purpose of this White Paper is to review recent progress towards
elucidating and evaluating string amplitudes, relating them to quantum field
theory amplitudes, applying their predictions to string dualities, exploring
their connection with gravitational physics, and deepening our understanding of
their mathematical structure. We also present a selection of targets for future
research.


title:
PLANET: Dynamic Content Planning in Autoregressive Transformers for
  Long-form Text Generation

abstract:
  Despite recent progress of pre-trained language models on generating fluent
text, existing methods still suffer from incoherence problems in long-form text
generation tasks that require proper content control and planning to form a
coherent high-level logical flow. In this work, we propose PLANET, a novel
generation framework leveraging autoregressive self-attention mechanism to
conduct content planning and surface realization dynamically. To guide the
generation of output sentences, our framework enriches the Transformer decoder
with latent representations to maintain sentence-level semantic plans grounded
by bag-of-words. Moreover, we introduce a new coherence-based contrastive
learning objective to further improve the coherence of output. Extensive
experiments are conducted on two challenging long-form text generation tasks
including counterargument generation and opinion article generation. Both
automatic and human evaluations show that our method significantly outperforms
strong baselines and generates more coherent texts with richer contents.


title:
RelationPrompt: Leveraging Prompts to Generate Synthetic Data for
  Zero-Shot Relation Triplet Extraction

abstract:
  Despite the importance of relation extraction in building and representing
knowledge, less research is focused on generalizing to unseen relations types.
We introduce the task setting of Zero-Shot Relation Triplet Extraction
(ZeroRTE) to encourage further research in low-resource relation extraction
methods. Given an input sentence, each extracted triplet consists of the head
entity, relation label, and tail entity where the relation label is not seen at
the training stage. To solve ZeroRTE, we propose to synthesize relation
examples by prompting language models to generate structured texts. Concretely,
we unify language model prompts and structured text approaches to design a
structured prompt template for generating synthetic relation samples when
conditioning on relation label prompts (RelationPrompt). To overcome the
limitation for extracting multiple relation triplets in a sentence, we design a
novel Triplet Search Decoding method. Experiments on FewRel and Wiki-ZSL
datasets show the efficacy of RelationPrompt for the ZeroRTE task and zero-shot
relation classification. Our code and data are available at
github.com/declare-lab/RelationPrompt.


title: Rough Collisions

abstract:
  A rough collision law describes the limiting contact dynamics of a pair of
rough rigid bodies, as the scale of the rough features (asperities) on the
surface of each body goes to zero. The class of rough collision laws is quite
large and includes random elements. Our main results characterize the rough
collision laws for a freely moving rough disk and a fixed rough wall in
dimension 2. Any collision law which (i) is symmetric with respect to a certain
well-known invariant measure from billiards theory, and (ii) conserves the
projection of the phase space velocity onto the "rolling velocity" is a rough
collision law. We also provide a method for explicitly constructing rough
collision laws for a broad range of choices of microstructure on the disk and
wall. In our introduction, we review past work in billiards, including
characterizations of other rough billiard systems, which our results build
upon.


title: Knowledge Graph-Enabled Text-Based Automatic Personality Prediction

abstract:
  How people think, feel, and behave, primarily is a representation of their
personality characteristics. By being conscious of personality characteristics
of individuals whom we are dealing with or decided to deal with, one can
competently ameliorate the relationship, regardless of its type. With the rise
of Internet-based communication infrastructures (social networks, forums,
etc.), a considerable amount of human communications take place there. The most
prominent tool in such communications, is the language in written and spoken
form that adroitly encodes all those essential personality characteristics of
individuals. Text-based Automatic Personality Prediction (APP) is the automated
forecasting of the personality of individuals based on the generated/exchanged
text contents. This paper presents a novel knowledge graph-enabled approach to
text-based APP that relies on the Big Five personality traits. To this end,
given a text a knowledge graph which is a set of interlinked descriptions of
concepts, was built through matching the input text's concepts with DBpedia
knowledge base entries. Then, due to achieving more powerful representation the
graph was enriched with the DBpedia ontology, NRC Emotion Intensity Lexicon,
and MRC psycholinguistic database information. Afterwards, the knowledge graph
which is now a knowledgeable alternative for the input text was embedded to
yield an embedding matrix. Finally, to perform personality predictions the
resulting embedding matrix was fed to four suggested deep learning models
independently, which are based on convolutional neural network (CNN), simple
recurrent neural network (RNN), long short term memory (LSTM) and bidirectional
long short term memory (BiLSTM). The results indicated a considerable
improvements in prediction accuracies in all of the suggested classifiers.


title:
Ultrafast modulation of the molten metal surface tension under
  femtosecond laser irradiation

abstract:
  We predict ultrafast modulation of the pure molten metal surface stress
fields under the irradiation of the single femtosecond laser pulse through the
two-temperature model molecular-dynamics simulations. High-resolution and
precision calculations are used to resolve the ultrafast laser-induced
anisotropic relaxations of the pressure components on the time-scale comparable
to the intrinsic liquid density relaxation time. The surface tensions are found
being modulated to surprisingly high values, within picoseconds after the
irradiation, due to the ultrafast development of the nanometer scale
non-hydrostatic regime behind the exterior atomic layer of the liquid surfaces.
The reported novel regulation mechanism of the surface tension hints at
levitating the manipulation of liquid surfaces, such as ultrafast steering the
surface directional transport and patterning.


title: On Modular Cohomotopy Groups

abstract:
  Let $p$ be a prime and let $\pi^n(X;\mathbb{Z}/p^r)=[X,M_n(\mathbb{Z}/p^r)]$
be the set of homotopy classes of based maps from CW-complexes $X$ into the mod
$p^r$ Moore spaces $M_n(\mathbb{Z}/p^r)$ of degree $n$, where $\mathbb{Z}/p^r$
denotes the integers mod $p^r$. In this paper we firstly determine the modular
cohomotopy groups $\pi^n(X;\mathbb{Z}/p^r)$ up to extensions by classical
methods of primary cohomology operations and give conditions for the splitness
of the extensions. Secondly we utilize some unstable homotopy theory of Moore
spaces to study the modular cohomotopy groups; especially, the group
$\pi^3(X;\mathbb{Z}_{(2)})$ with $\dim(X)\leq 6$ is determined.


title: Parameterized and Exact Algorithms for Class Domination Coloring

abstract:
  A class domination coloring (also called cd-Coloring or dominated coloring)
of a graph is a proper coloring in which every color class is contained in the
neighbourhood of some vertex. The minimum number of colors required for any
cd-coloring of $G$, denoted by $\chi_{cd}(G)$, is called the class domination
chromatic number (cd-chromatic number) of $G$. In this work, we consider two
problems associated with the cd-coloring of a graph in the context of exact
exponential-time algorithms and parameterized complexity. (1) Given a graph $G$
on $n$ vertices, find its cd-chromatic number. (2) Given a graph $G$ and
integers $k$ and $q$, can we delete at most $k$ vertices such that the
cd-chromatic number of the resulting graph is at most $q$? For the first
problem, we give an exact algorithm with running time $\Oh(2^n n^4 \log n)$.
Also, we show that the problem is \FPT\ with respect to the number $q$ of
colors as the parameter on chordal graphs. On graphs of girth at least 5, we
show that the problem also admits a kernel with $\Oh(q^3)$ vertices. For the
second (deletion) problem, we show \NP-hardness for each $q \geq 2$. Further,
on split graphs, we show that the problem is \NP-hard if $q$ is a part of the
input and \FPT\ with respect to $k$ and $q$ as combined parameters. As
recognizing graphs with cd-chromatic number at most $q$ is \NP-hard in general
for $q \geq 4$, the deletion problem is unlikely to be \FPT\ when parameterized
by the size of the deletion set on general graphs. We show fixed parameter
tractability for $q \in \{2,3\}$ using the known algorithms for finding a
vertex cover and an odd cycle transversal as subroutines.


title: A note about rational surfaces as unions of affine planes

abstract:
  We prove that any smooth rational projective surface over the field of
complex numbers has an open covering consisting of 3 subsets isomorphic to
affine planes.


title: Thick hyperbolic invariant Cantor set and wild attractor

abstract:
  Let $A$ be the set of $\beta \in (1, 2]$ so that $f_\beta$ is a symmetric
tent map with finite critical orbit. For $\beta \in A$, by operating Denjoy
like surgery on $f_{\beta}$, we construct a $C^1$ unimodal map $g_\beta$
admitting thick hyperbolic invariant Cantor set which contains a wild Cantor
attractor of $g_\beta$. The smoothness of $g_\beta$ is ensured by effective
estimation of the preimages of the critical point as well as the prescribed
lengths of the glued intervals. Furthermore, $A$ is dense in $(1, 2]$, and
$g_\beta$ can not be $C^{1+\alpha}$ because the hyperbolic invariant Cantor set
of $C^{1+\alpha}$ map is measure zero.


title:
Community-Driven Comprehensive Scientific Paper Summarization: Insight
  from cvpaper.challenge

abstract:
  The present paper introduces a group activity involving writing summaries of
conference proceedings by volunteer participants. The rapid increase in
scientific papers is a heavy burden for researchers, especially non-native
speakers, who need to survey scientific literature. To alleviate this problem,
we organized a group of non-native English speakers to write summaries of
papers presented at a computer vision conference to share the knowledge of the
papers read by the group. We summarized a total of 2,000 papers presented at
the Conference on Computer Vision and Pattern Recognition, a top-tier
conference on computer vision, in 2019 and 2020. We quantitatively analyzed
participants' selection regarding which papers they read among the many
available papers. The experimental results suggest that we can summarize a wide
range of papers without asking participants to read papers unrelated to their
interests.


title:
Grow-up of a Filament Channel by Intermittent Small-scale Magnetic
  Reconnection

abstract:
  Filament channel (FC), a plasma volume where the magnetic field is primarily
aligned with the polarity inversion line, is believed to be the pre-eruptive
configuration of coronal mass ejections. Nevertheless, evidence for how the FC
is formed is still elusive. In this paper, we present a detailed study on the
build-up of a FC to understand its formation mechanism. The New Vacuum Solar
Telescope of Yunnan Observatories and Optical and Near-Infrared Solar Eruption
Tracer of Nanjing University, as well as the AIA and HMI on board Solar
Dynamics Observatory are used to study the grow-up process of the FC.
Furthermore, we reconstruct the non-linear force-free field (NLFFF) of the
active region using the regularized Biot-Savart laws (RBSL) and
magnetofrictional method to reveal three-dimension (3D) magnetic field
properties of the FC. We find that partial filament materials are quickly
transferred to longer magnetic field lines formed by small-scale magnetic
reconnection, as evidenced by dot-like H{\alpha}/EUV brightenings and
subsequent bidirectional outflow jets, as well as untwisting motions. The
H{\alpha}/EUV bursts appear repeatedly at the same location and are closely
associated with flux cancellation, which occurs between two small-scale
opposite polarities and is driven by shearing and converging motions. The 3D
NLFFF model reveals that the reconnection takes place in a hyperbolic flux tube
that is located above the flux cancellation site and below the FC. The FC is
gradually built up toward a twisted flux rope via series of small-scale
reconnection events that occur intermittently prior to the eruption.


title:
Electromagnetic moments of scandium isotopes and $N=28$ isotones in the
  distinctive $0f_{7/2}$ orbit

abstract:
  The electric quadrupole moment of $^{49}$Sc was measured by collinear laser
spectroscopy at CERN-ISOLDE to be $Q_{\rm s}=-0.159(8)$ $e$b, and a nearly
tenfold improvement in precision was reached for the electromagnetic moments of
$^{47,49}$Sc. The single-particle behavior and nucleon-nucleon correlations are
investigated with the electromagnetic moments of $Z=21$ isotopes and $N=28$
isotones as valence neutrons and protons fill the distinctive $0f_{7/2}$ orbit,
respectively, located between magic numbers, 20 and 28. The experimental data
are interpreted with shell-model calculations using an effective interaction,
and ab-initio valence-space in-medium similarity renormalization group
calculations based on chiral interactions. These results highlight the
sensitivity of nuclear electromagnetic moments to different types of
nucleon-nucleon correlations, and establish an important benchmark for further
developments of theoretical calculations.


title:
Nuclear Modified Transverse Momentum Distributions and 3D Imaging in
  Nuclei

abstract:
  In this proceeding, we review our recent work in which we performed the first
global analysis of nuclear modified Transverse Momentum Distribution Functions
(TMDs). We demonstrate for the first time that the global set of TMD
experimental data from HERMES, E866, E772, RHIC, ATLAS, and CMS, can be
described using a simple model which accounts for the nuclear modifications for
the TMDs as a non-perturbative correction. Using this model, we extract the
nuclear modified TMDs for the first time.


title:
Local Hard-Sphere Poisson-Nernst-Planck Models for Ionic Channels with
  Permanent Charges

abstract:
  The main goal of this work is to examine the qualitative effect of ion sizes
via a steady-state boundary value problem. We study a one-dimensional version
of a Poisson-Nernst-Planck system with a local hard-sphere potential model for
ionic flow through a membrane channel with fixed boundary ion concentrations
and electric potentials. A complete set of integrals for the inner system is
illustrated that delivers information for boundary and internal layers. In
addition, a group of simultaneous equations appears in the construction of
singular orbits. The research aims to set up a simple formation defined by the
profile of permanent charges with two mobile ion species, one positively
charged, cation, and one negatively charged, anion. A local hard-sphere
potential that depends pointwise on ion concentrations is included in the model
to estimate ion-size impacts on the ionic flow. The analysis is built on the
geometric singular perturbation theory, particularly on specific structures of
this concrete model. For 1:1 ionic mixtures, we first conduct precise
mathematical analysis and derive a matching system of nonlinear algebraic
equations. We then extend the results by directing on a critical case where the
current is zero. Treating the ion sizes as small parameters, we derive an
approximation of zero-current fluxes for general values of permanent charge. We
then focus on small values of permanent charge to obtain more concrete outputs.
We will also examine ion size effects on the flow rate of matter for the
zero-current case.


title: Causal Robot Communication Inspired by Observational Learning Insights

abstract:
  Autonomous robots must communicate about their decisions to gain trust and
acceptance. When doing so, robots must determine which actions are causal,
i.e., which directly give rise to the desired outcome, so that these actions
can be included in explanations. In behavior learning in psychology, this sort
of reasoning during an action sequence has been studied extensively in the
context of imitation learning. And yet, these techniques and empirical insights
are rarely applied to human-robot interaction (HRI). In this work, we discuss
the relevance of behavior learning insights for robot intent communication, and
present the first application of these insights for a robot to efficiently
communicate its intent by selectively explaining the causal actions in an
action sequence.


title: Nineteen vortex equations and integrability

abstract:
  The class of five integrable vortex equations discussed recently by Manton is
extended so it includes the relativistic BPS Chern-Simons vortices, yielding a
total of nineteen vortex equations. Not all the nineteen vortex equations are
integrable, but four new integrable equations are discovered and we generalize
them to infinitely many sets of four integrable vortex equations, with each set
denoted by its integer order $n$. Their integrability is similar to the known
cases, but give rise to different (generalized) Baptista geometries, where the
Baptista metric is a conformal rescaling of the background metric by the Higgs
field. In particular, the Baptista manifolds have conical singularities. Where
the Jackiw-Pi, Taubes, Popov and Ambj{\o}rn-Olesen vortices have conical
deficits of $2\pi$ at each vortex zero in their Baptista manifolds, the
higher-order generalizations of these equations are also integrable with larger
constant curvatures and a $2\pi n$ conical deficit at each vortex zero. We then
generalize a superposition law, known for Taubes vortices of how to add
vortices to a known solution, to all the integrable vortex equations. We find
that although the Taubes and the Popov equations relate to themselves, the
Ambj{\o}rn-Olesen and Jackiw-Pi vortices are added by using the Baptista metric
and the Popov equation. Finally, we find many further relations between vortex
equations, e.g. we find that the Chern-Simons vortices can be interpreted as
Taubes vortices on the Baptista manifold of their own solution.


title:
MotionAug: Augmentation with Physical Correction for Human Motion
  Prediction

abstract:
  This paper presents a motion data augmentation scheme incorporating motion
synthesis encouraging diversity and motion correction imposing physical
plausibility. This motion synthesis consists of our modified Variational
AutoEncoder (VAE) and Inverse Kinematics (IK). In this VAE, our proposed
sampling-near-samples method generates various valid motions even with
insufficient training motion data. Our IK-based motion synthesis method allows
us to generate a variety of motions semi-automatically. Since these two schemes
generate unrealistic artifacts in the synthesized motions, our motion
correction rectifies them. This motion correction scheme consists of imitation
learning with physics simulation and subsequent motion debiasing. For this
imitation learning, we propose the PD-residual force that significantly
accelerates the training process. Furthermore, our motion debiasing
successfully offsets the motion bias induced by imitation learning to maximize
the effect of augmentation. As a result, our method outperforms previous
noise-based motion augmentation methods by a large margin on both Recurrent
Neural Network-based and Graph Convolutional Network-based human motion
prediction models. The code is available at
https://github.com/meaten/MotionAug.


title:
An index theorem for quarter-plane Toeplitz operators via extended
  symbols and gapped Invariants related to corner states

abstract:
  In this paper, we discuss index theory for Toeplitz operators on a discrete
quarter-plane of two-variable rational matrix function symbols. By using
Gohberg-Krein theory for matrix factorizations, we extend the symbols defined
originally on a two-dimensional torus to some three-dimensional sphere and
derive a formula to express their Fredholm indices through extended symbols.
Variants for families of (self-adjoint) Fredholm quarter-plane Toeplitz
operators and those preserving real structures are also included. For some
bulk-edge gapped single-particle Hamiltonians of finite hopping range on a
discrete lattice with a codimension-two right angle corner, topological
invariants related to corner states are provided through extensions of bulk
Hamiltonians.


title: Time and the Value of Data

abstract:
  Managers often believe that collecting more data will continually improve the
accuracy of their machine learning models. However, we argue in this paper that
when data lose relevance over time, it may be optimal to collect a limited
amount of recent data instead of keeping around an infinite supply of older
(less relevant) data. In addition, we argue that increasing the stock of data
by including older datasets may, in fact, damage the model's accuracy.
Expectedly, the model's accuracy improves by increasing the flow of data
(defined as data collection rate); however, it requires other tradeoffs in
terms of refreshing or retraining machine learning models more frequently.
  Using these results, we investigate how the business value created by machine
learning models scales with data and when the stock of data establishes a
sustainable competitive advantage. We argue that data's time-dependency weakens
the barrier to entry that the stock of data creates. As a result, a competing
firm equipped with a limited (yet sufficient) amount of recent data can develop
more accurate models. This result, coupled with the fact that older datasets
may deteriorate models' accuracy, suggests that created business value doesn't
scale with the stock of available data unless the firm offloads less relevant
data from its data repository. Consequently, a firm's growth policy should
incorporate a balance between the stock of historical data and the flow of new
data.
  We complement our theoretical results with an experiment. In the experiment,
we empirically measure the loss in the accuracy of a next word prediction model
trained on datasets from various time periods. Our empirical measurements
confirm the economic significance of the value decline over time. For example,
100MB of text data, after seven years, becomes as valuable as 50MB of current
data for the next word prediction task.


title: False Negative Awareness in Indicator-based Caching Systems

abstract:
  Distributed caching systems such as content distribution networks often
advertise their content via lightweight approximate indicators (e.g., Bloom
filters) to efficiently inform clients where each datum is likely cached. While
false-positive indications are necessary and well understood, most existing
works assume no false-negative indications. Our work illustrates practical
scenarios where false-negatives are unavoidable and ignoring them significantly
impacts system performance. Specifically, we focus on false-negatives induced
by indicator staleness, which arises whenever the system advertises the
indicator only periodically, rather than immediately reporting every change in
the cache. Such scenarios naturally occur, e.g., in bandwidth-constraint
environments or when latency impedes each client's ability to obtain an updated
indicator. Our work introduces novel false-negative aware access policies that
continuously estimate the false-negative ratio and sometimes access caches
despite negative indications. We present optimal policies for homogeneous
settings and provide approximation guarantees for our algorithms in
heterogeneous environments. We further perform an extensive simulation study
with multiple real system traces. We show that our false-negative aware
algorithms incur a significantly lower access cost than existing approaches or
match the cost of these approaches while requiring an order of magnitude fewer
resources (e.g., caching capacity or bandwidth).


title:
Semi-toric and toroidal compactifications as log minimal models, and
  applications to weak K-moduli

abstract:
  We give a characterization of toroidal (resp., semi-toric) compactifications
due to Ash-Mumford-Rapoport-Tai (resp., Looijenga) as log minimal models and
apply it to study weak K-moduli compactifications, giving a different proof to
a theorem of Alexeev-Engel.
  We also discuss towards further generalization, in particular revisit
Shah-Sterk compactification of moduli of polarized Enriques surfaces to show
compatibility with log K-stability.


title: DRAG: Dynamic Region-Aware GCN for Privacy-Leaking Image Detection

abstract:
  The daily practice of sharing images on social media raises a severe issue
about privacy leakage. To address the issue, privacy-leaking image detection is
studied recently, with the goal to automatically identify images that may leak
privacy. Recent advance on this task benefits from focusing on crucial objects
via pretrained object detectors and modeling their correlation. However, these
methods have two limitations: 1) they neglect other important elements like
scenes, textures, and objects beyond the capacity of pretrained object
detectors; 2) the correlation among objects is fixed, but a fixed correlation
is not appropriate for all the images. To overcome the limitations, we propose
the Dynamic Region-Aware Graph Convolutional Network (DRAG) that dynamically
finds out crucial regions including objects and other important elements, and
models their correlation adaptively for each input image. To find out crucial
regions, we cluster spatially-correlated feature channels into several
region-aware feature maps. Further, we dynamically model the correlation with
the self-attention mechanism and explore the interaction among the regions with
a graph convolutional network. The DRAG achieved an accuracy of 87% on the
largest dataset for privacy-leaking image detection, which is 10 percentage
points higher than the state of the art. The further case study demonstrates
that it found out crucial regions containing not only objects but other
important elements like textures.


title:
To train or not to train adversarially: A study of bias mitigation
  strategies for speaker recognition

abstract:
  Speaker recognition is increasingly used in several everyday applications
including smart speakers, customer care centers and other speech-driven
analytics. It is crucial to accurately evaluate and mitigate biases present in
machine learning (ML) based speech technologies, such as speaker recognition,
to ensure their inclusive adoption. ML fairness studies with respect to various
demographic factors in modern speaker recognition systems are lagging compared
to other human-centered applications such as face recognition. Existing studies
on fairness in speaker recognition systems are largely limited to evaluating
biases at specific operating points of the systems, which can lead to false
expectations of fairness. Moreover, there are only a handful of bias mitigation
strategies developed for speaker recognition systems. In this paper, we
systematically evaluate the biases present in speaker recognition systems with
respect to gender across a range of system operating points. We also propose
adversarial and multi-task learning techniques to improve the fairness of these
systems. We show through quantitative and qualitative evaluations that the
proposed methods improve the fairness of ASV systems over baseline methods
trained using data balancing techniques. We also present a fairness-utility
trade-off analysis to jointly examine fairness and the overall system
performance. We show that although systems trained using adversarial techniques
improve fairness, they are prone to reduced utility. On the other hand,
multi-task methods can improve the fairness while retaining the utility. These
findings can inform the choice of bias mitigation strategies in the field of
speaker recognition.


title:
Improving the Transferability of Targeted Adversarial Examples through
  Object-Based Diverse Input

abstract:
  The transferability of adversarial examples allows the deception on black-box
models, and transfer-based targeted attacks have attracted a lot of interest
due to their practical applicability. To maximize the transfer success rate,
adversarial examples should avoid overfitting to the source model, and image
augmentation is one of the primary approaches for this. However, prior works
utilize simple image transformations such as resizing, which limits input
diversity. To tackle this limitation, we propose the object-based diverse input
(ODI) method that draws an adversarial image on a 3D object and induces the
rendered image to be classified as the target class. Our motivation comes from
the humans' superior perception of an image printed on a 3D object. If the
image is clear enough, humans can recognize the image content in a variety of
viewing conditions. Likewise, if an adversarial example looks like the target
class to the model, the model should also classify the rendered image of the 3D
object as the target class. The ODI method effectively diversifies the input by
leveraging an ensemble of multiple source objects and randomizing viewing
conditions. In our experimental results on the ImageNet-Compatible dataset,
this method boosts the average targeted attack success rate from 28.3% to 47.0%
compared to the state-of-the-art methods. We also demonstrate the applicability
of the ODI method to adversarial examples on the face verification task and its
superior performance improvement. Our code is available at
https://github.com/dreamflake/ODI.


title:
1D/2D hybrid Te/Graphene and Te/MoS2: multifaceted broadband photonics
  and green energy applications

abstract:
  In this letter, we highlight the enhanced functionalization of the electronic
and optical properties in the hybrid heterojunction of 1D Tellurene with 2D
monolayer of Graphene and MoS2 in both lateral and vertical geometries, having
potential applications in the field of photonics and energy harvesting. The
structural geometries of the lateral and vertical assemblies are optimized with
a comparative and systematic analysis of the energetics of the different
positional placement of the 1D system with respect to the hexagonal 2D layer.
The 1D/2D coupling of the electronic structure in this unique assembly enables
the realization of the four different types of heterojunctions, viz. type I,
type II, Z-scheme and Schottky type, with the band-alignments being entirely
dependent upon the stacking geometry of 1D Tellurene with respect to the 2D
monolayer. With the static and time-dependent first-principles calculations, we
indicate the potential applications of these hybrid systems in broadband photo
detection and absorption, covering the full range of Infra-red to visible
(IR-Vis) spectrum and in green energy harvesting with an effective separation
and migration of photo-generated charge carriers.


title: Are Vision Transformers Robust to Spurious Correlations?

abstract:
  Deep neural networks may be susceptible to learning spurious correlations
that hold on average but not in atypical test samples. As with the recent
emergence of vision transformer (ViT) models, it remains underexplored how
spurious correlations are manifested in such architectures. In this paper, we
systematically investigate the robustness of vision transformers to spurious
correlations on three challenging benchmark datasets and compare their
performance with popular CNNs. Our study reveals that when pre-trained on a
sufficiently large dataset, ViT models are more robust to spurious correlations
than CNNs. Key to their success is the ability to generalize better from the
examples where spurious correlations do not hold. Further, we perform extensive
ablations and experiments to understand the role of the self-attention
mechanism in providing robustness under spuriously correlated environments. We
hope that our work will inspire future research on further understanding the
robustness of ViT models.


title: Conversational Recommendation: A Grand AI Challenge

abstract:
  Animated avatars, which look and talk like humans, are iconic visions of the
future of AI-powered systems. Through many sci-fi movies we are acquainted with
the idea of speaking to such virtual personalities as if they were humans.
Today, we talk more and more to machines like Apple's Siri, e.g., to ask them
for the weather forecast. However, when asked for recommendations, e.g., for a
restaurant to go to, the limitations of such devices quickly become obvious.
They do not engage in a conversation to find out what we might prefer, they
often do not provide explanations for what they recommend, and they may have
difficulties remembering what was said one minute earlier. Conversational
recommender systems promise to address these limitations. In this paper, we
review existing approaches to build such systems, which developments we observe
today, which challenges are still open and why the development of
conversational recommenders represents one of the next grand challenges of AI.


title:
ERNIE-GeoL: A Geography-and-Language Pre-trained Model and its
  Applications in Baidu Maps

abstract:
  Pre-trained models (PTMs) have become a fundamental backbone for downstream
tasks in natural language processing and computer vision. Despite initial gains
that were obtained by applying generic PTMs to geo-related tasks at Baidu Maps,
a clear performance plateau over time was observed. One of the main reasons for
this plateau is the lack of readily available geographic knowledge in generic
PTMs. To address this problem, in this paper, we present ERNIE-GeoL, which is a
geography-and-language pre-trained model designed and developed for improving
the geo-related tasks at Baidu Maps. ERNIE-GeoL is elaborately designed to
learn a universal representation of geography-language by pre-training on
large-scale data generated from a heterogeneous graph that contains abundant
geographic knowledge. Extensive quantitative and qualitative experiments
conducted on large-scale real-world datasets demonstrate the superiority and
effectiveness of ERNIE-GeoL. ERNIE-GeoL has already been deployed in production
at Baidu Maps since April 2021, which significantly benefits the performance of
a wide range of downstream tasks. This demonstrates that ERNIE-GeoL can serve
as a fundamental backbone for geo-related tasks.


title: Time Dependency, Data Flow, and Competitive Advantage

abstract:
  Data is fundamental to machine learning-based products and services and is
considered strategic due to its externalities for businesses, governments,
non-profits, and more generally for society. It is renowned that the value of
organizations (businesses, government agencies and programs, and even
industries) scales with the volume of available data. What is often less
appreciated is that the data value in making useful organizational predictions
will range widely and is prominently a function of data characteristics and
underlying algorithms.
  In this research, our goal is to study how the value of data changes over
time and how this change varies across contexts and business areas (e.g. next
word prediction in the context of history, sports, politics). We focus on data
from Reddit.com and compare the value's time-dependency across various Reddit
topics (Subreddits). We make this comparison by measuring the rate at which
user-generated text data loses its relevance to the algorithmic prediction of
conversations. We show that different subreddits have different rates of
relevance decline over time.
  Relating the text topics to various business areas of interest, we argue that
competing in a business area in which data value decays rapidly alters
strategies to acquire competitive advantage. When data value decays rapidly,
access to a continuous flow of data will be more valuable than access to a
fixed stock of data. In this kind of setting, improving user engagement and
increasing user-base help creating and maintaining a competitive advantage.


title:
Contrastive Learning with Positive-Negative Frame Mask for Music
  Representation

abstract:
  Self-supervised learning, especially contrastive learning, has made an
outstanding contribution to the development of many deep learning research
fields. Recently, researchers in the acoustic signal processing field noticed
its success and leveraged contrastive learning for better music representation.
Typically, existing approaches maximize the similarity between two distorted
audio segments sampled from the same music. In other words, they ensure a
semantic agreement at the music level. However, those coarse-grained methods
neglect some inessential or noisy elements at the frame level, which may be
detrimental to the model to learn the effective representation of music.
Towards this end, this paper proposes a novel Positive-nEgative frame mask for
Music Representation based on the contrastive learning framework, abbreviated
as PEMR. Concretely, PEMR incorporates a Positive-Negative Mask Generation
module, which leverages transformer blocks to generate frame masks on the
Log-Mel spectrogram. We can generate self-augmented negative and positive
samples by masking important components or inessential components,
respectively. We devise a novel contrastive learning objective to accommodate
both self-augmented positives/negatives sampled from the same music. We conduct
experiments on four public datasets. The experimental results of two
music-related downstream tasks, music classification, and cover song
identification, demonstrate the generalization ability and transferability of
music representation learned by PEMR.


title:
Large global solutions of the parabolic-parabolic Keller-Segel system in
  higher dimensions

abstract:
  We study the global existence of the parabolic-parabolic Keller-Segel system
in $\R^d , d \ge 2$. We prove that initial data of arbitrary size give rise to
global solutions provided the diffusion parameter $\tau$ is large enough in the
equation for the chemoattractant. This fact was observed before in the
two-dimensional case by Biler, Guerra \& Karch (2015) and Corrias, Escobedo \&
Matos (2014). Our analysis improves earlier results and extends them to any
dimension $d \ge 3$. Our size conditions on the initial data for the global
existence of solutions seem to be optimal, up to a logarithmic factor in
$\tau$, when $\tau>>1$: we illustrate this fact by introducing two toy models,
both consisting of systems of two parabolic equations, obtained after a slight
modification of the nonlinearity of the usual Keller-Segel system. For these
toy models, we establish in a companion paper [4] finite time blowup for a
class of large solutions.


title: Function Field Analogue of Shimura's Conjecture on Period Symbols

abstract:
  In this paper we introduce the notion of Shimura's period symbols over
function fields in positive characteristic and establish their fundamental
properties. We further formulate and prove a function field analogue of
Shimura's conjecture on the algebraic independence of period symbols. Our
results enable us to verify the algebraic independence of the coordinates of
any nonzero period vector of an abelian t-module with complex multiplication
whose CM type is non-degenerate and defined over an algebraic function field.
This is an extension of Yu's work on Hilbert-Blumenthal t-modules.


title: Feature-informed Latent Space Regularization for Music Source Separation

abstract:
  The integration of additional side information to improve music source
separation has been investigated numerous times, e.g., by adding features to
the input or by adding learning targets in a multi-task learning scenario.
These approaches, however, require additional annotations such as musical
scores, instrument labels, etc. in training and possibly during inference. The
available datasets for source separation do not usually provide these
additional annotations. In this work, we explore transfer learning strategies
to incorporate VGGish features with a state-of-the-art source separation model;
VGGish features are known to be a very condensed representation of audio
content and have been successfully used in many MIR tasks. We introduce three
approaches to incorporate the features, including two latent space
regularization methods and one naive concatenation method. Experimental results
show that our proposed approaches improve several evaluation metrics for music
source separation.


title:
Geometric morphisms between toposes of monoid actions: factorization
  systems

abstract:
  Let M, N be monoids, and PSh(M), PSh(N) their respective categories of right
actions on sets. In this paper, we systematically investigate correspondences
between properties of geometric morphisms PSh(M) $\rightarrow$ PSh(N) and
properties of the semigroup homomorphisms M $\rightarrow$ N or
flat-left-N-right-M-sets inducing them. More specifically, we consider
properties of geometric morphisms featuring in factorization systems, namely:
surjections, inclusions, localic morphisms, hyperconnected morphisms,
terminal-connected morphisms, {\'e}tale morphisms, pure morphisms and complete
spreads. We end with an application to topos-theoretic Galois theory to the
special case of toposes of the form PSh(M).


title: An Empirical Study of Bugs in Eclipse Stable Internal Interfaces

abstract:
  The Eclipse framework is a popular and widely used framework that has been
evolving for over a decade. The framework provides both stable interfaces
(APIs) and unstable interfaces (non-APIs). Despite being discouraged by
Eclipse, application developers often use non-APIs which cause their systems to
fail when ported to new framework releases. Previous studies showed that
applications using relatively old non-APIs are more likely to be compatible
with new releases compared to the ones that used newly introduced non-APIs.
Furthermore, from our previous study about the stability of Eclipse internal
interfaces, we discovered that there exist 327K stable non-API methods as the
Eclipse framework evolves. In the same study, we recommended that 327K stable
non-API methods can be used by Eclipse interface providers as possible
candidates for promotion to stable interfaces. However since non-APIs are
unsupported and considered to be immature i.e. can contain bugs, to this end
there exist a need to first investigate the stable non-APIs for possible bugs
before they can be promoted to APIs. In this study, we empirically investigated
the stable non-API for possible bugs using Sonarqube software quality tool. We
discovered that over 79.8% classes containing old stable non-APIs methods have
zero bugs. These results can be used by both interface providers and users as a
starting point to analyze which interfaces are well tested and also estimate
how much work could be involved when performing bug fixing for a given eclipse
release.


title: Mutual Generative Transformer Learning for Cross-view Geo-localization

abstract:
  Cross-view geo-localization (CVGL), which aims to estimate the geographical
location of the ground-level camera by matching against enormous geo-tagged
aerial (e.g., satellite) images, remains extremely challenging due to the
drastic appearance differences across views. Existing methods mainly employ
Siamese-like CNNs to extract global descriptors without examining the mutual
benefits between the two modes. In this paper, we present a novel approach
using cross-modal knowledge generative tactics in combination with transformer,
namely mutual generative transformer learning (MGTL), for CVGL. Specifically,
MGTL develops two separate generative modules--one for aerial-like knowledge
generation from ground-level semantic information and vice versa--and fully
exploits their mutual benefits through the attention mechanism. Experiments on
challenging public benchmarks, CVACT and CVUSA, demonstrate the effectiveness
of the proposed method compared to the existing state-of-the-art models.


title: Type-Driven Multi-Turn Corrections for Grammatical Error Correction

abstract:
  Grammatical Error Correction (GEC) aims to automatically detect and correct
grammatical errors. In this aspect, dominant models are trained by
one-iteration learning while performing multiple iterations of corrections
during inference. Previous studies mainly focus on the data augmentation
approach to combat the exposure bias, which suffers from two drawbacks. First,
they simply mix additionally-constructed training instances and original ones
to train models, which fails to help models be explicitly aware of the
procedure of gradual corrections. Second, they ignore the interdependence
between different types of corrections. In this paper, we propose a Type-Driven
Multi-Turn Corrections approach for GEC. Using this approach, from each
training instance, we additionally construct multiple training instances, each
of which involves the correction of a specific type of errors. Then, we use
these additionally-constructed training instances and the original one to train
the model in turn. Experimental results and in-depth analysis show that our
approach significantly benefits the model training. Particularly, our enhanced
model achieves state-of-the-art single-model performance on English GEC
benchmarks. We release our code at Github.


title:
Global Convergence of MAML and Theory-Inspired Neural Architecture
  Search for Few-Shot Learning

abstract:
  Model-agnostic meta-learning (MAML) and its variants have become popular
approaches for few-shot learning. However, due to the non-convexity of deep
neural nets (DNNs) and the bi-level formulation of MAML, the theoretical
properties of MAML with DNNs remain largely unknown. In this paper, we first
prove that MAML with over-parameterized DNNs is guaranteed to converge to
global optima at a linear rate. Our convergence analysis indicates that MAML
with over-parameterized DNNs is equivalent to kernel regression with a novel
class of kernels, which we name as Meta Neural Tangent Kernels (MetaNTK). Then,
we propose MetaNTK-NAS, a new training-free neural architecture search (NAS)
method for few-shot learning that uses MetaNTK to rank and select
architectures. Empirically, we compare our MetaNTK-NAS with previous NAS
methods on two popular few-shot learning benchmarks, miniImageNet, and
tieredImageNet. We show that the performance of MetaNTK-NAS is comparable or
better than the state-of-the-art NAS method designed for few-shot learning
while enjoying more than 100x speedup. We believe the efficiency of MetaNTK-NAS
makes itself more practical for many real-world tasks.


title:
MuKEA: Multimodal Knowledge Extraction and Accumulation for
  Knowledge-based Visual Question Answering

abstract:
  Knowledge-based visual question answering requires the ability of associating
external knowledge for open-ended cross-modal scene understanding. One
limitation of existing solutions is that they capture relevant knowledge from
text-only knowledge bases, which merely contain facts expressed by first-order
predicates or language descriptions while lacking complex but indispensable
multimodal knowledge for visual understanding. How to construct vision-relevant
and explainable multimodal knowledge for the VQA scenario has been less
studied. In this paper, we propose MuKEA to represent multimodal knowledge by
an explicit triplet to correlate visual objects and fact answers with implicit
relations. To bridge the heterogeneous gap, we propose three objective losses
to learn the triplet representations from complementary views: embedding
structure, topological relation and semantic space. By adopting a pre-training
and fine-tuning learning strategy, both basic and domain-specific multimodal
knowledge are progressively accumulated for answer prediction. We outperform
the state-of-the-art by 3.35% and 6.08% respectively on two challenging
knowledge-required datasets: OK-VQA and KRVQA. Experimental results prove the
complementary benefits of the multimodal knowledge with existing knowledge
bases and the advantages of our end-to-end framework over the existing pipeline
methods. The code is available at https://github.com/AndersonStra/MuKEA.


title:
Search for the correlations between host properties and ${\rm
  DM_{host}}$ of fast radio bursts: constraints on the baryon mass fraction in
  IGM

abstract:
  The application of fast radio bursts (FRBs) as probes to investigate
astrophysics and cosmology requires the proper modelling of the dispersion
measures of Milky Way (${\rm DM_{MW}}$) and host galaxy (${\rm DM_{host}}$).
${\rm DM_{MW}}$ can be estimated using the Milky Way electron models, such as
NE2001 model and YMW16 model. However, ${\rm DM_{host}}$ is hard to model due
to limited information on the local environment of FRBs. In this paper, using
17 well-localized FRBs, we search for the possible correlations between ${\rm
DM_{host}}$ and the properties of host galaxies, such as the redshift, the
stellar mass, the star-formation rate, the age of galaxy, the offset of FRB
site from galactic center, and the half-light radius. We find no strong
correlation between ${\rm DM_{host}}$ and any of the host property. Assuming
that ${\rm DM_{host}}$ is a constant for all host galaxies, we constrain the
fraction of baryon mass in the intergalactic medium today to be $f_{\rm
IGM,0}=0.78_{-0.19}^{+0.15}$. If we model ${\rm DM_{host}}$ as a log-normal
distribution, however, we obtain a larger value, $f_{\rm
IGM,0}=0.83_{-0.17}^{+0.12}$. Based on the limited number of FRBs, no strong
evidence for the redshift evolution of $f_{\rm IGM}$ is found.


title: Harmonic Pole Placement

abstract:
  In this paper, we propose a method to design state feedback harmonic control
laws that assign the closed loop poles of a linear harmonic model to some
desired locations. The procedure is based on the solution of an
infinite-dimensional harmonic Sylvester equation under an invertibility
constraint. We provide a sufficient condition to ensure this invertibility and
show how this infinite-dimensional Sylvester equation can be solved up to an
arbitrary small error. The results are illustrated on an unstable linear
periodic system. We also provide a counterexample to illustrate the fact that,
unlike the classical finite dimensional case, the solution of the Sylvester
equation may not be invertible in the infinite dimensional case even if an
observability condition is satisfied.


title: Graph Representation Learning with Individualization and Refinement

abstract:
  Graph Neural Networks (GNNs) have emerged as prominent models for
representation learning on graph structured data. GNNs follow an approach of
message passing analogous to 1-dimensional Weisfeiler Lehman (1-WL) test for
graph isomorphism and consequently are limited by the distinguishing power of
1-WL. More expressive higher-order GNNs which operate on k-tuples of nodes need
increased computational resources in order to process higher-order tensors.
Instead of the WL approach, in this work, we follow the classical approach of
Individualization and Refinement (IR), a technique followed by most practical
isomorphism solvers. Individualization refers to artificially distinguishing a
node in the graph and refinement is the propagation of this information to
other nodes through message passing. We learn to adaptively select nodes to
individualize and to aggregate the resulting graphs after refinement to help
handle the complexity. Our technique lets us learn richer node embeddings while
keeping the computational complexity manageable. Theoretically, we show that
our procedure is more expressive than the 1-WL test. Experiments show that our
method outperforms prominent 1-WL GNN models as well as competitive
higher-order baselines on several benchmark synthetic and real datasets.
Furthermore, our method opens new doors for exploring the paradigm of learning
on graph structures with individualization and refinement.


title:
Covid19 Reproduction Number: Credibility Intervals by Blockwise Proximal
  Monte Carlo Samplers

abstract:
  Monitoring the Covid19 pandemic constitutes a critical societal stake that
received considerable research efforts. The intensity of the pandemic on a
given territory is efficiently measured by the reproduction number, quantifying
the rate of growth of daily new infections. Recently, estimates for the time
evolution of the reproduction number were produced using an inverse problem
formulation with a nonsmooth functional minimization. While it was designed to
be robust to the limited quality of the Covid19 data (outliers, missing
counts), the procedure lacks the ability to output credibility interval based
estimates. This remains a severe limitation for practical use in actual
pandemic monitoring by epidemiologists that the present work aims to overcome
by use of Monte Carlo sampling. After interpretation of the functional into a
Bayesian framework, several sampling schemes are tailored to adjust the
nonsmooth nature of the resulting posterior distribution. The originality of
the devised algorithms stems from combining a Langevin Monte Carlo sampling
scheme with Proximal operators. Performance of the new algorithms in producing
relevant credibility intervals for the reproduction number estimates and
denoised counts are compared. Assessment is conducted on real daily new
infection counts made available by the Johns Hopkins University. The interest
of the devised monitoring tools are illustrated on Covid19 data from several
different countries.


title:
Stability and upper bounds for statistical estimation of unbalanced
  transport potentials

abstract:
  In this note, we derive upper-bounds on the statistical estimation rates of
unbalanced optimal transport (UOT) maps for the quadratic cost. Our work relies
on the stability of the semi-dual formulation of optimal transport (OT)
extended to the unbalanced case. Depending on the considered variant of UOT,
our stability result interpolates between the OT (balanced) case where the
semi-dual is only locally strongly convex with respect the Sobolev semi-norm H1
dot and the case where it is locally strongly convex with respect to the H 1
norm. When the optimal potential belongs to a certain class C with sufficiently
low metric-entropy, local strong convexity enables us to recover
super-parametric rates, faster than 1 / root n.


title: Recent mass ejection from AGB star W Hya

abstract:
  We analyse archival ALMA observations of two molecular line emissions,
$^{12}$CO(3-2) and $^{29}$SiO(8-7), from oxygen-rich AGB star W Hya. Together
with results of earlier VLT observations at visible and infrared wavelengths,
our results suggest a two-component picture of the morpho-kinematics of the
circumstellar envelope (CSE), one stable over time, at the scale of centuries,
and the other variable, at the scale of years. The stable component consists of
an approximately spherical shell of gas and dust expanding radially to a
terminal velocity of $\sim$5 km s$^{-1}$ at a distance of $\sim$30 au from the
star. It is found to display comparable features as seen in the CSE of R Dor, a
star similar to W Hya. The variable component projects on the plane of the sky
over a region confined to the neighbourhood of the star and elongated toward
the north. Its very high density and sudden acceleration suggest an
interpretation in terms of mass ejection initiated a few years ago. We discuss
its properties in relation with earlier observations of dust formation in the
same region. Our results have an impact on the evidence published earlier for
the presence of CO masers. They favour an interpretation in terms of convective
cell ejections playing the main role in the generation of the nascent wind, the
stable component of the CSE being seen as the result of many successive such
events occurring in different directions at short time intervals.


title:
Effects of dense matter on the tidal deformations of binary neutron star
  inspirals and gravitational waves

abstract:
  The role of the dense matter properties on the tidal deformability and
gravitational waveforms of binary neutron stars is studied using a set of
unified equations of state. Based on the nuclear energy-density functional
theory, these equations of state provide a thermodynamically consistent
treatment of all regions of the stars and were calculated using functionals
that were precision fitted to experimental and theoretical nuclear data.


title:
Resonances and Phase Locking Phenomena for Foliation Preserving Torus
  Maps

abstract:
  It is well known for experts that resonances in nonlinear systems lead to new
invariant objects that lead to new behaviors.
  The goal of this paper is to study the invariant sets generated by resonances
under foliation preserving torus maps. That is torus which preserve a foliation
of irrational lines $L_{\theta_{0}}=\{\theta_{0}+\Omega t |
t\in\mathbb{R}\}\subset\mathbb{T}^{d}$.
  Foliation preserving maps appear naturally as reparametrization of linear
flows in the torus and also play an important role in several applications
involving coupled oscillators, delay equations, resonators with moving walls,
etc. The invariant objects we find here, lead to predictions on the behavior of
these models.
  Since the results of this paper are meant to be applied for other problems,
we have developed very quantitative results giving very explicit descriptions
of the phenomena and the invariant objects that control them.
  The structure of the phase locking regions for foliation preserving maps is
very different than for generic maps of the torus. Indeed, for the sake of
completeness, we have developed similar analysis for the case of generic maps
of the torus and shown that the objects that appear in foliation preserving
maps are quantitatively and qualitatively different from those of generic torus
maps. This has consequences in applications.


title: The Qualification of GEM detector and its application to Imaging

abstract:
  The Gas Electron Multiplier (GEM) is a new age detector, which can handle the
high flux of particles. The GEM foil, which is constructed using 50$\mu$m
highly insulating foil (Kapton/Apical) coated with 5$\mu$m layers of copper, on
both sides, with a network of specifically shaped holes is the major component
of these detectors. The European Center for Nuclear Research (CERN) has been
the sole supplier of the GEM foils until recently when a few other companies
started manufacturing GEM foils under the transfer of technology (TOT) agrement
from CERN. Techtra is one such company in Europe which gained a right to use
CERN developed technology in order to produce commercially viable GEM foils.
Micropack Pvt. Ltd. is another company in India which has successfully
manufactured good quality GEM foils. Due to the microscopic structure of holes
and dependence on the electric field inside, it becomes essential to study the
defect and uniformity of holes along with the electrical property of foils
under ambient conditions. In this work we are reporting the tests condition of
Techtra GEM foils. We report on the development of a cost effective and
efficient technique to study the GEM foils holes geometry, distribution, and
defects. We also report on the electrical properties of these foils like
leakage current, stability, and discharges. At the detector level, we describe
the high voltage (HV) response, gain, uniformity, and stability. The GEMs have
been proposed to have a wider applications, so we performed a feasibility study
to utilize these for the imaging. We irrediated various objects of varying
density with X-rays and reconstructed the images. The reconstructed image shows
a good distinction between materials of different densities, which can be very
useful in various applications like medical imaging or cargo imaging.


title: Prediction of speech intelligibility with DNN-based performance measures

abstract:
  This paper presents a speech intelligibility model based on automatic speech
recognition (ASR), combining phoneme probabilities from deep neural networks
(DNN) and a performance measure that estimates the word error rate from these
probabilities. This model does not require the clean speech reference nor the
word labels during testing as the ASR decoding step, which finds the most
likely sequence of words given phoneme posterior probabilities, is omitted. The
model is evaluated via the root-mean-squared error between the predicted and
observed speech reception thresholds from eight normal-hearing listeners. The
recognition task consists of identifying noisy words from a German matrix
sentence test. The speech material was mixed with eight noise maskers covering
different modulation types, from speech-shaped stationary noise to a
single-talker masker. The prediction performance is compared to five
established models and an ASR-model using word labels. Two combinations of
features and networks were tested. Both include temporal information either at
the feature level (amplitude modulation filterbanks and a feed-forward network)
or captured by the architecture (mel-spectrograms and a time-delay deep neural
network, TDNN). The TDNN model is on par with the DNN while reducing the number
of parameters by a factor of 37; this optimization allows parallel streams on
dedicated hearing aid hardware as a forward-pass can be computed within the
10ms of each frame. The proposed model performs almost as well as the
label-based model and produces more accurate predictions than the baseline
models.


title: Active Visuo-Haptic Object Shape Completion

abstract:
  Recent advancements in object shape completion have enabled impressive object
reconstructions using only visual input. However, due to self-occlusion, the
reconstructions have high uncertainty in the occluded object parts, which
negatively impacts the performance of downstream robotic tasks such as
grasping. In this work, we propose an active visuo-haptic shape completion
method called Act-VH that actively computes where to touch the objects based on
the reconstruction uncertainty. Act-VH reconstructs objects from point clouds
and calculates the reconstruction uncertainty using IGR, a recent
state-of-the-art implicit surface deep neural network. We experimentally
evaluate the reconstruction accuracy of Act-VH against five baselines in
simulation and in the real world. We also propose a new simulation environment
for this purpose. The results show that Act-VH outperforms all baselines and
that an uncertainty-driven haptic exploration policy leads to higher
reconstruction accuracy than a random policy and a policy driven by Gaussian
Process Implicit Surfaces. As a final experiment, we evaluate Act-VH and the
best reconstruction baseline on grasping 10 novel objects. The results show
that Act-VH reaches a significantly higher grasp success rate than the baseline
on all objects. Together, this work opens up the door for using active
visuo-haptic shape completion in more complex cluttered scenes.


title:
The methanol emission in the $J_1-J_0$ A$^{-+}$ line series as a tracer
  of specific physical conditions in high-mass star-forming regions

abstract:
  We present results of the investigations of the properties of the methanol
$J_1 - J_0$ A$^{-+}$ line series motivated by the recent serendipitous
detection of the maser emission in the $14_1 - 14_0$ A$^{-+}$ line at 349~GHz
in S255IR-SMA1 soon after the accretion burst. The study includes further
observations of several lines of this series in S255IR with the SMA, a
mini-survey of methanol lines in the 0.8 mm range toward a sample of bright
6.7~GHz methanol maser sources with the IRAM-30m telescope, and theoretical
modeling. We found that the maser component of the $14_1 - 14_0$ A$^{-+}$ line
in S255IR decayed by more than order of magnitude in comparison with that in
2016. No clear sign of maser emission is observed in other lines of this series
in the SMA observations except the $7_1 - 7_0$ A$^{-+}$ line where an
additional bright component is detected at the velocity of the maser emission
observed earlier in the $14_1 - 14_0$ A$^{-+}$ line. Our LVG model constrains
the ranges of the physical parameters that matches the observed emission
intensities. No obvious maser emission in the $J_1 - J_0$ A$^{-+}$ lines was
detected in the mini-survey of the 6.7~GHz methanol maser sources, though one
component in NGC7538 may represent a weak maser. In general, the maser effect
in the $J_1 - J_0$ A$^{-+}$ lines may serve as a tracer of rather hot
environments and in particular luminosity flaring events during high mass star
formation.


title: Optimal Rejection Function Meets Character Recognition Tasks

abstract:
  In this paper, we propose an optimal rejection method for rejecting ambiguous
samples by a rejection function. This rejection function is trained together
with a classification function under the framework of Learning-with-Rejection
(LwR). The highlights of LwR are: (1) the rejection strategy is not heuristic
but has a strong background from a machine learning theory, and (2) the
rejection function can be trained on an arbitrary feature space which is
different from the feature space for classification. The latter suggests we can
choose a feature space that is more suitable for rejection. Although the past
research on LwR focused only on its theoretical aspect, we propose to utilize
LwR for practical pattern classification tasks. Moreover, we propose to use
features from different CNN layers for classification and rejection. Our
extensive experiments of notMNIST classification and character/non-character
classification demonstrate that the proposed method achieves better performance
than traditional rejection strategies.


title:
Self-protecting aqueous lithium-ion batteries with smart
  ther-moresponsive separators

abstract:
  Capacity degradation and destructive hazards are two core challenges for
lithium-ion batteries at high temperatures, which need to be solved urgently.
Adding flame retardants or fire extinguishing agents can only achieve one-time
self-protection in case of emergency overheating. Herein, smart self-protecting
aqueous lithium-ion batteries were developed using thermoresponsive separators
through in-situ polymerization on the hydrophilic separator. The
thermoresponsive separator will close the lithium ions transport channel at
high temperatures and reopen when the battery cools down; more importantly, the
transition is reversible. We studied the lithium salts influence on the
thermoresponsive properties of the hydrogels and selected suitable lithium salt
(LiNO3) and concentration (1 M) in the electrolyte to achieve self-protection
without sacrificing battery performance. In addition, the shut-off temperature
can be tuned by adjusting the hydrophilic and hydrophobic moiety ratio in the
hydrogel according to actual demands. This self-protecting lithium-ion battery
shows promise for smart energy storage devices with safe and extended lifespan.


title:
The UV 2175{\AA} Attenuation Bump and its Correlation with PAH Emission
  at z~2

abstract:
  The UV bump is a broad absorption feature centered at 2175{\AA} that is seen
in the attenuation/extinction curve of some galaxies, but its origin is not
well known. Here, we use a sample of 86 star-forming galaxies at z=1.7-2.7 with
deep rest-frame UV spectroscopy from the MUSE HUDF Survey to study the
connection between the strength of the observed UV 2175{\AA} bump and the
Spitzer/MIPS 24 micron photometry, which at the redshift range of our sample
probes mid-IR polycyclic aromatic hydrocarbon (PAH) emission at ~6-8 micron.
The sample has robust spectroscopic redshifts and consists of typical
main-sequence galaxies with a wide range in stellar mass (log(Mstar/Msun) ~
8.5-10.7) and star formation rates (SFRs; SFR ~ 1-100 Msun/yr). Galaxies with
MIPS detections have strong UV bumps, except for those with light-weighted ages
younger than ~100-200 Myr. We find that the UV bump amplitude does not change
with SFR at fixed stellar mass but increases with mass at fixed SFR. The UV
bump amplitude and the PAH strength (defined as mid-IR emission normalized by
SFR) are highly correlated and both also correlate strongly with stellar mass.
We interpret these correlations as the result of the mass-metallicity
relationship, such that at low metallicities PAH emission is weak due to a
lower abundance of PAH molecules. The weak or complete absence of the 2175{\AA}
bump feature on top of the underlying smooth attenuation curve at low
mass/metallicities is then expected if the PAH carriers are the main source of
the additional UV absorption.


title:
Measurement of MHD turbulence properties by synchrotron radiation
  techniques

abstract:
  It is well-known that magnetohydrodynamic (MHD) turbulence is ubiquitous in
astrophysical environments. The correct understanding of the fundamental
properties of MHD turbulence is a prerequisite for revealing many key
astrophysical processes. The development of observation-based measurement
techniques has significantly promoted MHD turbulence theory and its
implications in astrophysics. After describing the modern understanding of MHD
turbulence based on theoretical analysis and direct numerical simulations, we
review recent developments related to synchrotron fluctuation techniques.
Specifically, we comment on the validation of synchrotron fluctuation
techniques and the measurement performance of several properties of magnetic
turbulence based on data cubes from MHD turbulence simulations and
observations. Furthermore, we propose to strengthen the studies of the
magnetization and 3D magnetic field structure's measurements of interstellar
turbulence. At the same time, we also discuss the prospects of new techniques
for measuring magnetic field properties and understanding astrophysical
processes, using a large number of data cubes from the Low-Frequency Array
(LOFAR) and the Square Kilometre Array (SKA).


title:
AdaSplats: Adaptative Splats from Semantic Point Cloud for Fast and
  High-Fidelity LiDAR Simulation

abstract:
  LiDAR sensors provide rich 3D information about surrounding scenes and are
becoming increasingly important for autonomous vehicles' tasks, such as
semantic segmentation, object detection, and tracking. Being able to simulate a
LiDAR sensor will accelerate the testing, validation, and deployment of
autonomous vehicles while reducing the cost and eliminating the risks of
testing in real-world scenarios. To tackle the issue of simulating LiDAR data
with high fidelity, we present a pipeline that leverages real-world point
clouds acquired by mobile mapping systems. Point-based geometry
representations, more specifically splats, have proven their ability to
accurately model the underlying surface in very large point clouds. Showing the
limits of basic splatting, we introduce an adaptative splats generation method
that accurately models the underlying 3D geometry, especially for thin
structures. We have also developed a LiDAR simulation that is 200 times
faster-than-real-time by ray casting on GPU while focusing on efficiently
handling large point clouds. We test our LiDAR simulation in real-world
conditions, showing qualitative and quantitative results against basic
splatting and meshing, demonstrating the superiority of our modeling technique.


title: Arithmetic of Chatelet surfaces under extensions of base fields

abstract:
  For Ch\^atelet surfaces defined over number fields, we study two arithmetic
properties, the Hasse principle and weak approximation, when passing to an
extension of the base field. Generalizing a construction of Y. Liang, we show
that for an arbitrary extension of number fields $L/K,$ there is a Ch\^atelet
surface over $K$ which does not satisfy weak approximation over any
intermediate field of $L/K,$ and a Ch\^atelet surface over $K$ which satisfies
the Hasse principle over an intermediate field $L'$ if and only if $[L' : K]$
is even.


title:
Dynamic groups in complex task environments: To change or not to change
  a winning team?

abstract:
  Organisations rely upon group formation to solve complex tasks, and groups
often adapt to the demands of the task they face by changing their composition
periodically. Previous research comes to ambiguous results regarding the
effects of group adaptation on task performance. This paper aims to understand
the impact of group adaptation, defined as a process of periodically changing a
group's composition, on complex task performance and considers the moderating
role of individual learning and task complexity in this relationship. We base
our analyses on an agent-based model of adaptive groups in a complex task
environment based on the NK-framework. The results indicate that reorganising
well-performing groups might be beneficial, but only if individual learning is
restricted. However, there are also cases in which group adaptation might
unfold adverse effects. We provide extensive analyses that shed additional
light on and, thereby, help explain the ambiguous results of previous research.


title:
Thermodynamic symmetry resolved entanglement entropies in integrable
  systems

abstract:
  We develop a general approach to compute the symmetry-resolved R\'enyi and
von Neumann entanglement entropies (SREE) of thermodynamic macrostates in
interacting integrable systems. Our method is based on a combination of the
thermodynamic Bethe ansatz and the G\"artner-Ellis theorem from large deviation
theory. We derive an explicit simple formula for the von Neumann SREE, which we
show to coincide with the thermodynamic Yang-Yang entropy of an effective
macrostate determined by the charge sector. Focusing on the XXZ Heisenberg spin
chain, we test our result against iTEBD calculations for thermal states,
finding good agreement. As an application, we provide analytic predictions for
the asymptotic value of the SREE following a quantum quench.


title: EMAKG: An Enhanced Version Of The Microsoft Academic Knowledge Graph

abstract:
  Scholarly knowledge graphs are valuable sources of information in several
research fields. Despite the number of existing datasets related to
publications and researchers, resource quality, coverage and accessibility are
still limited. This article presents the Enhanced Microsoft Academic Knowledge
Graph, a large dataset of information about scientific publications and
involved entities, and the methods developed to build it. Data includes
geographical information, researchers' collaborative networks and movements
between institutions, academic-related metrics, and linguistic features. The
dataset merges information from several data sources and has high temporal and
spatial 7 coverage, allowing several use cases.


title:
Biasing Like Human: A Cognitive Bias Framework for Scene Graph
  Generation

abstract:
  Scene graph generation is a sophisticated task because there is no specific
recognition pattern (e.g., "looking at" and "near" have no conspicuous
difference concerning vision, whereas "near" could occur between entities with
different morphology). Thus some scene graph generation methods are trapped
into most frequent relation predictions caused by capricious visual features
and trivial dataset annotations. Therefore, recent works emphasized the
"unbiased" approaches to balance predictions for a more informative scene
graph. However, human's quick and accurate judgments over relations between
numerous objects should be attributed to "bias" (i.e., experience and
linguistic knowledge) rather than pure vision. To enhance the model capability,
inspired by the "cognitive bias" mechanism, we propose a novel 3-paradigms
framework that simulates how humans incorporate the label linguistic features
as guidance of vision-based representations to better mine hidden relation
patterns and alleviate noisy visual propagation. Our framework is
model-agnostic to any scene graph model. Comprehensive experiments prove our
framework outperforms baseline modules in several metrics with minimum
parameters increment and achieves new SOTA performance on Visual Genome
dataset.


title: How Many Data Samples is an Additional Instruction Worth?

abstract:
  Recently introduced instruction-paradigm empowers non-expert users to
leverage NLP resources by defining a new task in natural language.
Instruction-tuned models have significantly outperformed multitask learning
models (without instruction); however they are far from state of the art task
specific models. Conventional approaches to improve model performance via
creating large datasets with lots of task instances or architectural/training
changes in model may not be feasible for non-expert users. However, they can
write alternate instructions to represent an instruction task. Is
Instruction-augumentation helpful? We augment a subset of tasks in the expanded
version of NATURAL INSTRUCTIONS with additional instructions and find that
these significantly improve model performance (up to 35%), especially in the
low-data regime. Our results indicate that an additional instruction can be
equivalent to ~200 data samples on average across tasks.


title:
Interactions between the individual and the group level in
  organizations: The case of learning and autonomous group adaptation

abstract:
  Previous research on organizations often focuses on either the individual,
team, or organizational level. There is a lack of multidimensional research on
emergent phenomena and interactions between the mechanisms at different levels.
This paper takes a multifaceted perspective on individual learning and
autonomous group formation and adaptation. To analyze interactions between the
two levels, we introduce an agent-based model that captures an organization
with a population of heterogeneous agents who learn and are limited in their
rationality. To solve a task, agents form a group that can be adapted from time
to time. We explore organizations that promote learning and group adaptation
either simultaneously or sequentially and analyze the interactions between the
activities and the effects on performance. We observe underproportional
interactions when tasks are interdependent and show that pushing learning and
group adaptation too far might backfire and decrease performance significantly.