title:
Spin-orbit driven terahertz optical response in ferromagnetic Fe-Co-Al
  alloys

abstract:
  We study the magneto-optical properties of Fe-Co-Al ordered alloys in the
terahertz range of frequencies. We find that magnetism can modify the
reflection of light from these magnets and that this modification strongly
depends on the frequency of incoming light in the terahertz range. For example,
we find that below 10~THz Co$_3$Al has nearly constant $\sigma_{xy}$ and that
above 10~THz it is reduced by about 50 times. Furthermore, we find a strong
dependence of $\sigma_{xy}$ on chemical composition. For example, we find that
the addition of Al to Fe changes the sign of $\sigma_{xy}$, while the addition
of Co to Fe leads to non-monotonic dependence of $\sigma_{xy}$ on
Co-concentration.


title:
What Makes Good Contrastive Learning on Small-Scale Wearable-based
  Tasks?

abstract:
  Self-supervised learning establishes a new paradigm of learning
representations with much fewer or even no label annotations. Recently there
has been remarkable progress on large-scale contrastive learning models which
require substantial computing resources, yet such models are not practically
optimal for small-scale tasks. To fill the gap, we aim to study contrastive
learning on the wearable-based activity recognition task. Specifically, we
conduct an in-depth study of contrastive learning from both algorithmic-level
and task-level perspectives. For algorithmic-level analysis, we decompose
contrastive models into several key components and conduct rigorous
experimental evaluations to better understand the efficacy and rationale behind
contrastive learning. More importantly, for task-level analysis, we show that
the wearable-based signals bring unique challenges and opportunities to
existing contrastive models, which cannot be readily solved by existing
algorithms. Our thorough empirical studies suggest important practices and shed
light on future research challenges. In the meantime, this paper presents an
open-source PyTorch library \texttt{CL-HAR}, which can serve as a practical
tool for researchers. The library is highly modularized and easy to use, which
opens up avenues for exploring novel contrastive models quickly in the future.


title:
Production of $\psi (4040)$, $\psi (4160)$, and $\psi (4415)$ mesons in
  hadronic matter

abstract:
  We present the first study of production of $\psi (4040)$, $\psi (4160)$, and
$\psi (4415)$ mesons in hadronic matter. Quark interchange between two
colliding charmed mesons leads to the production of the three mesons. We
calculate unpolarized cross sections for the reactions, $D\bar{D} \to \rho R$,
$D\bar{D}^* \to \pi R$, $D\bar{D}^* \to \rho R$, $D^*\bar{D} \to \pi R$,
$D^*\bar{D} \to \rho R$, $D^*\bar{D}^* \to \pi R$, and $D^*\bar{D}^* \to \rho
R$, where $R$ stands for $\psi (4040)$, $\psi (4160)$, and $\psi (4415)$. In
the temperature region covering hadronic matter the peak cross sections of
producing $\psi (4040)$ are similar to or larger than the ones of producing
$\psi (4415)$, and the latter are generally larger than those of producing
$\psi (4160)$. With the cross sections we establish new master rate equations
for $\psi (4040)$, $\psi (4160)$, and $\psi (4415)$. The equations are solved
for central Pb-Pb collisions at $\sqrt{s_{NN}}=5.02$ TeV at the Large Hadron
Collider. Solutions of the equations show that the $\psi (4040)$ number density
at kinetic freeze-out of hadronic matter is larger than the $\psi (4415)$
number density which is larger than the $\psi (4160)$ number density.


title:
"I Don't Want People to Look At Me Differently": Designing User-Defined
  Above-the-Neck Gestures for People with Upper Body Motor Impairments

abstract:
  Recent research proposed eyelid gestures for people with upper-body motor
impairments (UMI) to interact with smartphones without finger touch. However,
such eyelid gestures were designed by researchers. It remains unknown what
eyelid gestures people with UMI would want and be able to perform. Moreover,
other above-the-neck body parts (e.g., mouth, head) could be used to form more
gestures. We conducted a user study in which 17 people with UMI designed
above-the-neck gestures for 26 common commands on smartphones. We collected a
total of 442 user-defined gestures involving the eyes, the mouth, and the head.
Participants were more likely to make gestures with their eyes and preferred
gestures that were simple, easy-to-remember, and less likely to draw attention
from others. We further conducted a survey (N=24) to validate the usability and
acceptance of these user-defined gestures. Results show that user-defined
gestures were acceptable to both people with and without motor impairments.


title:
The Ihara expression for generalized weighted zeta functions of
  Bartholdi type on finite digraphs

abstract:
  The Ihara expression of a weighted zeta function for a general finite digraph
is given. It unifies all the Ihara expressions obtained for known zeta
functions for finite digraphs. Any digraph in this paper permits multi-edges
and multi-loops.


title:
Probabilistic fair behaviors spark its boost in the Ultimatum Game: the
  strength of good Samaritans

abstract:
  Behavioral experiments on the Ultimatum Game have shown that we human beings
have remarkable preference in fair play, contradicting the predictions by the
game theory. Most of the existing models seeking for explanations, however,
strictly follow the assumption of \emph{Homo economicus} in orthodox Economics
that people are self-interested and fully rational to maximize their earnings.
Here we relax this assumption by allowing that people probabilistically choose
to be "good Samaritans", acting as fair players from time to time. For
well-mixed and homogeneously structured populations, we numerically show that
as this probability increases the level of fairness undergoes from the low
scenario abruptly to the full fairness state, where occasional fair behaviors
($\sim5\%$) are sufficient to drive the whole population to behave in the
half-half split manner. We also develop a mean-field theory, which correctly
reproduces the first-order phase transition and points out that the bistability
is an intrinsic property of this game and small fair acts lead to dramatical
change due to its bifurcation structure. Heterogeneously structured
populations, however, display continuous fairness transition; surprisingly,
very few hub nodes acting as fair players are able to entrain the whole
population to the full fairness state. Our results thus reveal the unexpected
strength of "good Samaritans", which may constitute a new explanation for the
emergence of fairness in our society.


title: Robust Learning from Observation with Model Misspecification

abstract:
  Imitation learning (IL) is a popular paradigm for training policies in
robotic systems when specifying the reward function is difficult. However,
despite the success of IL algorithms, they impose the somewhat unrealistic
requirement that the expert demonstrations must come from the same domain in
which a new imitator policy is to be learned. We consider a practical setting,
where (i) state-only expert demonstrations from the real (deployment)
environment are given to the learner, (ii) the imitation learner policy is
trained in a simulation (training) environment whose transition dynamics is
slightly different from the real environment, and (iii) the learner does not
have any access to the real environment during the training phase beyond the
batch of demonstrations given. Most of the current IL methods, such as
generative adversarial imitation learning and its state-only variants, fail to
imitate the optimal expert behavior under the above setting. By leveraging
insights from the Robust reinforcement learning (RL) literature and building on
recent adversarial imitation approaches, we propose a robust IL algorithm to
learn policies that can effectively transfer to the real environment without
fine-tuning. Furthermore, we empirically demonstrate on continuous-control
benchmarks that our method outperforms the state-of-the-art state-only IL
method in terms of the zero-shot transfer performance in the real environment
and robust performance under different testing conditions.


title: Integrable systems and crystals for edge labeled tableaux

abstract:
  We introduce the edge Schur functions $E^{\lambda}$ that are defined as a
generating series over edge labeled tableaux. We formulate $E^{\lambda}$ as the
partition function for a solvable lattice model, which we use to show they are
symmetric polynomials and derive a Cauchy-type identity with factorial Schur
polynomials. Finally, we give a crystal structure on edge labeled tableau to
give a positive Schur polynomial expansion of $E^{\lambda}$ and show it
intertwines with an uncrowding algorithm.


title: An Information-Theoretic Proof of the Kac--Bernstein Theorem

abstract:
  A short, information-theoretic proof of the Kac--Bernstein theorem, which is
stated as follows, is presented: For any independent random variables $X$ and
$Y$, if $X+Y$ and $X-Y$ are independent, then $X$ and $Y$ are normally
distributed.


title: Sign-changing bubble tower solutions for a Paneitz-type problem

abstract:
  This paper is concerned with the following biharmonic problem
\begin{equation}\label{ineq} \begin{cases} \Delta^2 u=|u|^{\frac{8}{N-4}}u
&\text{ in } \ \Omega\backslash \overline{{B(\xi_0,\varepsilon)}},
  u=\Delta u=0 &\text{ on } \ \partial (\Omega \backslash
\overline{{B(\xi_0,\varepsilon)}}), \end{cases} \end{equation} where $\Omega$
is an open bounded domain in $\mathbb{R}^N$, $N\geq 5$, and
$B(\xi_0,\varepsilon)$ is a ball centered at $\xi_0$ with radius $\varepsilon$,
$\varepsilon$ is a small positive parameter. We obtain the existence of
solutions for problem (\ref{ineq}), which is an arbitrary large number of
sign-changing solutions whose profile is a superposition of bubbles with
alternate sign which concentrate at the center of the hole.


title:
Bounds on sterile neutrino lifetime and mixing angle with active
  neutrinos by global 21 cm signal

abstract:
  Sterile neutrinos can be a possible candidate for dark matter. Sterile
neutrinos are radiatively unstable and can inject photon energy into the
intergalactic medium (IGM). The injection of photon energy into IGM can modify
the temperature and ionization history of IGM gas during cosmic dawn.
Theoretical models based on the {\Lambda}CDM framework predict an absorption
profile in the 21 cm line during the cosmic dawn era. Recently, the Experiment
to Detect the Global Epoch of Reionization Signature (EDGES) collaboration
confirmed such an adsorption signal. Injection of energy into IGM can modify
the absorption amplitude in the 21 cm signal. Considering the 21 cm absorption
signal at cosmic dawn, we constrain the lifetime of sterile neutrinos and the
mixing angle of sterile neutrinos with active neutrinos. We also compare these
bounds with other astrophysical observational bounds.


title: Applying multi product lines to equity market software ecosystem

abstract:
  Context: In recent decades, many financial markets and their participants
have changed their working method from a completely manual and traditional one
to an automatic one, benefiting from complex software systems. There are
different approaches to the development of such software systems. Objective: In
this paper, we study the application of the Multi Product Line (MPL) approach
in the software ecosystem (SECO) of the equity market. By profiting from the
concepts and practices of the MPL approach, we want to design a SECO that makes
the real-time and automated flow of financial transaction data between market
participants' software pieces possible. Method: We first provide some
background information about the equity market, its participants and their
relations, and two primary order life-cycles in which these players cooperate.
After that, we analyze the variability in each market participant's software.
Next, we describe the employed architecture and the implementation approach.
Finally, we discuss three scenarios by which the whole proposed SECO is tested
and validated. Results: To implement the mentioned working method, named
Straight-through Processing (STP), different technical and non-technical
elements' contribution is essential. Attaining success in developing the equity
market's SECO addresses the technical aspect and prepares the technical
infrastructure for the rest of the work. Conclusion: The successful validation
of the equity market's SECO indicates that the adoption of the MPL approach is
a viable strategy for the development of equity market SECOs. It also suggests
that this approach is worthy of more attention and investment.


title:
Maximizing Communication Efficiency for Large-scale Training via 0/1
  Adam

abstract:
  1-bit communication is an effective method to scale up model training, and
has been studied extensively on SGD. Its benefits, however, remain an open
question on Adam-based model training (e.g. BERT and GPT). In this paper, we
propose 0/1 Adam, which improves upon the state-of-the-art 1-bit Adam via two
novel designs: (1) adaptive variance state freezing, which eliminates the
requirement of running expensive full-precision communication at early stage of
training; (2) 1-bit sync, which allows skipping communication rounds with
bit-free synchronization over Adam's optimizer states, momentum and variance.
In theory, we provide convergence analysis for 0/1 Adam on smooth non-convex
objectives, and show the complexity bound is better than original Adam under
certain conditions. On various benchmarks such as BERT-Base/Large pretraining
and ImageNet, we demonstrate on up to 128 GPUs that 0/1 Adam is able to reduce
up to 87% of data volume, 54% of communication rounds, and achieve up to 2X
higher throughput compared to the state-of-the-art 1-bit Adam while enjoying
the same statistical convergence speed and end-to-end model accuracy on GLUE
dataset and ImageNet validation set.


title:
Incoherent phonon transport dominates heat conduction across van der
  Waals superlattices

abstract:
  Heat conduction mechanisms in superlattices could be different across
different types of interfaces. Van der Waals superlattices are structures
physically assembled through weak van der Waals interactions by design, and may
host properties beyond the traditional limits of lattice matching and
processing compatibility, offering new types of interfaces. In this work,
natural van der Waals (SnS)1.17(NbS2)n superlattices are synthesized, and their
thermal conductivities are measured by time-domain thermoreflectance as a
function of interface density. Our results show that heat conduction of
(SnS)1.17(NbS2)n superlattices is dominated by interface scattering when the
coherent length of phonons is larger than the superlattice period, indicating
incoherent phonon transport dominates cross-plane heat conduction in van der
Waals superlattices even when the period is atomically thin and abrupt.
Moreover, our result suggests that the widely accepted heat conduction
mechanism for conventional superlattices that coherent phonons dominate when
the period is short, is not applicable due to symmetry breaking in most van der
Waals superlattices. Our findings provide new insight for understanding the
thermal behavior of van der Waals superlattices, and devise approaches for
effective thermal management of superlattices depending on the distinct types
of interfaces.


title:
Generalization of the Kutta-Joukowski theorem for the hydrodynamic
  forces acting on a quantized vortex

abstract:
  The hydrodynamic forces acting on a quantized vortex in a superfluid have
long been a highly controversial issue. A new approach, originally developed in
the astrophysical context of compact stars, is presented to determine these
forces by considering small perturbations of the asymptotically uniform flows
in the region far from the vortex in the framework of Landau-Khalatnikov
two-fluid model. Focusing on the irrotational part of the flows in the
Helmholtz decomposition, the classical Kutta-Joukowski theorem from ordinary
hydrodynamics is thus generalized to superfluid systems. The same method is
applied to predict the hydrodynamic forces acting on vortices in cold atomic
condensates and superfluid mixtures.

categories: cond-mat.quant-gas cond-mat.supr-con physics.flu-dyn quant-ph


title:
Cloud-based computational model predictive control using a parallel
  multi-block ADMM approach

abstract:
  Heavy computational load for solving nonconvex problems for large-scale
systems or systems with real-time demands at each sample step has been
recognized as one of the reasons for preventing a wider application of
nonlinear model predictive control (NMPC). To improve the real-time feasibility
of NMPC with input nonlinearity, we devise an innovative scheme called
cloud-based computational model predictive control (MPC) by using an
elaborately designed parallel multi-block alternating direction method of
multipliers (ADMM) algorithm. This novel parallel multi-block ADMM algorithm is
tailored to tackle the computational issue of solving a nonconvex problem with
nonlinear constraints.


title:
A Multi-Event Study of Early-Stage SEP Acceleration by CME-Driven Shocks
  -- Sun to 1 AU

abstract:
  The solar corona below 10 solar radii is an important region for early
acceleration and transport of solar energetic particles (SEPs) by coronal mass
ejection-driven shock waves. There, these waves propagate into a highly
variable dynamic medium with steep gradients and rapidly expanding coronal
magnetic fields, which modulates the particle acceleration near the shock or
wave surfaces, and the way SEPs spread into the heliosphere. We present a study
modelling the acceleration of SEPs in global coronal shock events in the
corona, as well as their transport to 1 au, based on telescopic observations
coupled with dynamic physical models.


title:
Multi-direction and Multi-scale Pyramid in Transformer for Video-based
  Pedestrian Retrieval

abstract:
  In video surveillance, pedestrian retrieval (also called person
re-identification) is a critical task. This task aims to retrieve the
pedestrian of interest from non-overlapping cameras. Recently,
transformer-based models have achieved significant progress for this task.
However, these models still suffer from ignoring fine-grained, part-informed
information. This paper proposes a multi-direction and multi-scale Pyramid in
Transformer (PiT) to solve this problem. In transformer-based architecture,
each pedestrian image is split into many patches. Then, these patches are fed
to transformer layers to obtain the feature representation of this image. To
explore the fine-grained information, this paper proposes to apply vertical
division and horizontal division on these patches to generate
different-direction human parts. These parts provide more fine-grained
information. To fuse multi-scale feature representation, this paper presents a
pyramid structure containing global-level information and many pieces of
local-level information from different scales. The feature pyramids of all the
pedestrian images from the same video are fused to form the final
multi-direction and multi-scale feature representation. Experimental results on
two challenging video-based benchmarks, MARS and iLIDS-VID, show the proposed
PiT achieves state-of-the-art performance. Extensive ablation studies
demonstrate the superiority of the proposed pyramid structure. The code is
available at https://git.openi.org.cn/zangxh/PiT.git.


title: Towards Continuous Consistency Axiom

abstract:
  Development of new algorithms in the area of machine learning, especially
clustering, comparative studies of such algorithms as well as testing according
to software engineering principles requires availability of labeled data sets.
While standard benchmarks are made available, a broader range of such data sets
is necessary in order to avoid the problem of overfitting. In this context,
theoretical works on axiomatization of clustering algorithms, especially axioms
on clustering preserving transformations are quite a cheap way to produce
labeled data sets from existing ones. However, the frequently cited axiomatic
system of Kleinberg:2002, as we show in this paper, is not applicable for
finite dimensional Euclidean spaces, in which many algorithms like $k$-means,
operate. In particular, the so-called outer-consistency axiom fails upon making
small changes in datapoint positions and inner-consistency axiom is valid only
for identity transformation in general settings.
  Hence we propose an alternative axiomatic system, in which Kleinberg's inner
consistency axiom is replaced by a centric consistency axiom and outer
consistency axiom is replaced by motion consistency axiom. We demonstrate that
the new system is satisfiable for a hierarchical version of $k$-means with
auto-adjusted $k$, hence it is not contradictory. Additionally, as $k$-means
creates convex clusters only, we demonstrate that it is possible to create a
version detecting concave clusters and still the axiomatic system can be
satisfied. The practical application area of such an axiomatic system may be
the generation of new labeled test data from existent ones for clustering
algorithm testing. %We propose the gravitational consistency as a replacement
which does not have this deficiency.


title:
Pressure-induced ideal Weyl semimetal state in the layered
  antiferromagnet EuCd2As2

abstract:
  The rich nontrivial topological phases rooted in the interplay between
magnetism and topology in the layered antiferromagnet EuCd2As2 have captured
vast attention, especially the ideal Weyl semimetal state realized in the
spin-polarized ferromagnetic (FM) structure driven by a moderate external
magnetic field. In this work, combining high-pressure magnetotransport
measurements, structure chracterizations and first principles calculations, we
find that application of pressure can also realize the ideal Weyl state in
EuCd2As2 through driving the in-plane antiferromagnetic state across an
intermediate in-plane FM state then into the out-of-plane FM state. Our
high-pressure angle dispersive X-ray diffraction and X-ray absorption near-edge
spectroscopy measurements excluded structure transition and/or change of Eu2+
valence state as the sources for the magnetic phase transitions. Alternatively,
the apparently reduced axial ratio (c/a) and compressed Eu-layer space distance
should play important roles. Our result provides an alternative way to realize
the ideal Weyl semimetal state in EuCd2As2 and would be instructive for the
exploration of exotic topological properties in such layered magnetic
topological phase with strongly competing magnetic exchanges by using high
pressure.


title: Global Optimization via Optimal Decision Trees

abstract:
  The global optimization literature places large emphasis on reducing
intractable optimization problems into more tractable structured optimization
forms. In order to achieve this goal, many existing methods are restricted to
optimization over explicit constraints and objectives that use a subset of
possible mathematical primitives. These are limiting in real-world contexts
where more general explicit and black box constraints appear. Leveraging the
dramatic speed improvements in mixed-integer optimization (MIO) and recent
research in machine learning, we propose a new method to learn MIO-compatible
approximations of global optimization problems using optimal decision trees
with hyperplanes (OCT-Hs). This constraint learning approach only requires a
bounded variable domain, and can address both explicit and inexplicit
constraints. We solve the MIO approximation efficiently to find a near-optimal,
near-feasible solution to the global optimization problem. We further improve
the solution using a series of projected gradient descent iterations. We test
the method on a number of numerical benchmarks from the literature as well as
real-world design problems, demonstrating its promise in finding global optima
efficiently.


title: Integrating Hackathons into an Online Cybersecurity Course

abstract:
  Cybersecurity educators have widely introduced hackathons to facilitate
practical knowledge gaining in cybersecurity education. Introducing such events
into cybersecurity courses can provide valuable learning experiences for
students. The nature of the hackathon format encourages a learning-by-doing
approach, and the hackathon outcomes can serve as evidence for students
knowledge, capability and learning gains. Prior work on hackathons in education
mainly focused on collocated hackathon events in the traditional classroom
setting. These hackathon events often took place as a one-off event at the end
of the course. However, one-off hackathon events at the end of a course might
not be sufficient to improve learning. Instead, we focus on analyzing the
integration of a series of online hackathon events into an online cybersecurity
course and explore how this integration can address online education issues by
encouraging collaboration and developing a practical understanding of the
delivered course by solving real-world challenges. We evaluate interventions to
foster learning and analyze its effect on collaboration and learning gains for
students in the course. Our findings indicate that students attribute learning
benefits to the introduced interventions that supported teamwork and
collaboration, maintained student participation and interest in the course, and
encouraged learning-by-doing.


title:
New analysis of the fraction of observable nights at astronomical sites
  based on FengYun-2 satellite data

abstract:
  The fraction of observable nights is an essential parameter for selecting
astronomical sites. In recent years, meteorological satellite data have played
an essential role in recognising and providing statistics of observable nights.
We present a method to estimate the fraction of observable nights based on the
FengYun-2 series of geostationary meteorological satellites and weather records
of multiple astronomical sites. We have calculated the fraction of observable
nights at 27 sites in Indonesia and two astronomical sites in China to validate
the method. The results derived from our method show good agreement with
previous works. Furthermore, we have derived the yearly distribution of the
fraction of observable nights above China, which indicates the area near
40$^{\circ}$N has more observable nights than other areas in China.


title: Colored vertex models and $k$-tilings of the Aztec diamond

abstract:
  We study $k$-tilings ($k$-tuples of domino tilings) of the Aztec diamond of
rank $m$. We assign a weight to each $k$-tiling, depending on the number of
dominos of certain types and the number of "interactions" between the tilings.
Employing the colored vertex models introduced in earlier work to study
supersymmetric LLT polynomials, we compute the generating polynomials of the
$k$-tilings. We then prove some combinatorial results about $k$-tilings,
including a bijection between $k$-tilings with no interactions and $1$-tilings,
and we compute the arctic curves of the tilings for $t=0$ and
$t\rightarrow\infty$. We also present some lozenge $k$-tilings of the hexagon
and compute the arctic curves of the tilings for $t=0$.


title: Jarvis: Large-scale Server Monitoring with Adaptive Near-data Processing

abstract:
  Rapid detection and mitigation of issues that impact performance and
reliability is paramount for large-scale online services. For real-time
detection of such issues, datacenter operators use a stream processor and
analyze streams of monitoring data collected from servers (referred to as data
source nodes) and their hosted services. The timely processing of incoming
streams requires the network to transfer massive amounts of data, and
significant compute resources to process it. These factors often create
bottlenecks for stream analytics. To help overcome these bottlenecks, current
monitoring systems employ near-data processing by either computing an optimal
query partition based on a cost model or using model-agnostic heuristics.
Optimal partitioning is computationally expensive, while model-agnostic
heuristics are iterative and search over a large solution space. We combine
these approaches by using model-agnostic heuristics to improve the partitioning
solution from a model-based heuristic. Moreover, current systems use
operator-level partitioning: if a data source does not have sufficient
resources to execute an operator on all records, the operator is executed only
on the stream processor. Instead, we perform data-level partitioning, i.e., we
allow an operator to be executed both on a stream processor and data sources.
We implement our algorithm in a system called Jarvis, which enables quick
adaptation to dynamic resource conditions. Our evaluation on a diverse set of
monitoring workloads suggests that Jarvis converges to a stable query partition
within seconds of a change in node resource conditions. Compared to current
partitioning strategies, Jarvis handles up to 75% more data sources while
improving throughput in resource-constrained scenarios by 1.2-4.4x.


title: Fun Selfie Filters in Face Recognition: Impact Assessment and Removal

abstract:
  This work investigates the impact of fun selfie filters, which are frequently
used to modify selfies, on face recognition systems. Based on a qualitative
assessment and classification of freely available mobile applications, ten
relevant fun selfie filters are selected to create a database. To this end, the
selected filters are automatically applied to face images of public face image
databases. Different state-of-the-art methods are used to evaluate the
influence of fun selfie filters on the performance of face detection using
dlib, RetinaFace, and a COTS method, sample quality estimated by FaceQNet and
MagFace, and recognition accuracy employing ArcFace and a COTS algorithm. The
obtained results indicate that selfie filters negatively affect face
recognition modules, especially if fun selfie filters cover a large region of
the face, where the mouth, nose, and eyes are covered. To mitigate such
unwanted effects, a GAN-based selfie filter removal algorithm is proposed which
consists of a segmentation module, a perceptual network, and a generation
module. In a cross-database experiment the application of the presented selfie
filter removal technique has shown to significantly improve the biometric
performance of the underlying face recognition systems.


title:
Bearing-constrained Formation Tracking Control of Nonholonomic Agents
  without Inter-agent Communication

abstract:
  This letter presents two bearing-constrained formation tracking control
protocols for multiple nonholonomic agents based respectively on the bearing
vectors and displacements between the agents. The desired formation pattern of
the system is specified by the desired inter-agent bearing vectors. In the
proposed control schemes, there are two or more leaders moving with the same
constant velocity; the other follower agents do not have the information of the
leaders' velocity nor communicate variables with their neighbors. Under both
the proposed control laws, the system achieves the moving target formation
asymptotically. Simulation results are provided to support the theoretical
development.


title:
Diffusive limit of random walks on tessellations via generalized
  gradient flows

abstract:
  We study asymptotic limits of reversible random walks on tessellations via a
variational approach, which relies on a specific generalized-gradient-flow
formulation of the corresponding forward Kolmogorov equation. We establish
sufficient conditions on sequences of tessellations and jump intensities under
which a sequence of random walks converges to a diffusion process with a
possibly spatially-dependent diffusion tensor.


title: Lower Bounds on Lattice Covering Densities of Simplices

abstract:
  This paper presents new lower bounds for the lattice covering densities of
simplices by studying the Degree-Diameter Problem for abelian Cayley digraphs.
In particular, it proves that the density of any lattice covering of a
tetrahedron is at least $25/18$ and the density of any lattice covering of a
four-dimensional simplex is at least $343/264$.


title:
"Phase Transitions" in small systems: why standard threshold definitions
  fail for nanolasers

abstract:
  Since the development of micro- and nanolasers, the question of laser
threshold has been subject to debate. Different definitions have been used to
try and establish its occurrence, often encountering major obstacles. We
examine a set of common physical definitions which we apply to measurements
taken in a micro-VCSEL. Their predictions not only clearly disagree, pointing
to different pump values at which the laser should cross threshold, but they
also correspond to autocorrelation values which demonstrate very low field
coherence. A topological analysis of the rate equations, with average
spontaneous emission added to the lasing mode, clearly identifies the
contradictions and explains the origin of the discrepancies. Additional
considerations help understanding the failure of the approach and highlight the
path towards a unique and general definition of threshold in all lasers,
irrespective of their sizes. A critical scrutiny of the assumptions made in the
rate equations with spontaneous emission illustrates their strength and
weaknesses and better defines the bounds within which their predictions hold.
We remark in the conclusions how the main results of this paper could hold for
other small systems.


title:
End-to-end Reinforcement Learning of Robotic Manipulation with Robust
  Keypoints Representation

abstract:
  We present an end-to-end Reinforcement Learning(RL) framework for robotic
manipulation tasks, using a robust and efficient keypoints representation. The
proposed method learns keypoints from camera images as the state
representation, through a self-supervised autoencoder architecture. The
keypoints encode the geometric information, as well as the relationship of the
tool and target in a compact representation to ensure efficient and robust
learning. After keypoints learning, the RL step then learns the robot motion
from the extracted keypoints state representation. The keypoints and RL
learning processes are entirely done in the simulated environment. We
demonstrate the effectiveness of the proposed method on robotic manipulation
tasks including grasping and pushing, in different scenarios. We also
investigate the generalization capability of the trained model. In addition to
the robust keypoints representation, we further apply domain randomization and
adversarial training examples to achieve zero-shot sim-to-real transfer in
real-world robotic manipulation tasks.


title:
OctAttention: Octree-based Large-scale Contexts Model for Point Cloud
  Compression

abstract:
  In point cloud compression, sufficient contexts are significant for modeling
the point cloud distribution. However, the contexts gathered by the previous
voxel-based methods decrease when handling sparse point clouds. To address this
problem, we propose a multiple-contexts deep learning framework called
OctAttention employing the octree structure, a memory-efficient representation
for point clouds. Our approach encodes octree symbol sequences in a lossless
way by gathering the information of sibling and ancestor nodes. Expressly, we
first represent point clouds with octree to reduce spatial redundancy, which is
robust for point clouds with different resolutions. We then design a
conditional entropy model with a large receptive field that models the sibling
and ancestor contexts to exploit the strong dependency among the neighboring
nodes and employ an attention mechanism to emphasize the correlated nodes in
the context. Furthermore, we introduce a mask operation during training and
testing to make a trade-off between encoding time and performance. Compared to
the previous state-of-the-art works, our approach obtains a 10%-35% BD-Rate
gain on the LiDAR benchmark (e.g. SemanticKITTI) and object point cloud dataset
(e.g. MPEG 8i, MVUB), and saves 95% coding time compared to the voxel-based
baseline. The code is available at https://github.com/zb12138/OctAttention.


title:
Pressure-enhanced superconductivity in cage-type quasiskutterudite
  Sc5Rh6Sn18 single crystal

abstract:
  Sc5Rh6Sn18 with a cage-type quasiskutterudite crystal lattice and type II
superconductivity, with superconducting transition temperature Tc = 4.99 K, was
investigated under hydrostatic high-pressure (HP) using electrical transport,
synchrotron X-ray diffraction (XRD) and Raman spectroscopy. Our data show that
HP enhance the metallic nature and Tc of the system. Tc is found to show a
continuous increase reaching to 5.24 K at 2.5 GPa. Athough the system is
metallic in nature, Raman spectroscopy investigations at ambient pressure
revealed the presence of three weak modes at 165.97, 219.86 and 230.35 cm-1,
mostly related to the rattling atom Sc. The HP-XRD data revealed that the cage
structure was stable without any structural phase transition up to ~7 GPa. The
lattice parameters and volume exhibited a smooth decrease without any anomalies
as a function of pressure in this pressure range. In particular, a second order
Birch-Murnaghan equation of state can describe the pressure dependence of the
unit cell volume well, yielding a bulk modulus of ~ 97 GPa. HP Raman
investigations revealed a linear shift of all the three Raman modes to higher
wavenumbers with increasing pressure up to ~8 GPa. As the pressure enhances the
bond overlap, thus inducing more electronic charges into the system, HP-XRD and
Raman results may indciate the possibility of obtaining higher Tc with
increasing pressures in this pressure range.


title: Light as quantum back-action nullifying meter

abstract:
  We propose a new method to overcome quantum back-action in a measurement
process using oscillators. An optical oscillator is used as a meter to measure
the parameters of another open oscillator. The optical oscillator is
synthesized such that the optical restoring force counters any perturbations
induced by the quantum back-action phenomena. As a result, it is shown that the
quantum back-action in continuous measurement is suppressed in the low
frequency regime i.e., for frequencies much smaller than the resonance
frequency of the open oscillator. As the meter plays the role of measuring
parameters as well as suppressing the quantum back-action, we call it as
quantum back-action nullifying meter. As an application of this method,
synthesis of quantum back-action nullifying optical oscillator for suppressing
radiation pressure force noise in linear and non-linear optomechanics is
described.


title: Abelian differentials and their periods: the bi-algebraic point of view

abstract:
  We study the transcendence of periods of abelian differentials, both at the
arithmetic and functional level, from the point of view of the natural
bi-algebraic structure on strata of abelian differentials. We characterise
geometrically the arithmetic points, study their distribution, and prove that
in many cases the bi-algebraic curves are the linear ones.


title:
Metal-insulator transition via control of spin liquidity in a doped Mott
  insulator

abstract:
  Quantum spin liquid states, in which spins are quantum-mechanically
delocalized in direction, have been so far studied for charge-localized Mott
insulators arising from strong repulsive interaction. Recently, however, it was
found that the doped Mott insulator with a triangular lattice,
$\kappa$-(ET)$_4$Hg$_{2.89}$Br$_8$, exhibits both spin-liquid-like magnetism
and metallic electrical conduction. Thus, it is now possible to experimentally
explore how the spin liquidity affects the electrical conduction, an issue that
has received a great deal of theoretical attention. Here, with a newly
developed method to combine uniaxial and hydrostatic pressures, we investigate
the electrical conduction in the doped Mott insulator with controlling the
triangular lattice geometry and the repulsion strength which determines the
spin-liquidity and Mottness, respectively. We found that, in a strongly
interacting regime, the electronic state drastically changes from an insulator
to a Fermi liquid via a non-Fermi liquid with varying geometrical frustration,
which suggests that spin liquidity promotes delocalization of charges. This
result indicates that frustration in spin degrees of freedom has a decisive
impact on the transport of charges through the entanglement of spin and charge
in a doped Mott insulator.


title: Reflekt: a Library for Compile-Time Reflection in Kotlin

abstract:
  Reflection in Kotlin is a powerful mechanism to introspect program behavior
during its execution at run-time. However, among the variety of practical tasks
involving reflection, there are scenarios when the poor performance of run-time
approaches becomes a significant disadvantage. This problem manifests itself in
Kotless, a popular framework for developing serverless applications, because
the faster the applications launch, the less their cloud infrastructure costs.
In this paper, we present Reflekt - a compile-time reflection library which
allows to perform the search among classes, object expressions (which in Kotlin
are implemented as singleton classes), and functions in Kotlin code based on
the given search query. It comes with a convenient DSL and better performance
comparing to the existing run-time reflection approaches. Our experiments show
that replacing run-time reflection calls with Reflekt in serverless
applications created with Kotless resulted in a significant performance boost
in start-up time of these applications.


title: Deep Performer: Score-to-Audio Music Performance Synthesis

abstract:
  Music performance synthesis aims to synthesize a musical score into a natural
performance. In this paper, we borrow recent advances in text-to-speech
synthesis and present the Deep Performer -- a novel system for score-to-audio
music performance synthesis. Unlike speech, music often contains polyphony and
long notes. Hence, we propose two new techniques for handling polyphonic inputs
and providing a fine-grained conditioning in a transformer encoder-decoder
model. To train our proposed system, we present a new violin dataset consisting
of paired recordings and scores along with estimated alignments between them.
We show that our proposed model can synthesize music with clear polyphony and
harmonic structures. In a listening test, we achieve competitive quality
against the baseline model, a conditional generative audio model, in terms of
pitch accuracy, timbre and noise level. Moreover, our proposed model
significantly outperforms the baseline on an existing piano dataset in overall
quality.


title:
Measurement of top-quark electroweak couplings in associated top quark
  production with vector bosons at the ATLAS and CMS experiments

abstract:
  Recent analyses of top quark production in association with vector bosons are
summarized, representing the most precise inclusive and differential cross
section measurements of these processes to date. Proton-proton collision data
at a center-of-mass energy of $\sqrt{s}=13$ TeV, recorded by the ATLAS and CMS
detectors at the LHC are analyzed, corresponding to an integrated luminosity of
up to 139 fb$^{-1}$ for each experiment. Comparisons with theory calculations
are performed and overall good agreement with standard model predictions are
obtained.


title: Neural NID Rules

abstract:
  Abstract object properties and their relations are deeply rooted in human
common sense, allowing people to predict the dynamics of the world even in
situations that are novel but governed by familiar laws of physics. Standard
machine learning models in model-based reinforcement learning are inadequate to
generalize in this way. Inspired by the classic framework of noisy
indeterministic deictic (NID) rules, we introduce here Neural NID, a method
that learns abstract object properties and relations between objects with a
suitably regularized graph neural network. We validate the greater
generalization capability of Neural NID on simple benchmarks specifically
designed to assess the transition dynamics learned by the model.


title: Macro-microturbulence in the solar photosphere

abstract:
  The velocity distribution of the large and small-scale motion in solar
photosphere has been obtained by crossing method based on fitting the observed
and calculated equivalent widths as well as the central depths of the spectral
lines at the center of the and the limb of the solar disk. We used about 200 Fe
I lines. According to our results the motions in photosphere are anisotropic.
The radial component of microturbulent velocity decreases from 1.0 to 0.3 km/s
and the tangential one from 1.7 to 1.3 km/c at the photosphere heights from 200
to 500 km (log tau_5 = -1.4 and -3.5). At the same heights the radial component
of the macroturbulent velocity decreases from 1.8 to 1.2 km/s and the
tangential one from 2.3 to 0.8 km/s.


title: Rare top quark production in CMS

abstract:
  Latest results from the CMS experiment at the LHC on top quark production in
association with a Z boson or a photon, and the production of four top quarks,
are summarized. Proton-proton collision data corresponding to an integrated
luminosity of up to 138 fb$^{-1}$, collected at 13 TeV center-of-mass energy
are used. Among the measured results, most precise inclusive cross sections,
most stringent limits on models beyond the standard model and first
differential measurements are presented. Overall, good agreement with standard
model predictions is observed.


title: Application of Modular Vehicle Technology to Mitigate Bus Bunching

abstract:
  The stochastic nature of public transport systems leads to headway
variability and bus bunching, causing both operator and passenger cost to
increase significantly. Traditional strategies to counter bus bunching,
including bus-holding, stop-skipping, and bus substitution/insertion, suffer
from trade-offs and shortcomings. Autonomous modular vehicle (AMV) technology
provides an additional level of flexibility in bus dispatching and operations,
which can offer significant benefits in mitigating bus bunching compared to
strategies available with conventional buses. This paper introduces a novel
alternative to stop-skipping by leveraging the new capabilities offered by AMVs
(in particular, en-route coupling and decoupling of modular units). We develop
a simple bus-splitting strategy that directs a modular bus to decouple into
individual units when it experiences a headway longer than a given threshold.
We then use a macroscopic simulation to present a proof-of-concept evaluation
of the proposed modular strategy compared to a benchmark traditional
stop-skipping strategy and the base (no control) case. We find that the
proposed strategy outperforms the benchmark in decreasing each of the three
travel time components: waiting time, in-vehicle time, and walking time (which
it eliminates completely). It therefore reduces the overhead of bus bunching
and thus the travel cost by more than twice as much as the benchmark for busy
bus lines. Simultaneously, it also reduces headway variability to a comparable
degree. Furthermore, we analyze different control thresholds for applying the
proposed strategy, and show that it is most effective when applied proactively,
i.e. with the control action being triggered even by small headway deviations.


title:
A Variational Formula for Infinity-R\'{e}nyi Divergence with
  Applications to Information Leakage

abstract:
  We present a variational characterization for the R\'{e}nyi divergence of
order infinity. Our characterization is related to guessing: the objective
functional is a ratio of maximal expected values of a gain function applied to
the probability of correctly guessing an unknown random variable. An important
aspect of our variational characterization is that it remains agnostic to the
particular gain function considered, as long as it satisfies some regularity
conditions. Also, we define two variants of a tunable measure of information
leakage, the maximal $\alpha$-leakage, and obtain closed-form expressions for
these information measures by leveraging our variational characterization.


title: A multi-task semi-supervised framework for Text2Graph & Graph2Text

abstract:
  The Artificial Intelligence industry regularly develops applications that
mostly rely on Knowledge Bases, a data repository about specific, or general,
domains, usually represented in a graph shape. Similar to other databases, they
face two main challenges: information ingestion and information retrieval. We
approach these challenges by jointly learning graph extraction from text and
text generation from graphs. The proposed solution, a T5 architecture, is
trained in a multi-task semi-supervised environment, with our collected
non-parallel data, following a cycle training regime. Experiments on WebNLG
dataset show that our approach surpasses unsupervised state-of-the-art results
in text-to-graph and graph-to-text. More relevantly, our framework is more
consistent across seen and unseen domains than supervised models. The resulting
model can be easily trained in any new domain with non-parallel data, by simply
adding text and graphs about it, in our cycle framework.


title:
Kinetic-scale flux ropes: Observations and applications of kinetic
  equilibrium models

abstract:
  Magnetic flux ropes with helical field lines and strong core field are
ubiquitous structures in space plasmas. Recently, kinetic-scale flux ropes have
been identified by high-resolution observations from Magnetospheric Multiscale
(MMS) spacecraft in the magnetosheath, which have drawn a lot of attention
because of their non-ideal behavior and internal structures. Detailed
investigation of flux rope structure and dynamics requires development of
realistic kinetic models. In this paper, we generalize an equilibrium model to
reconstruct a kinetic-scale flux rope previously reported via MMS observations.
The key features in the magnetic field and electron pitch-angle distribution
measurements of all four satellites are simultaneously reproduced in this
reconstruction. Besides validating the model, our results also indicate that
the anisotropic features previously attributed to asymmetric magnetic
topologies in the magnetosheath can be alternatively explained by the
spacecraft motion in the flux rope rest frame.


title:
RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding
  Style Transformation

abstract:
  Source code authorship attribution is an important problem often encountered
in applications such as software forensics, bug fixing, and software quality
analysis. Recent studies show that current source code authorship attribution
methods can be compromised by attackers exploiting adversarial examples and
coding style manipulation. This calls for robust solutions to the problem of
code authorship attribution. In this paper, we initiate the study on making
Deep Learning (DL)-based code authorship attribution robust. We propose an
innovative framework called Robust coding style Patterns Generation (RoPGen),
which essentially learns authors' unique coding style patterns that are hard
for attackers to manipulate or imitate. The key idea is to combine data
augmentation and gradient augmentation at the adversarial training phase. This
effectively increases the diversity of training examples, generates meaningful
perturbations to gradients of deep neural networks, and learns diversified
representations of coding styles. We evaluate the effectiveness of RoPGen using
four datasets of programs written in C, C++, and Java. Experimental results
show that RoPGen can significantly improve the robustness of DL-based code
authorship attribution, by respectively reducing 22.8% and 41.0% of the success
rate of targeted and untargeted attacks on average.


title:
Square-free Strong Triangular Decomposition of Zero-dimensional
  Polynomial Systems

abstract:
  Triangular decomposition with different properties has been used for various
types of problem solving, e.g. geometry theorem proving, real solution
isolation of zero-dimensional polynomial systems, etc. In this paper, the
concepts of strong chain and square-free strong triangular decomposition
(SFSTD) of zero-dimensional polynomial systems are defined. Because of its good
properties, SFSTD may be a key way to many problems related to zero-dimensional
polynomial systems, such as real solution isolation and computing radicals of
zero-dimensional ideals. Inspired by the work of Wang and of Dong and Mou, we
propose an algorithm for computing SFSTD based on Gr\"obner bases computation.
The novelty of the algorithm is that we make use of saturated ideals and
separant to ensure that the zero sets of any two strong chains have no
intersection and every strong chain is square-free, respectively. On one hand,
we prove that the arithmetic complexity of the new algorithm can be single
exponential in the square of the number of variables, which seems to be among
the rare complexity analysis results for triangular-decomposition methods. On
the other hand, we show experimentally that, on a large number of examples in
the literature, the new algorithm is far more efficient than a popular
triangular-decomposition method based on pseudo-division. Furthermore, it is
also shown that, on those examples, the methods based on SFSTD for real
solution isolation and for computing radicals of zero-dimensional ideals are
very efficient.


title: USTED: Improving ASR with a Unified Speech and Text Encoder-Decoder

abstract:
  Improving end-to-end speech recognition by incorporating external text data
has been a longstanding research topic. There has been a recent focus on
training E2E ASR models that get the performance benefits of external text data
without incurring the extra cost of evaluating an external language model at
inference time. In this work, we propose training ASR model jointly with a set
of text-to-text auxiliary tasks with which it shares a decoder and parts of the
encoder. When we jointly train ASR and masked language model with the 960-hour
Librispeech and Opensubtitles data respectively, we observe WER reductions of
16% and 20% on test-other and test-clean respectively over an ASR-only baseline
without any extra cost at inference time, and reductions of 6% and 8% compared
to a stronger MUTE-L baseline which trains the decoder with the same text data
as our model. We achieve further improvements when we train masked language
model on Librispeech data or when we use machine translation as the auxiliary
task, without significantly sacrificing performance on the task itself.


title:
Hierarchical Aerial Computing for Internet of Things via Cooperation of
  HAPs and UAVs

abstract:
  With the explosive increment of computation requirements, the multi-access
edge computing (MEC) paradigm appears as an effective mechanism. Besides, as
for the Internet of Things (IoT) in disasters or remote areas requiring MEC
services, unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs)
are available to provide aerial computing services for these IoT devices. In
this paper, we develop the hierarchical aerial computing framework composed of
HAPs and UAVs, to provide MEC services for various IoT applications. In
particular, the problem is formulated to maximize the total IoT data computed
by the aerial MEC platforms, restricted by the delay requirement of IoT and
multiple resource constraints of UAVs and HAPs, which is an integer programming
problem and intractable to solve. Due to the prohibitive complexity of
exhaustive search, we handle the problem by presenting the matching game theory
based algorithm to deal with the offloading decisions from IoT devices to UAVs,
as well as a heuristic algorithm for the offloading decisions between UAVs and
HAPs. The external effect affected by interplay of different IoT devices in the
matching is tackled by the externality elimination mechanism. Besides, an
adjustment algorithm is also proposed to make the best of aerial resources. The
complexity of proposed algorithms is analyzed and extensive simulation results
verify the efficiency of the proposed algorithms, and the system performances
are also analyzed by the numerical results.


title:
Load Balancing in Low-Voltage Distribution Networks via Optimizing
  Residential Phase Connections

abstract:
  Unbalance issues in low-voltage distribution networks (LVDN) can be worsened
by increasing penetration of residential PV generation if unevenly distributed
among three phases. To address this issue, the phase-switching device (PSD)
provides a viable and efficient method by dynamically switching customers to
other phases. This paper further investigates how to optimize residential phase
connections by controlling PSDs efficiently. The optimization problem is
formulated as a mixed-integer non-convex programming (MINCP) problem
considering relevant operational requirements of an LVDN based on the exact
formulation of unbalanced three-phase optimal power flow (UTOPF). Unlike most
heuristic algorithms and the linearization techniques in our previous work,
this paper proposes to solve the MINCP problem via an iteration-based algorithm
after exact reformulations and reasonable approximations of some constraints.
The proposed method is tested in a real LVDN and compared with the approach of
Zhao et al. based on the well-known linear UTOPF formulation. Case studies
based on the European low-voltage test feeder demonstrate the proposed method's
efficiency in mitigating the network unbalance while ensuring network security
and flexibility to deal with more controllable resources.


title:
Phonon wave packet emission during state preparation of a semiconductor
  quantum dot using different schemes

abstract:
  The carrier-phonon interaction in semiconductor quantum dots can greatly
affect the optical preparation of the excited state. For resonant excitation
used in the Rabi preparation scheme, the polaron is formed accompanied by the
emission of a phonon wave packet, leading to a degradation of preparation
fidelity. In this paper, phonon wave packets for different coherent excitation
schemes are analyzed. One example is the adiabatic rapid passage scheme relying
on a chirped excitation. Here, also a phonon wave packet is emitted, but the
preparation fidelity can still be approximately unity. A focus is on the phonon
impact on a recently proposed swing-up scheme, induced by two detuned pulses.
Similar to the Rabi scheme, a degradation and a phonon wave packet emission is
found, despite the detuning. If the swing-up frequency coincides with the
maximum of the phonon spectral density, a series of wave packets is emitted
yielding an even stronger degradation. The insight gained from our results will
further help in designing an optimal preparation scheme for quantum dots.


title: Renormalization-Group Theory of the Heisenberg Model in d Dimensions

abstract:
  The classical Heisenberg model has been solved in spatial d dimensins,
exactly in d=1 and by the Migdal-Kadanoff approximation in d>1, by using a
Fourier-Legendre expansion. The phase transition temperatures, the energy
densities, and the specific heats are calculated in arbitrary dimension d.
Fisher's exact result is recovered in d=1. The absence of an ordered phase,
conventional or algebraic (in contrast to the XY model yielding an
algebraically ordered phase), is recovered in d=2. A conventionally ordered
phase occurs at d>2. This method opens the way to complex-system calculations
with Heisenberg local degrees of freedom.


title:
Emergent properties in supercrystals of atomically precise nanocluster
  and colloidal nanocrystals

abstract:
  We provide a comprehensive account of the optical, electrical and mechanical
properties that emerge from the self-assembly of colloidal nanocrystals or
atomically precise nanoclusters into crystalline arrays with long-range order.
We compare the correlation between the supercrystalline structure and these
emergent properties with similar correlations in crystals of atoms to address
the hypothesis that nanocrystals and nanoclusters exhibit quasi-atomic
behaviour. We come to the conclusion that, effectively, this analogy is indeed
justified, although the chemical origin for the same emergent properties are
substantially different in crystals of atoms vs. supercrystals. We provide an
outlook onto the most promising applications of supercrystals of nanocrystals
and nanoclusters and discuss the challenges to be overcome before their
commercialization.


title: Artificial Dynamical Effects in Quantum Field Theory

abstract:
  In Newtonian mechanics, inertial pseudoforces - or fictitious forces - appear
in systems studied in non-Galilean reference frames; e.g., a centrifugal force
seems to arise if the dynamics is analyzed in a rotating reference frame. The
equivalent of Galilean invariance for relativistic kinematics is Poincar\'e
invariance; analogous artificial effects may arise in relativistic quantum
field theory (QFT) if a system is studied in a framework violating Poincar\'e
invariance. We highlight how such issues complicate the traditional canonical
quantization of QFTs and can lead to a subjective description of natural
phenomena. In fact, if the system involves the strong interaction, obtaining
objective results can become an intractable problem using canonical
quantization because the pseudoforces are essentially nonperturbative. In
contrast, the treatment of the same problem using light-front (LF) quantization
is free of spurious pseudoeffects because Poincar\'e invariance is manifest;
thus the treatment of strong interaction problems becomes simpler. These
statements are illustrated using several examples: the Gerasimov-Drell-Hearn
(GDH) relation, a fundamental feature of QFT; the absence of any measurable
impact of Lorentz contraction in high-energy collisions; and the fictitious
character of vacuum fluctuation contributions to the cosmological constant.


title:
Learning by Doing: Controlling a Dynamical System using Causality,
  Control, and Reinforcement Learning

abstract:
  Questions in causality, control, and reinforcement learning go beyond the
classical machine learning task of prediction under i.i.d. observations.
Instead, these fields consider the problem of learning how to actively perturb
a system to achieve a certain effect on a response variable. Arguably, they
have complementary views on the problem: In control, one usually aims to first
identify the system by excitation strategies to then apply model-based design
techniques to control the system. In (non-model-based) reinforcement learning,
one directly optimizes a reward. In causality, one focus is on identifiability
of causal structure. We believe that combining the different views might create
synergies and this competition is meant as a first step toward such synergies.
The participants had access to observational and (offline) interventional data
generated by dynamical systems. Track CHEM considers an open-loop problem in
which a single impulse at the beginning of the dynamics can be set, while Track
ROBO considers a closed-loop problem in which control variables can be set at
each time step. The goal in both tracks is to infer controls that drive the
system to a desired state. Code is open-sourced (
https://github.com/LearningByDoingCompetition/learningbydoing-comp ) to
reproduce the winning solutions of the competition and to facilitate trying out
new methods on the competition tasks.

categories: cs.LG cs.RO cs.SY eess.SY stat.ME stat.ML


title: Local Differential Privacy for Federated Learning in Industrial Settings

abstract:
  Federated learning (FL) is a collaborative learning approach that has gained
much attention due to its inherent privacy preservation capabilities. However,
advanced adversarial attacks such as membership inference and model
memorization can still make FL vulnerable and potentially leak sensitive
private data. Literature shows a few attempts to alleviate this problem by
using global (GDP) and local differential privacy (LDP). Compared to GDP, LDP
approaches are gaining more popularity due to stronger privacy notions and
native support for data distribution. However, DP approaches assume that the
server that aggregates the models, to be honest (run the FL protocol honestly)
or semi-honest (run the FL protocol honestly while also trying to learn as much
information possible), making such approaches unreliable for real-world
settings. In real-world industrial environments (e.g. healthcare), the
distributed entities (e.g. hospitals) are already composed of locally running
machine learning models (e.g. high-performing deep neural networks on local
health records). Existing approaches do not provide a scalable mechanism to
utilize such settings for privacy-preserving FL. This paper proposes a new
local differentially private FL (named LDPFL) protocol for industrial settings.
LDPFL avoids the requirement of an honest or a semi-honest server and provides
better performance while enforcing stronger privacy levels compared to existing
approaches. Our experimental evaluation of LDPFL shows high FL model
performance (up to ~98%) under a small privacy budget (e.g. epsilon = 0.5) in
comparison to existing methods.


title:
Relaxing the Feature Covariance Assumption: Time-Variant Bounds for
  Benign Overfitting in Linear Regression

abstract:
  Benign overfitting demonstrates that overparameterized models can perform
well on test data while fitting noisy training data. However, it only considers
the final min-norm solution in linear regression, which ignores the algorithm
information and the corresponding training procedure. In this paper, we
generalize the idea of benign overfitting to the whole training trajectory
instead of the min-norm solution and derive a time-variant bound based on the
trajectory analysis. Starting from the time-variant bound, we further derive a
time interval that suffices to guarantee a consistent generalization error for
a given feature covariance. Unlike existing approaches, the newly proposed
generalization bound is characterized by a time-variant effective dimension of
feature covariance. By introducing the time factor, we relax the strict
assumption on the feature covariance matrix required in previous benign
overfitting under the regimes of overparameterized linear regression with
gradient descent. This paper extends the scope of benign overfitting, and
experiment results indicate that the proposed bound accords better with
empirical evidence.


title: Trace formula for the magnetic Laplacian on a compact hyperbolic surface

abstract:
  We compute the trace formula for the magnetic Laplacian on a compact
hyperbolic surface of constant curvature with constant magnetic field for
energies above the Mane critical level of the corresponding magnetic geodesic
flow. We discuss the asymptotic behavior of the coefficients of the trace
formula when the energy approaches the Mane critical level.


title:
Optimization-based Trajectory Tracking Approach for Multi-rotor Aerial
  Vehicles in Unknown Environments

abstract:
  The goal of this paper is to develop a continuous optimization-based
refinement of the reference trajectory to 'push it out' of the
obstacle-occupied space in the global phase for Multi-rotor Aerial Vehicles in
unknown environments. Our proposed approach comprises two planners: a global
planner and a local planner. The global planner refines the initial reference
trajectory when the trajectory goes either through an obstacle or near an
obstacle and lets the local planner calculate a near-optimal control policy.
The global planner comprises two convex programming approaches: the first one
helps to refine the reference trajectory, and the second one helps to recover
the reference trajectory if the first approach fails to refine. The global
planner mainly focuses on real-time performance and obstacles avoidance,
whereas the proposed formulation of the constrained nonlinear model predictive
control-based local planner ensures safety, dynamic feasibility, and the
reference trajectory tracking accuracy for low-speed maneuvers, provided that
local and global planners have mean computation times 0.06s (15Hz) and 0.05s
(20Hz), respectively, on an NVIDIA Jetson Xavier NX computer. The results of
our experiment confirmed that, in cluttered environments, the proposed approach
outperformed three other approaches: sampling-based pathfinding followed by
trajectory generation, a local planner, and graph-based pathfinding followed by
trajectory generation.


title: Mixed standardization and Ringel duality

abstract:
  Dlab--Ringel's standardization method gives a realization of a standardly
stratified algebra. In this paper, we construct mixed stratified algebras,
which are a generalization of standardly stratified algebras, following
Dlab--Ringel's standardization method. Moreover, we study a Ringel duality of
mixed stratified algebras from the viewpoint of stratifying systems.


title: Comment on "$Z_c$-like spectra from QCD Laplace sum rules at NLO"

abstract:
  In Phys. Rev. {\bf D103} (2021) 074015 (arXiv:2101.07281), huge (or largest)
mass gaps between the ground states and first radial excited states for the
tetraquark molecular states are obtained, $\Delta=1.70\sim 2.35\,\rm{GeV}$. We
explore the problems in the operator product expansion, Borel windows, etc, in
details.


title: Nonlinear biphasic mixture model: existence and uniqueness results

abstract:
  This paper is concerned with the development and analysis of a mathematical
model that is motivated by the interstitial hydrodynamics and tissue
deformation mechanics (poro-elasto-hydrodynamics) within an in-vitro solid
tumor. The classical mixture theory is adopted to mass and momentum balance
equations for a two phase system. The main contribution of this study: we treat
the physiological transport parameter (i.e., hydraulic resistivity) as
anisotropic and heterogeneous, thus the governing system is strongly coupled
and non-linear. We establish the existence and uniqueness results to this
non-linear system in a weak sense. We show further that the solution depends
continuously on the given data.


title: Depth-Cooperated Trimodal Network for Video Salient Object Detection

abstract:
  Depth can provide useful geographical cues for salient object detection
(SOD), and has been proven helpful in recent RGB-D SOD methods. However,
existing video salient object detection (VSOD) methods only utilize
spatiotemporal information and seldom exploit depth information for detection.
In this paper, we propose a depth-cooperated trimodal network, called DCTNet
for VSOD, which is a pioneering work to incorporate depth information to assist
VSOD. To this end, we first generate depth from RGB frames, and then propose an
approach to treat the three modalities unequally. Specifically, a multi-modal
attention module (MAM) is designed to model multi-modal long-range dependencies
between the main modality (RGB) and the two auxiliary modalities (depth,
optical flow). We also introduce a refinement fusion module (RFM) to suppress
noises in each modality and select useful information dynamically for further
feature refinement. Lastly, a progressive fusion strategy is adopted after the
refined features to achieve final cross-modal fusion. Experiments on five
benchmark datasets demonstrate the superiority of our depth-cooperated model
against 12 state-of-the-art methods, and the necessity of depth is also
validated.


title:
Topological Lifshitz transition in Weyl semimetal NbP decorated with
  heavy elements

abstract:
  Studies of the Fermi surface modification after in-situ covering NbP
semimetal with heavy elements Pb and Nb ultrathin layers were performed by
means of angle-resolved photoemission spectroscopy (ARPES). First, the
electronic structure was investigated for pristine single crystals with two
possible terminations (P and Nb) of the (0 0 1) surface. The nature of the
electronic states of these two cleaving planes is different: P-terminated
surface shows spoon and bow tie shaped fingerprints, whereas these shapes are
not present in Nb-terminated surfaces. ARPES studies show that even 1 monolayer
(ML) of Pb causes topological quantum Lifshitz transition (TQLT) in P- and
Nb-terminated surfaces. Deposited Pb 5d electrons have wide extended atomic
orbitals which leads to strong hybridization with Pb-terminated surface and a
corresponding shift in the Fermi energy. Nb has less capability to perturb the
system than Pb because Nb has weaker spin-orbit coupling than Pb. Nb-terminated
surface subjected to surface decoration with approximately 1.3 ML of Nb shows
no dramatic modification in the Fermi surface. In the case of Nb decorated
P-terminated surface, deposition of approximately 1 ML modifies the electronic
structure of NbP and it is on the verge of TQLT. Despite the strong spin-orbit
and strong hybridization of the heavy elements on the surface, it is possible
to observe the TQLT of the surface states thanks to the robustness of the bulk
topology.


title:
Detailed chemistry modelling of rotating detonations with dilute
  n-heptane sprays and preheated air

abstract:
  Utilization of liquid fuels is crucial to enabling commercialization of
rotating detonation engines in the near future. In this study,
Eulerian-Lagrangian simulations are conducted for rotating detonative
combustion with dilute n-heptane sprays and preheated air. Two-dimensional
flattened configuration is used and a skeletal chemical mechanism with 44
species and 112 elementary reactions for n-heptane combustion is adopted. The
flow structure, droplet distribution, and thermochemical parameters in the
refill zone are first analyzed. It is shown that the mixture in the refill zone
is heterogeneous, including evaporating droplets, vapor, and air. When the
total temperature is below 950 K, the average equivalence ratio increases with
the total temperature. When it is higher than 950 K, the average equivalence
ratio is almost constant. Subsequently, the chemical explosive mode analysis is
applied to identify the controlling reactions and dominant combustion modes in
the fuel refill zone and reaction fronts. Results demonstrate that the
initiation reaction and low-temperature reaction are dominant in the upstream
and downstream of the refill zone, respectively. The intermediate species from
low-temperature chemistry is found to be important for the chemical explosive
mode in the undetonated mixture. The influence of species diffusion and
dispersed droplets is further analyzed. Results show that vapor autoignition
facilitated by droplet evaporation occurs in the refill zone. Finally, the
effects of the air total temperature on the detonation propagation speed and
RDE propulsion performance are investigated. It is found that the detonation
propagation speed and specific impulse increase with air total temperature.


title:
Data Analysis of Bulacan State University Faculty Scientific Publication
  Based on Google Scholar using Web Data Scraping Technique

abstract:
  The paper aims to analyze and monitor the research publication productivity
of the faculty members of Bulacan State University. This paper compiles all the
scientific publications from Bulacan State University (BulSU) and its external
campuses as an index in Google Scholar. This study was intended to track and
monitor the scientific productivity of the faculty members of Bulacan State
University and each college and campus.


title:
Self-Adaptive Real-Time Time-Dependent Density Functional Theory for
  Core Excitations

abstract:
  Real-time time-dependent density functional theory (RT-TDDFT) can in
principle access the whole absorption spectrum of a many-electron system
exposed to a narrow pulse. However, this requires an accurate and efficient
propagator for the numerical integration of the time-dependent Kohn-Sham
equation. While a low-order time propagator is already sufficient for the
low-lying valence absorption spectra, it is no longer the case for the X-ray
absorption (XAS) of systems composed even only of light elements, for which the
use of a high-order propagator is indispensable. It is then crucial to choose a
largest possible time step and a shortest possible simulation time, so as to
reduce the computational cost. To this end, we propose here a robust AutoPST
approach to determine automatically (Auto) the propagator (P), step (S), and
time (T) for relativistic RT-TDDFT simulations of XAS.


title: What can be certified compactly?

abstract:
  Local certification consists in assigning labels (called \emph{certificates})
to the nodes of a network to certify a property of the network or the
correctness of a data structure distributed on the network. The verification of
this certification must be local: a node typically sees only its neighbors in
the network. The main measure of performance of a certification is the size of
its certificates.
  In 2011, G\"o\"os and Suomela identified $\Theta(\log n)$ as a special
certificate size: below this threshold little is possible, and several key
properties do have certifications of this type. A certification with such small
certificates is now called a \emph{compact local certification}, and it has
become the gold standard of the area, similarly to polynomial time for
centralized computing. A major question is then to understand which properties
have $O(\log n)$ certificates, or in other words: what is the power of compact
local certification?
  Recently, a series of papers have proved that several well-known network
properties have compact local certifications: planarity, bounded-genus, etc.
But one would like to have more general results, \emph{i.e.} meta-theorems. In
the analogue setting of polynomial-time centralized algorithms, a very fruitful
approach has been to prove that restricted types of problems can be solved in
polynomial time in graphs with restricted structures. These problems are
typically those that can be expressed in some logic, and the graph structures
are whose with bounded width or depth parameters. We take a similar approach
and prove the first meta-theorems for local certification. (See the abstract of
the pdf for more details.)


title:
Predicting Pollution Level Using Random Forest: A Case Study of Marilao
  River in Bulacan Province, Philippines

abstract:
  This study aims to predict the pollution level that threatens the Marilao
River, located in the province of Bulacan, Philippines. The inhabitants of this
area are now being exposed to pollution. Contamination of this waterway comes
from both formal and informal industries, such as a used lead-acid battery,
open dumpsites metal refining, and other toxic metals. Using various water
quality parameters like Dissolved Oxygen (DO), Potential of Hydrogen (pH),
Biochemical Oxygen Demand (BOD) and Total Suspended Solids (TSS) were the basis
for predicting the pollution level. This study used the Data Mining technique
based on the sample data collected from January of 2013 to November of 2017.
These were used as a training data and test results to predict the river
condition with its corresponding pollution level classification indicated with
the used of colors such as Green for Normal, Yellow for Average, Orange for
Polluted and Red for Highly Polluted. The model got an accuracy of 91.75% with
a Kappa value of 0.8115, interpreted as Strong in terms of the level of
agreement.


title:
Theoretical investigation of vacancy related defects at
  4H-SiC(000$\bar{1}$)/SiO$_2$ interface after wet oxidation

abstract:
  The stability and formation mechanism of the defects relevant to silicon and
carbon vacancies at the 4H-SiC(000$\bar{1}$)/SiO$_2$ interface after wet
oxidation are investigated by first-principles calculation based on the density
functional theory. The difference in the total energy of the defects agrees
with the experimental results concerning the dencity of defects. We found that
the characteristic behaviors of the generation of defects are explained by the
positions of vacancies and antisites in the SiC(000$\bar{1}$) substrate and
that the formation of silicon and carbon vacancies is relevant to the
generation mechanism of defects. The generation of silicon and carbon vacancies
is attributed to the termination of dangling bonds by H atoms introduced by wet
oxidation, resulting in generation of carbon-antisite--carbon-vacancy and
divacancies defects in wet oxidation.


title:
Structural peculiarities, mineral inclusions and point defects in
  yakutites -- a variety of impact-related diamond

abstract:
  An unusual variety of impact-related diamond from the Popigai impact
structure - yakutites - is characterized by complementary methods including
optical microscopy, X-ray diffraction, radiography and tomography, infra-red,
Raman and luminescence spectroscopy providing structural information at widely
different scales. It is shown that relatively large graphite aggregates may be
transformed to diamond with preservation of many morphological features.
Spectroscopic and X-ray diffraction data indicate that the yakutite matrix
represents bulk nanocrystalline diamond. For the first time, features of
two-phonon infra-red absorption spectra of bulk nanocrystalline diamond are
interpreted in the framework of phonon dispersion curves. Luminescence spectra
of yakutite are dominated by dislocation-related defects. Optical microscopy
supported by X-ray diffraction reveals the presence of single crystal diamonds
with sizes of up to several tens of microns embedded into nanodiamond matrix.
The presence of single crystal grains in impact diamond may be explained by
CVD-like growth in a transient cavity and/or a seconds-long compression stage
of the impact process due to slow pressure release in a volatile-rich target.
For the first time, protogenetic mineral inclusions in yakutites represented by
mixed monoclinic and tetragonal ZrO2 are observed. This implies the presence of
baddeleyite in target rocks responsible for yakutite formation.


title:
Assessment of E-Learning Readiness of Faculty Members and Students in
  the Government and Private Higher Education Institutions in the Philippines

abstract:
  This study seeks to determine the level of readiness of selected private and
government-managed colleges and universities in the Philippines. The study also
aims to determine if there's a significant difference in the level of readiness
between the private and government HEI's. In line with this, a
descriptive-comparative method was employed. A questionnaire from the works of
Aklaslan and Merca was utilized to determine the faculty members' and students'
status of their e-learning readiness, acceptance, training, technological
infrastructure, and tool awareness on the implementation of an e-learning
program. Weighted mean was used to determine the level of readiness while a
t-test was employed to determine if there is a significant difference between
results.


title:
The molecular gas resolved by ALMA in the low-metallicity dwarf merging
  galaxy Haro 11

abstract:
  The physical mechanisms for starburst or quenching in less massive ($M_* <
10^{10} M_{\odot}$) galaxies are unclear. The merger is one of the inescapable
processes referred to as both starburst and quenching in massive galaxies.
However, the effects of the merger on star formation in dwarf galaxies and
their evolution results are still uncertain. We aim to explore how to trigger
and quench star formation in dwarf galaxies by studying the metal-poor gas-rich
dwarf mergers based on the multi-band observations at a spatial resolution of
$\sim$ 460 pc. We use the archival data of ALMA (band 3, 8) and VLT/MUSE to map
CO($J=$1-0), [CI]($^3$P$_1 - ^3$P$_0$), and H$\alpha$ emission in one of the
most extreme starburst merging dwarf galaxies, Haro 11. We find the molecular
gas is assembled around the central two star-forming regions. The
molecular/ionized gas and stellar components show complex kinematics,
indicating that the gas is probably at a combined stage of collision of clouds
and feedback from star formation. The peak location and distribution of
[CI](1-0) strongly resemble the CO(1-0) emission, meaning that it might trace
the same molecular gas as CO in such a dwarf merger starburst galaxy. The
enhancement of line ratios ($\sim 0.5$) of [CI]/CO around knot C is probably
generated by the dissociation of CO molecules by cosmic rays and
far-ultraviolet photons. Globally, Haro 11 and its star-forming regions share
similar SFEs as the high-$z$ starburst galaxies or the clumps in nearby
(U)LIRGs. Given the high SFE, sSFR, small stellar mass, low metallicity, and
deficient HI gas, Haro 11 could be an analog of high-$z$ dwarf starburst and
the potential progenitor of the nearby less massive elliptical galaxies. The
significantly smaller turbulent pressure and viral parameter will probably
trigger the intense starbursts. We also predict that it will quench at $M_* <
8.5 \times 10^9 M_{\odot}$.


title:
Recursive Feasibility and Deadlock Resolution in MPC-based Multi-robot
  Trajectory Generation

abstract:
  Collision-free trajectory generation within a shared workspace is fundamental
for most multi-robot applications. However, despite of their versatility, many
widely-used methods based on model predictive control (MPC) lack theoretical
guarantees on the feasibility of underlying optimization. Furthermore, when
applied in a distributed manner, deadlocks often occur where several robots
block each other indefinitely without resolution. Towards this end, we propose
a systematic method called infinite-horizon model predictive control with
deadlock resolution (IMPC-DR). It can provably ensure recursive feasibility and
effectively resolve deadlocks online in addition to the handling of input and
model constraints. The method is based on formulating a convex optimization
over the proposed modified buffered Voronoi cells in each planning horizon.
Moreover, it is fully distributed and requires only local inter-robot
communication. Comprehensive simulation and experiment studies are conducted
over large-scale multi-robot systems. Significant improvements of both
feasibility and success rate are shown, in comparison with other
state-of-the-art methods and especially in crowded and high-speed scenarios.


title: Characterization and Comparison of Glass Electrodes

abstract:
  This paper presents the study on the characterization of glass electrodes,
which are one of the main components of detectors like Resistive Plate Chambers
(RPCs). The RPCs are being used in various ongoing High Energy Physics
experiments, e.g., BELLE at KEK, CMS at LHC, and would be used in the near
future experiments e.g., INO-ICAL in India. The characterization of glass
electrodes has been done to understand the factors like quality of glass that
can help in improving the detector's performance. The glass samples chosen were
procured locally Asahi (A), Saint Gobain (S), Modi (M) that are easily
available in Indian industry. The characterization includes the tests to study
the optical, surface, physical, electrical properties, the composition of glass
samples, and leakage currents. This paper adds new information to the existing
body of research on the subject. Based on the techniques discussed in the paper
a comparison of the measurements among the three different types of glass
electrodes has been done. This study helps us to determine the best quality of
glass that can be chosen for better operation of the detectors.


title:
Classification of Microscopy Images of Breast Tissue: Region Duplication
  based Self-Supervision vs. Off-the Shelf Deep Representations

abstract:
  Breast cancer is one of the leading causes of female mortality in the world.
This can be reduced when diagnoses are performed at the early stages of
progression. Further, the efficiency of the process can be significantly
improved with computer aided diagnosis. Deep learning based approaches have
been successfully applied to achieve this. One of the limiting factors for
training deep networks in a supervised manner is the dependency on large
amounts of expert annotated data. In reality, large amounts of unlabelled data
and only small amounts of expert annotated data are available. In such
scenarios, transfer learning approaches and self-supervised learning (SSL)
based approaches can be leveraged. In this study, we propose a novel
self-supervision pretext task to train a convolutional neural network (CNN) and
extract domain specific features. This method was compared with deep features
extracted using pre-trained CNNs such as DenseNet-121 and ResNet-50 trained on
ImageNet. Additionally, two types of patch-combination methods were introduced
and compared with majority voting. The methods were validated on the BACH
microscopy images dataset. Results indicated that the best performance of 99%
sensitivity was achieved for the deep features extracted using ResNet50 with
concatenation of patch-level embedding. Preliminary results of SSL to extract
domain specific features indicated that with just 15% of unlabelled data a high
sensitivity of 94% can be achieved for a four class classification of
microscopy images.


title:
The DESI $N$-body Simulation Project II: Suppressing Sample Variance
  with Fast Simulations

abstract:
  Dark Energy Spectroscopic Instrument (DESI) will construct a large and
precise 3D map of our Universe. The survey effective volume reaches $\sim20$
Gpc$^3h^{-3}$. It is a great challenge to prepare high-resolution simulations
with a much larger volume for validating the DESI analysis pipelines.
AbacusSummit is a suite of high-resolution dark-matter-only simulations
designed for this purpose, with $200$ Gpc$^3h^{-3}$ (10 times DESI volume) for
the base cosmology. However, further efforts need to be done to provide more
precise analysis of the data and to cover also other cosmologies. Recently, the
CARPool method was proposed to use paired accurate and approximate simulations
to achieve high statistical precision with a limited number of high-resolution
simulations. Relying on this technique, we propose to use fast quasi-$N$-body
solvers combined with accurate simulations to produce accurate summary
statistics. This enables us to obtain 100 times smaller variances than the
expected DESI statistical variance at the scales we are interested in, e.g., $k
< 0.3~h$Mpc$^{-1}$. We further generalize the method for other cosmologies with
only one realization in AbacusSummit suite to extend the effective volume $\sim
20$ times. In summary, our proposed strategy of combining high fidelity
simulations with fast approximate gravity solvers and a series of variance
suppression techniques sets the path for a robust cosmological analysis of
galaxy survey data.


title:
Search for new physics in the lepton plus missing transverse momentum
  final state in proton-proton collisions at $\sqrt{s}$ = 13 TeV

abstract:
  A search for physics beyond the standard model (SM) in final states with an
electron or muon and missing transverse momentum is presented. The analysis
uses data from proton-proton collisions at a centre-of-mass energy of 13 TeV,
collected with the CMS detector at the LHC in 2016-2018 and corresponding to an
integrated luminosity of 138 fb$^{-1}$. No significant deviation from the SM
prediction is observed. Model-independent limits are set on the production
cross section of W' bosons decaying into lepton-plus-neutrino final states.
Within the framework of the sequential standard model, with the combined
results from the electron and muon decay channels a W' boson with mass less
than 5.7 TeV is excluded at 95% confidence level. Results on a SM precision
test, the determination of the oblique electroweak $W$ parameter, are presented
using LHC data for the first time. These results together with those from the
direct W' resonance search are used to extend existing constraints on composite
Higgs scenarios. This is the first experimental exclusion on compositeness
parameters using results from LHC data other than Higgs boson measurements.


title:
Indication as Prior Knowledge for Multimodal Disease Classification in
  Chest Radiographs with Transformers

abstract:
  When a clinician refers a patient for an imaging exam, they include the
reason (e.g. relevant patient history, suspected disease) in the scan request;
this appears as the indication field in the radiology report. The
interpretation and reporting of the image are substantially influenced by this
request text, steering the radiologist to focus on particular aspects of the
image. We use the indication field to drive better image classification, by
taking a transformer network which is unimodally pre-trained on text (BERT) and
fine-tuning it for multimodal classification of a dual image-text input. We
evaluate the method on the MIMIC-CXR dataset, and present ablation studies to
investigate the effect of the indication field on the classification
performance. The experimental results show our approach achieves 87.8 average
micro AUROC, outperforming the state-of-the-art methods for unimodal (84.4) and
multimodal (86.0) classification. Our code is available at
https://github.com/jacenkow/mmbt.


title:
An experimental study of morphological formation in bilayered tubular
  structures driven by swelling/growth

abstract:
  This paper presents an experimental investigation on pattern formation and
evolution in bilayered tubular organs using swelling deformation of
polydimethylsiloxane (PDMS) and aims at supplying a thorough comparison with
theoretical and finite element results. To create a twin model in modelling and
simulation, the shear modulus in the incompressible neo-Hookean material is
estimated via uni-axial tensile and pure shear tests. Five bilayered tubes with
different material or geometrical parameters are fabricated. Swelling
experiments are carried out for these samples in an individual experimental
setup where a plane-strain deformation is guaranteed, and several surface
patterns and the associated mode transformations are observed, namely, creases,
wrinkles, period-doubling profiles, wrinkle-to-crease transition, and
wrinkle-to-period-doubling transition. In particular, an interfacial wrinkling
pattern is also observed. To make comparisons, a buckling analysis is conducted
within the framework of finite elasticity by means of the Stroh formulation. In
addition, a finite element analysis is performed to trace the evolution of
surface instabilities. It turns out that the experimental findings agree well
with the theoretical predictions as well as the finite element results. From
our experiments, it is found that creasing mode may appear instead of wrinkling
mode when both layers share a similar mechanical property. It is expected that
the current work could provide novel experimental insight into pattern
formation in tubular structures, and the good agreement among experimental,
theoretical, and simulation consequences supplies strong evidence that a
phenomenological growth model is satisfactory to reveal mechanisms behind
intricate surface morphology in tubular tissues.


title: On some of Brouwer's axioms

abstract:
  We discuss the position of intuitionistic mathematics within the field of
constructive mathematics. We discuss some principles defended and used by
Brouwer but rejected by Bishop, like the Coninuity Principle, the Fan Theorem
and the Bar Theorem. We explain some of their consequences in the development
of constructive mathematics, like the Borel Hierarchy Theorem and the
Intuitionistic Ramsey Theorem. We go into the theory of measure and integration
as Bishop followed in this field a path different from Brouwer's.


title: Text and Image Guided 3D Avatar Generation and Manipulation

abstract:
  The manipulation of latent space has recently become an interesting topic in
the field of generative models. Recent research shows that latent directions
can be used to manipulate images towards certain attributes. However,
controlling the generation process of 3D generative models remains a challenge.
In this work, we propose a novel 3D manipulation method that can manipulate
both the shape and texture of the model using text or image-based prompts such
as 'a young face' or 'a surprised face'. We leverage the power of Contrastive
Language-Image Pre-training (CLIP) model and a pre-trained 3D GAN model
designed to generate face avatars, and create a fully differentiable rendering
pipeline to manipulate meshes. More specifically, our method takes an input
latent code and modifies it such that the target attribute specified by a text
or image prompt is present or enhanced, while leaving other attributes largely
unaffected. Our method requires only 5 minutes per manipulation, and we
demonstrate the effectiveness of our approach with extensive results and
comparisons.


title: Recognition-free Question Answering on Handwritten Document Collections

abstract:
  In recent years, considerable progress has been made in the research area of
Question Answering (QA) on document images. Current QA approaches from the
Document Image Analysis community are mainly focusing on machine-printed
documents and perform rather limited on handwriting. This is mainly due to the
reduced recognition performance on handwritten documents. To tackle this
problem, we propose a recognition-free QA approach, especially designed for
handwritten document image collections. We present a robust document retrieval
method, as well as two QA models. Our approaches outperform the
state-of-the-art recognition-free models on the challenging BenthamQA and
HW-SQuAD datasets.


title:
Modeling User Behavior with Graph Convolution for Personalized Product
  Search

abstract:
  User preference modeling is a vital yet challenging problem in personalized
product search. In recent years, latent space based methods have achieved
state-of-the-art performance by jointly learning semantic representations of
products, users, and text tokens. However, existing methods are limited in
their ability to model user preferences. They typically represent users by the
products they visited in a short span of time using attentive models and lack
the ability to exploit relational information such as user-product interactions
or item co-occurrence relations. In this work, we propose to address the
limitations of prior arts by exploring local and global user behavior patterns
on a user successive behavior graph, which is constructed by utilizing
short-term actions of all users. To capture implicit user preference signals
and collaborative patterns, we use an efficient jumping graph convolution to
explore high-order relations to enrich product representations for user
preference modeling. Our approach can be seamlessly integrated with existing
latent space based methods and be potentially applied in any product retrieval
method that uses purchase history to model user preferences. Extensive
experiments on eight Amazon benchmarks demonstrate the effectiveness and
potential of our approach. The source code is available at
\url{https://github.com/floatSDSDS/SBG}.