title: Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging

abstract:
  Prompting methods recently achieve impressive success in few-shot learning.
These methods modify input samples with prompt sentence pieces, and decode
label tokens to map samples to corresponding labels. However, such a paradigm
is very inefficient for the task of slot tagging. Since slot tagging samples
are multiple consecutive words in a sentence, the prompting methods have to
enumerate all n-grams token spans to find all the possible slots, which greatly
slows down the prediction. To tackle this, we introduce an inverse paradigm for
prompting. Different from the classic prompts mapping tokens to labels, we
reversely predict slot values given slot types. Such inverse prompting only
requires a one-turn prediction for each slot type and greatly speeds up the
prediction. Besides, we propose a novel Iterative Prediction Strategy, from
which the model learns to refine predictions by considering the relations
between different slot types. We find, somewhat surprisingly, the proposed
method not only predicts faster but also significantly improves the effect
(improve over 6.1 F1-scores on 10-shot setting) and achieves new
state-of-the-art performance.


title:
A general mathematical framework for constrained mixed-variable blackbox
  optimization problems with meta and categorical variables

abstract:
  A mathematical framework for modelling constrained mixed-variable
optimization problems is presented in a blackbox optimization context. The
framework introduces a new notation and allows solution strategies. The
notation framework allows meta and categorical variables to be explicitly and
efficiently modelled, which facilitates the solution of such problems. The new
term meta variables is used to describe variables that influence which
variables are acting or nonacting: meta variables may affect the number of
variables and constraints. The flexibility of the solution strategies supports
the main blackbox mixed-variable optimization approaches: direct search methods
and surrogate-based methods (Bayesian optimization). The notation system and
solution strategies are illustrated through an example of a hyperparameter
optimization problem from the machine learning community.


title: Dimensionless machine learning: Imposing exact units equivariance

abstract:
  Units equivariance is the exact symmetry that follows from the requirement
that relationships among measured quantities of physics relevance must obey
self-consistent dimensional scalings. Here, we employ dimensional analysis and
ideas from equivariant machine learning to provide a two stage learning
procedure for units-equivariant machine learning. For a given learning task, we
first construct a dimensionless version of its inputs using classic results
from dimensional analysis, and then perform inference in the dimensionless
space. Our approach can be used to impose units equivariance across a broad
range of machine learning methods which are equivariant to rotations and other
groups. We discuss the in-sample and out-of-sample prediction accuracy gains
one can obtain in contexts like symbolic regression and emulation, where
symmetry is important. We illustrate our approach with simple numerical
examples involving dynamical systems in physics and ecology.


title:
Learning List-wise Representation in Reinforcement Learning for Ads
  Allocation with Multiple Auxiliary Tasks

abstract:
  With the recent prevalence of reinforcement learning (RL), there have been
tremendous interests in utilizing RL for ads allocation in recommendation
platforms (e.g., e-commerce and news feed sites). For better performance,
recent RL-based ads allocation agent makes decisions based on representations
of list-wise item arrangement. This results in a high-dimensional state-action
space, which makes it difficult to learn an efficient and generalizable
list-wise representation. To address this problem, we propose a novel algorithm
to learn a better representation by leveraging task-specific signals on Meituan
food delivery platform. Specifically, we propose three different types of
auxiliary tasks that are based on reconstruction, prediction, and contrastive
learning respectively. We conduct extensive offline experiments on the
effectiveness of these auxiliary tasks and test our method on real-world food
delivery platform. The experimental results show that our method can learn
better list-wise representations and achieve higher revenue for the platform.


title: Moment-based Adversarial Training for Embodied Language Comprehension

abstract:
  In this paper, we focus on a vision-and-language task in which a robot is
instructed to execute household tasks. Given an instruction such as "Rinse off
a mug and place it in the coffee maker," the robot is required to locate the
mug, wash it, and put it in the coffee maker. This is challenging because the
robot needs to break down the instruction sentences into subgoals and execute
them in the correct order. On the ALFRED benchmark, the performance of
state-of-the-art methods is still far lower than that of humans. This is
partially because existing methods sometimes fail to infer subgoals that are
not explicitly specified in the instruction sentences. We propose Moment-based
Adversarial Training (MAT), which uses two types of moments for perturbation
updates in adversarial training. We introduce MAT to the embedding spaces of
the instruction, subgoals, and state representations to handle their varieties.
We validated our method on the ALFRED benchmark, and the results demonstrated
that our method outperformed the baseline method for all the metrics on the
benchmark.


title:
From Simulated Mixtures to Simulated Conversations as Training Data for
  End-to-End Neural Diarization

abstract:
  End-to-end neural diarization (EEND) is nowadays one of the most prominent
research topics in speaker diarization. EEND presents an attractive alternative
to standard cascaded diarization systems since a single system is trained at
once to deal with the whole diarization problem. Several EEND variants and
approaches are being proposed, however, all these models require large amounts
of annotated data for training but available annotated data are scarce. Thus,
EEND works have used mostly simulated mixtures for training. However, simulated
mixtures do not resemble real conversations in many aspects. In this work we
present an alternative method for creating synthetic conversations that
resemble real ones by using statistics about distributions of pauses and
overlaps estimated on genuine conversations. Furthermore, we analyze the effect
of the source of the statistics, different augmentations and amounts of data.
We demonstrate that our approach performs substantially better than the
original one, while reducing the dependence on the fine-tuning stage.
Experiments are carried out on 2-speaker telephone conversations of Callhome
and DIHARD 3. Together with this publication, we release our implementations of
EEND and the method for creating simulated conversations.


title:
A Free Lunch to Person Re-identification: Learning from Automatically
  Generated Noisy Tracklets

abstract:
  A series of unsupervised video-based re-identification (re-ID) methods have
been proposed to solve the problem of high labor cost required to annotate
re-ID datasets. But their performance is still far lower than the supervised
counterparts. In the mean time, clean datasets without noise are used in these
methods, which is not realistic. In this paper, we propose to tackle this
problem by learning re-ID models from automatically generated person tracklets
by multiple objects tracking (MOT) algorithm. To this end, we design a
tracklet-based multi-level clustering (TMC) framework to effectively learn the
re-ID model from the noisy person tracklets. First, intra-tracklet isolation to
reduce ID switch noise within tracklets; second, alternates between using
inter-tracklet association to eliminate ID fragmentation noise and network
training using the pseudo label. Extensive experiments on MARS with various
manually generated noises show the effectiveness of the proposed framework.
Specifically, the proposed framework achieved mAP 53.4% and rank-1 63.7% on the
simulated tracklets with strongest noise, even outperforming the best existing
method on clean tracklets. Based on the results, we believe that building re-ID
models from automatically generated noisy tracklets is a reasonable approach
and will also be an important way to make re-ID models feasible in real-world
applications.


title: Dilepton production from chirally asymmetric matter

abstract:
  We evaluate the dilepton production rate (DPR) from hot and dense chirally
asymmetric quark matter. The presence of a finite chiral chemical potential
(CCP) in the electromagnetic spectral function results in the appearance of new
cut structures signifying additional scattering processes in the medium which
leads to a significant enhancement in the DPR at lower values of invariant
mass. The constituent quark mass evaluated using a 3-flavour
Nambu--Jona-Lasinio model is also non-trivially affected by the CCP. These are
found to result in a continuous dilepton production rate as a function of the
invariant mass for higher values of temperature and baryonic chemical
potential.


title: On resolution coresets for constrained clustering

abstract:
  Specific data compression techniques, formalized by the concept of coresets,
proved to be powerful for many optimization problems. In fact, while tightly
controlling the approximation error, coresets may lead to significant speed up
of the computations and hence allow to extend algorithms to much larger problem
sizes. The present paper deals with a weight-balanced clustering problem from
imaging in materials science. Here, the class of desired coresets is naturally
confined to those which can be viewed as lowering the resolution of the data.
Hence one would expect that such resolution coresets are inferior to
unrestricted coreset. We show, however, that the restrictions are more than
compensated by utilizing the underlying structure of the data. In particular,
we prove bounds for resolution coresets which improve known bounds in the
relevant dimensions and also lead to significantly faster algorithms practice.


title: The Price of COVID-19 Risk in a Public University

abstract:
  We study the allocation of and compensation for occupational COVID-19 risk at
Auburn University, a large public university in the U.S. In Spring 2021,
approximately half of the face-to-face classes had enrollments above the legal
capacity allowed by a public health order, which followed CDC social distancing
guidelines. We find lower-ranked graduate student teaching assistants and
adjunct instructors were systematically recruited to deliver riskier classes.
Using an IV strategy in which teaching risk is shifted by classroom features
(geometry and furniture), we show instructors who taught at least one risky
class earned $7,400 more than those who did not.


title:
Class-Incremental Learning by Knowledge Distillation with Adaptive
  Feature Consolidation

abstract:
  We present a novel class incremental learning approach based on deep neural
networks, which continually learns new tasks with limited memory for storing
examples in the previous tasks. Our algorithm is based on knowledge
distillation and provides a principled way to maintain the representations of
old models while adjusting to new tasks effectively. The proposed method
estimates the relationship between the representation changes and the resulting
loss increases incurred by model updates. It minimizes the upper bound of the
loss increases using the representations, which exploits the estimated
importance of each feature map within a backbone model. Based on the
importance, the model restricts updates of important features for robustness
while allowing changes in less critical features for flexibility. This
optimization strategy effectively alleviates the notorious catastrophic
forgetting problem despite the limited accessibility of data in the previous
tasks. The experimental results show significant accuracy improvement of the
proposed algorithm over the existing methods on the standard datasets. Code is
available.


title: Equity, diversity, and inclusion in sports analytics

abstract:
  This paper presents a landmark study of equity, diversity and inclusion (EDI)
in the field of sports analytics. We developed a survey that examined personal
and job-related demographics, as well as individual perceptions and experiences
about EDI in the workplace. We sent the survey to individuals in the five major
North American professional leagues, representatives from the Olympic and
Paralympic Committees in Canada and the U.S., the NCAA Division I programs,
companies in sports tech/analytics, and university research groups. Our
findings indicate the presence of a clear dominant group in sports analytics
identifying as: young (72.0%), White (69.5%), heterosexual (89.7%) and male
(82.0%). Within professional sports, males in management positions earned
roughly $30,000 (27%) more on average compared to females. A smaller but
equally alarming pay gap of $17,000 (14%) was found between White and non-White
management personnel. Of concern, females were nearly five times as likely to
experience discrimination and twice as likely to have considered leaving their
job due to isolation or feeling unwelcome. While they had similar levels of
agreement regarding fair processes for rewards and compensation, females
"strongly agreed" less often than males regarding equitable support, equitable
workload, having a voice, and being taken seriously. Over one third (36.3%) of
females indicated that they "strongly agreed" that they must work harder than
others to be valued equally, compared to 9.8% of males. We conclude the paper
with concrete recommendations that could be considered to create a more
equitable, diverse and inclusive environment for individuals working within the
sports analytics sector.


title:
Photon regions, shadow observables and constraints from M87* of a
  charged rotating black hole

abstract:
  Inspired by the observations of supermassive black hole M87* in \emph{Event
Horizon Telescope }(EHT) experiment, a remarkable surge in black hole physics
is to use the black hole shadow's observables to distinguish general relativity
(GR) and modified theories of gravity (MoG), which could also help to disclose
the astrophysical nature of the center black hole in EHT observation. In this
paper, we shall extensively carry out the study of a charged rotating black
hole in conformal gravity, in which the term related with the charge has
different falloffs from the usual Kerr-Newman (KN) black hole. We investigate
the spacetime properties including the horizons, ergospheres and the photon
regions; afterward, we show the boundary of black hole shadow and investigate
its characterized observables. The features closely depend on the spin and
charge parameters, which are compared with those in Kerr and KN black holes.
Then presupposing the M87* a charged rotating black hole in conformal gravity,
we also constrain the black hole parameters via the observation constraints
from EHT experiment. We find that the constraints on the inferred circularity
deviation, $\Delta C \lesssim 0.1$, and on the shadow axial ratio, $1< D_x
\lesssim 4/3$, for the M87* black hole are satisfied for the entire parameter
space of the charged rotating black hole in conformal gravity. However, the
shadow angular diameter $\theta_d = 42 \pm 3 \mu as$ will give upper bound on
the parameter space. Our findings indicate that the current charged rotating
black hole in conformal gravity could be a candidate for astrophysical black
holes. Moreover, the EHT observation on the axial ratio $D_x$ may help us to
distinguish Kerr black hole and the current charged rotating black hole in
conformal gravity in some parameter space.


title: Hierarchical Reinforcement Learning under Mixed Observability

abstract:
  The framework of mixed observable Markov decision processes (MOMDP) models
many robotic domains in which some state variables are fully observable while
others are not. In this work, we identify a significant subclass of MOMDPs
defined by how actions influence the fully observable components of the state
and how those, in turn, influence the partially observable components and the
rewards. This unique property allows for a two-level hierarchical approach we
call HIerarchical Reinforcement Learning under Mixed Observability (HILMO),
which restricts partial observability to the top level while the bottom level
remains fully observable, enabling higher learning efficiency. The top level
produces desired goals to be reached by the bottom level until the task is
solved. We further develop theoretical guarantees to show that our approach can
achieve optimal and quasi-optimal behavior under mild assumptions. Empirical
results on long-horizon continuous control tasks demonstrate the efficacy and
efficiency of our approach in terms of improved success rate, sample
efficiency, and wall-clock training time. We also deploy policies learned in
simulation on a real robot.


title: On Prime Numbers and The Riemann Zeros

abstract:
  The current research regarding the Riemann zeros suggests the existence of a
non-trivial algebraic/analytic structure on the set of Riemann zeros. The
duality between primes and Riemann zeta function zeros suggests some new goals
and aspects to be studied: {\em adelic duality} and the {\em POSet of prime
numbers}.
  The article presents computational evidence of the structure of the imaginary
parts $t$ of the non-trivial zeros of the Riemann zeta function $\rho=1/2+it$,
called in this article the {\em Riemann Spectrum}, using the study of their
distribution.
  The novelty represents in considering the associated characters $p^{it}$,
towards an algebraic point of view, than rather in the sense of Analytic Number
Theory. This structure is tentatively interpreted in terms of adelic
characters, and the duality of the rationals.
  Second, the POSet structure of prime numbers studied, is tentatively mirrored
via duality in the Riemann spectrum. A direct study of the convergence of their
Fourier series, along Pratt trees, is proposed.
  Further considerations, relating the Riemann Spectrum, adelic characters and
distributions, in terms of Hecke idelic characters, local zeta integrals
(Mellin transform) and $\omega$-eigen-distributions, are explored following.


title:
Towards Efficient Sparse Matrix Vector Multiplication on Real
  Processing-In-Memory Systems

abstract:
  Several manufacturers have already started to commercialize near-bank
Processing-In-Memory (PIM) architectures. Near-bank PIM architectures place
simple cores close to DRAM banks and can yield significant performance and
energy improvements in parallel applications by alleviating data access costs.
Real PIM systems can provide high levels of parallelism, large aggregate memory
bandwidth and low memory access latency, thereby being a good fit to accelerate
the widely-used, memory-bound Sparse Matrix Vector Multiplication (SpMV)
kernel.
  This paper provides the first comprehensive analysis of SpMV on a real-world
PIM architecture, and presents SparseP, the first SpMV library for real PIM
architectures. We make two key contributions. First, we design efficient SpMV
algorithms to accelerate the SpMV kernel in current and future PIM systems,
while covering a wide variety of sparse matrices with diverse sparsity
patterns. Second, we provide the first comprehensive analysis of SpMV on a real
PIM architecture. Specifically, we conduct our rigorous experimental analysis
of SpMV kernels in the UPMEM PIM system, the first publicly-available
real-world PIM architecture. Our extensive evaluation provides new insights and
recommendations for software designers and hardware architects to efficiently
accelerate the SpMV kernel on real PIM systems. For more information about our
thorough characterization on the SpMV PIM execution, results, insights and the
open-source SparseP software package [26], we refer the reader to the full
version of the paper [3, 4]. The SparseP software package is publicly and
freely available at https://github.com/CMU-SAFARI/SparseP.


title: Mix-up Self-Supervised Learning for Contrast-agnostic Applications

abstract:
  Contrastive self-supervised learning has attracted significant research
attention recently. It learns effective visual representations from unlabeled
data by embedding augmented views of the same image close to each other while
pushing away embeddings of different images. Despite its great success on
ImageNet classification, COCO object detection, etc., its performance degrades
on contrast-agnostic applications, e.g., medical image classification, where
all images are visually similar to each other. This creates difficulties in
optimizing the embedding space as the distance between images is rather small.
To solve this issue, we present the first mix-up self-supervised learning
framework for contrast-agnostic applications. We address the low variance
across images based on cross-domain mix-up and build the pretext task based on
two synergistic objectives: image reconstruction and transparency prediction.
Experimental results on two benchmark datasets validate the effectiveness of
our method, where an improvement of 2.5% ~ 7.4% in top-1 accuracy was obtained
compared to existing self-supervised learning methods.


title: An objective test tool for pitch extractors' response attributes

abstract:
  We propose an objective measurement method for pitch extractors' responses to
frequency-modulated signals. It enables us to evaluate different pitch
extractors with unified criteria. The method uses extended time-stretched
pulses combined by binary orthogonal sequences. It provides simultaneous
measurement results consisting of the linear and the non-linear time-invariant
responses and random and time-varying responses. We tested representative pitch
extractors using fundamental frequencies spanning 80~Hz to 400~Hz with 1/48
octave steps and produced more than 1000 modulation frequency response plots.
We found that making scientific visualization by animating these plots enables
us to understand different pitch extractors' behavior at once. Such efficient
and effortless inspection is impossible by inspecting all individual plots. The
proposed measurement method with visualization leads to further improvement of
the performance of one of the extractors mentioned above. In other words, our
procedure turns the specific pitch extractor into the best reliable measuring
equipment that is crucial for scientific research. We open-sourced MATLAB codes
of the proposed objective measurement method and visualization procedure.


title:
Safety Verification of Neural Feedback Systems Based on Constrained
  Zonotopes

abstract:
  Artificial neural networks (ANNs) have been utilized in many feedback control
systems and introduced new challenges regarding the safety of the system. This
paper considers the problem of verifying whether the trajectories of a system
with a feedforward neural network (FNN) controller can avoid unsafe regions,
using a constrained zonotope-based reachability analysis approach. FNNs with
the rectified linear unit activation function are considered in this work. A
novel set-based method is proposed to compute both exact and over-approximated
reachable sets for linear discrete-time systems with FNN controllers, and
linear program-based sufficient conditions are presented to certify the safety
of the neural feedback systems. Reachability analysis and safety verification
for neural feedback systems with nonlinear models are also considered. The
computational efficiency and accuracy of the proposed method are demonstrated
by two numerical examples where a comparison with state-of-the-art methods is
also provided.


title: Handling of constraints in multiobjective blackbox optimization

abstract:
  This work proposes the integration of two new constraint-handling approaches
into the blackbox constrained multiobjective optimization algorithm
DMulti-MADS, an extension of the Mesh Adaptive Direct Search (MADS) algorithm
for single-objective constrained optimization. The constraints are aggregated
into a single constraint violation function which is used either in a two-phase
approach, where research of a feasible point is prioritized if not available
before improving the current solution set, or in a progressive barrier
approach, where any trial point whose constraint violation function values are
above a threshold are rejected. This threshold is progressively decreased along
the iterations. As in the single-objective case, it is proved that these two
variants generate feasible and/or infeasible sequences which converge either in
the feasible case to a set of local Pareto optimal points or in the infeasible
case to Clarke stationary points according to the constraint violation
function. Computational experiments show that these two approaches are
competitive with other state-of-the-art algorithms.


title:
On the linear $\ell$-intersection pair of codes over a finite principal
  ideal ring

abstract:
  Generalizing the linear complementary dual, the linear complementary pair and
the hull of codes, we introduce linear $\ell$-intersection pair of codes over a
finite principal ideal ring $R,$ for some positive integer $\ell$. Two linear
codes are said to be a linear $\ell$-intersection pair of codes over $R$ if the
cardinality of the intersection of two linear codes are equal to $q^{\ell}$,
where $q$ is the cardinality of the radical of $R.$ In this paper, we study
linear $\ell$-intersection pair of codes over $R$ in a very general setting by
a uniform method. We provide a necessary and sufficient condition for a
non-free (or free) linear $\ell$-intersection pair of codes over $R.$


title: Cosmological Inflation in f(Q, T) Gravity

abstract:
  We study the cosmological inflation within the context of f(Q, T) gravity,
wherein Q is the nonmetricity scalar and T is the trace of the matter
energy-momentum tensor. By choosing a linear combination of Q and T, we first
analyze the realization of an inflationary scenario driven via the geometrical
effects of f(Q, T) gravity and then, we obtain the modified slow-roll
parameters, the scalar and the tensor spectral indices, and the
tensor-to-scalar ratio for the proposed model. In addition, by choosing a few
appropriate inflationary potentials and by applying the slow-roll
approximations, we calculate these inflationary observables in the presence of
an inflaton scalar field. The results indicate that by properly restricting the
free parameters, the proposed model provides appropriate predictions that are
consistent with the observational data obtained from the Planck 2018.


title:
StyleWaveGAN: Style-based synthesis of drum sounds with extensive
  controls using generative adversarial networks

abstract:
  In this paper we introduce StyleWaveGAN, a style-based drum sound generator
that is a variation of StyleGAN, a state-of-the-art image generator. By
conditioning StyleWaveGAN on both the type of drum and several audio
descriptors, we are able to synthesize waveforms faster than real-time on a GPU
directly in CD quality up to a duration of 1.5s while retaining a considerable
amount of control over the generation. We also introduce an alternative to the
progressive growing of GANs and experimented on the effect of dataset balancing
for generative tasks. The experiments are carried out on an augmented subset of
a publicly available dataset comprised of different drums and cymbals. We
evaluate against two recent drum generators, WaveGAN and NeuroDrum,
demonstrating significantly improved generation quality (measured with the
Frechet Audio Distance) and interesting results with perceptual features.


title: Complexity and entanglement in non-local computation and holography

abstract:
  Does gravity constrain computation? We study this question using the AdS/CFT
correspondence, where computation in the presence of gravity can be related to
non-gravitational physics in the boundary theory. In AdS/CFT, computations
which happen locally in the bulk are implemented in a particular non-local form
in the boundary, which in general requires distributed entanglement. In more
detail, we recall that for a large class of bulk subregions the area of a
surface called the ridge is equal to the mutual information available in the
boundary to perform the computation non-locally. We then argue the complexity
of the local operation controls the amount of entanglement needed to implement
it non-locally, and in particular complexity and entanglement cost are related
by a polynomial. If this relationship holds, gravity constrains the complexity
of operations within these regions to be polynomial in the area of the ridge.


title:
Mechanical and electrical properties of MWCNT/PP films and structural
  health monitoring of GF/PP joints

abstract:
  While welding of thermoplastic composites (TPCs) is a promising rivetless
method to reduce weight, higher confidence in joints' structural integrity and
failure prediction must be achieved for widespread use in industry. In this
work, we present an innovative study on damage detection for ultrasonically
welded TPC joints with multiwalled carbon nanotubes (MWCNTs) and embedded
buckypaper films. MWCNTs show promise for structural health monitoring (SHM) of
composite joints, assembled by adhesive bonding or fusion bonding, through
electrical resistance changes. This study focuses on investigating
multifunctional films and their suitability for ultrasonic welding (USW) of
TPCs, using two approaches: 1) MWCNT/filled polypropylene (PP) nanocomposites
prepared via solvent dispersion, and 2) high conductivity MWCNT buckypaper
embedded between PP films by hot pressing. Nanocomposite formulations
containing 5 wt and 10 wt MWCNTs were synthesized using solvent dispersion
method, followed by compression molding to manufacture films. The effect of
MWCNT concentration on electrical and dynamic mechanical behavior of
multifunctional films was examined with a Sourcemeter and Dynamic Mechanical
Analyzer, and a comparison was made between 5 to 20 wt MWCNT-PP films based on
previous research. Glass fiber/polypropylene (GF/PP) composite joints were
ultrasonically welded in a single lap shear configuration using buckypaper and
MWCNT/PP films. Furthermore, electrical resistance measurements were carried
out for joints under bending loads. It was observed that 15 wt and 20 wt
MWCNT/PP films had higher stability and sensitivity for resistance response
than embedded buckypaper and films with low MWCNT contents, demonstrating their
suitability for USW and potential for SHM.


title:
MAANG? MANGA? Characterizing Spontaneous Ideation Contest on Social
  Media

abstract:
  Social media is not only a place for people to communicate on a daily matter
but also a virtual venue to transmit and exchange various ideas. Such ideas are
known as the raw voices of potential consumers, which come from a wide range of
people who may not participate in consumer surveys, and therefore their
opinions may contain high value to companies. However, how users share their
ideas on social media is still underexplored. This study investigates a
spontaneous ideation contest about a generic term for new Big Tech companies,
which occurred when Facebook changed its name to Meta. We constructed a
comprehensive dataset of tweets containing candidates and examined how they
were suggested, spread, and exchanged by social media users. Our findings
indicate that different ideas are better on different metrics. The ranking of
ideas was not decided immediately after the idea contest started. The first
people to post ideas have smaller followers than those who post secondarily or
who only share the idea. We also confirmed that replies accumulate unique
ideas, but most of them are added in the first depth in reply trees. This study
would promote the use of social media as a part of open innovation and
co-creation processes in the industry.


title:
Measuring pitch extractors' response to frequency-modulated
  multi-component signals

abstract:
  This article focuses on the research tool for investigating the fundamental
frequencies of voiced sounds. We introduce an objective and informative
measurement method of pitch extractors' response to frequency-modulated tones.
The method uses a new test signal for acoustic system analysis. The test signal
enables simultaneous measurement of the extractors' responses. They are the
modulation frequency response and the total distortion, including
intermodulation distortions. We applied this method to various pitch extractors
and placed them on several performance maps. We used the proposed method to
fine-tune one of the extractors to make it the best fit tool for scientific
research of voice fundamental frequencies.


title:
Escaping High-order Saddles in Policy Optimization for Linear Quadratic
  Gaussian (LQG) Control

abstract:
  First order policy optimization has been widely used in reinforcement
learning. It guarantees to find the optimal policy for the state-feedback
linear quadratic regulator (LQR). However, the performance of policy
optimization remains unclear for the linear quadratic Gaussian (LQG) control
where the LQG cost has spurious suboptimal stationary points. In this paper, we
introduce a novel perturbed policy gradient (PGD) method to escape a large
class of bad stationary points (including high-order saddles). In particular,
based on the specific structure of LQG, we introduce a novel reparameterization
procedure which converts the iterate from a high-order saddle to a strict
saddle, from which standard random perturbations in PGD can escape efficiently.
We further characterize the high-order saddles that can be escaped by our
algorithm.


title: Charmonium tetraquarks or a new light quark?

abstract:
  The existence of a fourth flavor of down-type quark with a mass of
approximately 1.6 GeV is hypothesized. The right-handed component of this quark
is assumed to decay to a right-handed charm quark and a virtual W boson. Many
of the recently discovered exotic charged charmonium-like resonances are
re-interpreted as mesons involving the new quark.


title:
Artificial Intelligence for 6G Networks: Technology Advancement and
  Standardization

abstract:
  With the deployment of 5G networks, standards organizations have started
working on the design phase for sixth-generation (6G) networks. 6G networks
will be immensely complex, requiring more deployment time, cost and management
efforts. On the other hand, mobile network operators demand these networks to
be intelligent, self-organizing, and cost-effective to reduce operating
expenses (OPEX). Machine learning (ML), a branch of artificial intelligence
(AI), is the answer to many of these challenges providing pragmatic solutions,
which can entirely change the future of wireless network technologies. By using
some case study examples, we briefly examine the most compelling problems,
particularly at the physical (PHY) and link layers in cellular networks where
ML can bring significant gains. We also review standardization activities in
relation to the use of ML in wireless networks and future timeline on readiness
of standardization bodies to adapt to these changes. Finally, we highlight
major issues in ML use in the wireless technology, and provide potential
directions to mitigate some of them in 6G wireless networks.


title: Optical computing of quantum revivals

abstract:
  Interference is the mechanism through which waves can be structured into the
most fascinating patterns. While for sensing, imaging, trapping, or in
fundamental investigations, structured waves play nowadays an important role
and are becoming subject of many interesting studies. Using a coherent optical
field as a probe, we show how to structure light into distributions presenting
collapse and revival structures in its wavefront. These distributions are
obtained from the Fourier spectrum of an arrangement of aperiodic diffracting
structures. Interestingly, the resulting interference may present quasiperiodic
structures of diffraction peaks on a number of distance scales, even though the
diffracting structure is not periodic. We establish an analogy with revival
phenomena in the evolution of quantum mechanical systems and illustrate this
computation numerically and experimentally, obtaining excellent agreement with
the proposed theory.


title:
BERT-Assisted Semantic Annotation Correction for Emotion-Related
  Questions

abstract:
  Annotated data have traditionally been used to provide the input for training
a supervised machine learning (ML) model. However, current pre-trained ML
models for natural language processing (NLP) contain embedded linguistic
information that can be used to inform the annotation process. We use the BERT
neural language model to feed information back into an annotation task that
involves semantic labelling of dialog behavior in a question-asking game called
Emotion Twenty Questions (EMO20Q). First we describe the background of BERT,
the EMO20Q data, and assisted annotation tasks. Then we describe the methods
for fine-tuning BERT for the purpose of checking the annotated labels. To do
this, we use the paraphrase task as a way to check that all utterances with the
same annotation label are classified as paraphrases of each other. We show this
method to be an effective way to assess and revise annotations of textual user
data with complex, utterance-level semantic labels.


title: Dually affine Information Geometry modeled on a Banach space

abstract:
  In this chapter, we study Information Geometry from a particular
non-parametric or functional point of view. The basic model is a probabilities
subset usually specified by regularity conditions. For example, probability
measures mutually absolutely continuous or probability densities with a given
degree of smoothness. We construct a manifold structure by giving an atlas of
charts as mappings from probabilities to a Banach space. The charts we use are
quite peculiar in that we consider only instances where the transition mappings
are affine. We chose a particular expression of the tangent and cotangent
bundles in this affine setting.


title: Ab initio description of Si, P, S, Cl and Ar isotopes in sd-shell

abstract:
  In the present work, we have reported comprehensive study of the $sd$-shell
nuclei in the range $Z = 14 - 18$ with neutron number varying from $N = 9$ to
$N = 20$ using the microscopic effective valence shell interactions. These
effective $sd$-shell interactions are developed using the ab initio no-core
shell model wave functions and the Okubo-Lee-Suzuki transformation method. The
valence shell effective interactions, which are used in this project, are N3LO,
JISP16 and DJ16A interactions. For comparison, we have also performed shell
model calculations with the empirical USDB interaction. Theoretically
calculated shell model results are compared with the experimental data, to
check the predictive strength of the microscopic interactions. It is found that
the binding energies of the ground states are better reproduced with the DJ16A
interaction as compared to other microscopic interactions. Spin-tensor
decomposition of two-body interaction is presented to understand the
contributions from central, vector and tensor components into these
interactions. Electromagnetic properties of these isotopic chains have been
studied. Spectroscopic strengths of $^{23}$Al($d$,$n$)$^{24}$Si are calculated
for the newly performed experiment at NSCL. The beta-decay properties of
$^{32}$Ar are also determined for recently available experimental data.


title: Open inverted bell and bell formation during the washing of vials

abstract:
  A range of fascinating flow features was observed while cleaning or filling
the medicine or liquid vials in the kitchen sink by serendipity. Here, we
present the formation of an open inverted bell and bell-shaped flow structures
formed from the water sheet in a new geometric arrangement, hitherto unknown.
When a laminar jet impinges on the surface of the liquid in the vial of
marginally larger or similar diameter, an inverted open water bell is formed,
which gradually changes into a flat water sheet to a classical water bell as
the flow rate is increased. The inverted water bell structures disintegrate by
forming water ridges which finally break down into different sizes of droplets.


title:
Deep Algebraic Fitting for Multiple Circle Primitives Extraction from
  Raw Point Clouds

abstract:
  The shape of circle is one of fundamental geometric primitives of man-made
engineering objects. Thus, extraction of circles from scanned point clouds is a
quite important task in 3D geometry data processing. However, existing circle
extraction methods either are sensitive to the quality of raw point clouds when
classifying circle-boundary points, or require well-designed fitting functions
when regressing circle parameters. To relieve the challenges, we propose an
end-to-end Point Cloud Circle Algebraic Fitting Network (Circle-Net) based on a
synergy of deep circle-boundary point feature learning and weighted algebraic
fitting. First, we design a circle-boundary learning module, which considers
local and global neighboring contexts of each point, to detect all potential
circle-boundary points. Second, we develop a deep feature based circle
parameter learning module for weighted algebraic fitting, without designing any
weight metric, to avoid the influence of outliers during fitting. Unlike most
of the cutting-edge circle extraction wisdoms, the proposed
classification-and-fitting modules are originally co-trained with a
comprehensive loss to enhance the quality of extracted circles.Comparisons on
the established dataset and real-scanned point clouds exhibit clear
improvements of Circle-Net over SOTAs in terms of both noise-robustness and
extraction accuracy. We will release our code, model, and data for both
training and evaluation on GitHub upon publication.


title:
On the Packing of Stiff Rods on Ellipsoids Part II -- Entropy and Self
  Avoidance

abstract:
  Using a statistical - mechanics approach, we study the effects of geometry
and entropy on the ordering of slender filaments inside non-isotropic
containers, considering cortical microtubules in plant cells, and packing of
genetic material inside viral capsids as concrete examples. Working in a mean -
field approximation, we show analytically how the shape of container, together
with self avoidance, affects the ordering of stiff rods. We find that the
strength of the self-avoiding interaction plays a significant role in the
preferred packing orientation, leading to a first-order transition for oblate
cells, where the preferred orientation changes from azimuthal, along the
equator, to a polar one, when self avoidance is strong enough. While for
prolate spheroids the ground state is always a polar like order, strong self
avoidance result with a deep meta-stable state along the equator. We compute
the critical surface describing the transition between azimuthal and polar
ordering in the three dimensional parameter space (persistence length,
container shape, and self avoidance) and show that the critical behavior of
this systems, in fact, relates to the butterfly catastrophe model. We compare
these results to the pure mechanical study in arXiv:1910.08171, and discuss
similarities and differences. We calculate the pressure and shear stress
applied by the filament on the surface, and the injection force needed to be
applied on the filament in order to insert it into the volume

categories: cond-mat.soft cond-mat.mtrl-sci cond-mat.stat-mech nlin.AO


title:
Application of Stochastic Optimization Techniques to the Unit Commitment
  Problem -- A Review

abstract:
  Due to the established energy production methods contribution to the climate
crisis, renewable energy is to replace a substantial part of coal or nuclear
plants to prevent greenhouse gases or toxic waste entering the atmosphere. This
relatively quick shift in energy production, primarily pushed by increasing
political and economical pressure, requires enormous effort on the part of the
energy providers to balance out production fluctuations. Consequently, a lot of
research is conducted in the key area of stochastic unit commitment (UC) on
electrical grids and microgrids. The term unit commitment includes a large
variety of optimization techniques, and in this paper we will review recent
developments in this area. We start by giving an overview over different
problem definitions and stochastic optimization procedures, to then assess
recent contributions to this topic. We therefore compare the proposals and case
studies of several papers.


title:
Word separation in continuous sign language using isolated signs and
  post-processing

abstract:
  Continuous Sign Language Recognition (CSLR) is a long challenging task in
Computer Vision due to the difficulties in detecting the explicit boundaries
between the words in a sign sentence. To deal with this challenge, we propose a
two-stage model. In the first stage, the predictor model, which includes a
combination of CNN, SVD, and LSTM, is trained with the isolated signs. In the
second stage, we apply a post-processing algorithm to the Softmax outputs
obtained from the first part of the model in order to separate the isolated
signs in the continuous signs. Due to the lack of a large dataset, including
both the sign sequences and the corresponding isolated signs, two public
datasets in Isolated Sign Language Recognition (ISLR), RKS-PERSIANSIGN and
ASLVID, are used for evaluation. Results of the continuous sign videos confirm
the efficiency of the proposed model to deal with isolated sign boundaries
detection.


title: Matrices over commutative rings as sum of higher powers

abstract:
  On the Waring's problems for matrices over a commutative ring, there are some
trace conditions provided for matrices eligibly expressed as a sum of $k$-th
powers with $k=2,3,4,5,6,7,8$ in several literatures. In this paper, we provide
the similar conditions for matrices written as a sum of $k$-th powers with
$k=9,10,11,12,13,14,15,16$.


title:
A UCB-based Tree Search Approach to Joint Verification-Correction
  Strategy for Large Scale Systems

abstract:
  Verification planning is a sequential decision-making problem that specifies
a set of verification activities (VA) and correction activities (CA) at
different phases of system development. While VAs are used to identify errors
and defects, CAs also play important roles in system verification as they
correct the identified errors and defects. However, current planning methods
only consider VAs as decision choices. Because VAs and CAs have different
activity spaces, planning a joint verification-correction strategy (JVCS) is
still challenging, especially for large-size systems. Here we introduce a
UCB-based tree search approach to search for near-optimal JVCSs. First,
verification planning is simplified as repeatable bandit problems and an upper
confidence bound rule for repeatable bandits (UCBRB) is presented with the
optimal regret bound. Next, a tree search algorithm is proposed to search for
feasible JVCSs. A tree-based ensemble learning model is also used to extend the
tree search algorithm to handle local optimality issues. The proposed approach
is evaluated on the notional case of a communication system.


title: Learning to Augment for Casual User Recommendation

abstract:
  Users who come to recommendation platforms are heterogeneous in activity
levels. There usually exists a group of core users who visit the platform
regularly and consume a large body of content upon each visit, while others are
casual users who tend to visit the platform occasionally and consume less each
time. As a result, consumption activities from core users often dominate the
training data used for learning. As core users can exhibit different activity
patterns from casual users, recommender systems trained on historical user
activity data usually achieve much worse performance on casual users than core
users. To bridge the gap, we propose a model-agnostic framework L2Aug to
improve recommendations for casual users through data augmentation, without
sacrificing core user experience. L2Aug is powered by a data augmentor that
learns to generate augmented interaction sequences, in order to fine-tune and
optimize the performance of the recommendation system for casual users. On four
real-world public datasets, L2Aug outperforms other treatment methods and
achieves the best sequential recommendation performance for both casual and
core users. We also test L2Aug in an online simulation environment with
real-time feedback to further validate its efficacy, and showcase its
flexibility in supporting different augmentation actions.


title: On generalized lacunary series

abstract:
  Given lacunary sequence of integers, $n_k$, $n_{k+1}/n_k>\lambda>1$, we
define a new sequence $\{m_k\}$ formed by all possible $l$-wise sums $\pm
n_{k_1}\pm n_{k_2}\pm \ldots\pm n_{k_l}$. We prove if $\lambda>\lambda_l$, then
any series \begin{equation}
  \sum_kc_ke^{im_kx},\qquad (1) \end{equation} with $\sum_k|c_k|^2<\infty$
converges almost everywhere after any rearrangement of the terms, where
$1<\lambda_l<2$ is a certain critical value. We establish this property,
proving a new Khintchine type inequality $\|S\|_p\le C_{l,\lambda,p}\|S\|_2$,
$p>2$, where $S$ is a finite sum of form (1).
  For $\lambda\ge 3$, we also establish a sharp rate $p^{l/2}$ for the growth
of the constant $C_{l,\lambda,p}$ as $p\to\infty$. Such an estimate for the
Rademacher chaos sums was proved independently by Bonami and Kiener. In the
case of $\lambda\ge 3$ we also establish some inverse convergence properties of
series (1): 1) if series (1) converges a.e., then $\sum_k|c_k|^2<\infty$, 2) if
it a.e. converges to zero, then $c_k=0$.


title:
SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single
  Image

abstract:
  Despite the rapid development of Neural Radiance Field (NeRF), the necessity
of dense covers largely prohibits its wider applications. While several recent
works have attempted to address this issue, they either operate with sparse
views (yet still, a few of them) or on simple objects/scenes. In this work, we
consider a more ambitious task: training neural radiance field, over
realistically complex visual scenes, by "looking only once", i.e., using only a
single view. To attain this goal, we present a Single View NeRF (SinNeRF)
framework consisting of thoughtfully designed semantic and geometry
regularizations. Specifically, SinNeRF constructs a semi-supervised learning
process, where we introduce and propagate geometry pseudo labels and semantic
pseudo labels to guide the progressive training process. Extensive experiments
are conducted on complex scene benchmarks, including NeRF synthetic dataset,
Local Light Field Fusion dataset, and DTU dataset. We show that even without
pre-training on multi-view datasets, SinNeRF can yield photo-realistic
novel-view synthesis results. Under the single image setting, SinNeRF
significantly outperforms the current state-of-the-art NeRF baselines in all
cases. Project page: https://vita-group.github.io/SinNeRF/


title: AutoProtoNet: Interpretability for Prototypical Networks

abstract:
  In meta-learning approaches, it is difficult for a practitioner to make sense
of what kind of representations the model employs. Without this ability, it can
be difficult to both understand what the model knows as well as to make
meaningful corrections. To address these challenges, we introduce AutoProtoNet,
which builds interpretability into Prototypical Networks by training an
embedding space suitable for reconstructing inputs, while remaining convenient
for few-shot learning. We demonstrate how points in this embedding space can be
visualized and used to understand class representations. We also devise a
prototype refinement method, which allows a human to debug inadequate
classification parameters. We use this debugging technique on a custom
classification task and find that it leads to accuracy improvements on a
validation set consisting of in-the-wild images. We advocate for
interpretability in meta-learning approaches and show that there are
interactive ways for a human to enhance meta-learning algorithms.


title:
Beyond Smoothness: Incorporating Low-Rank Analysis into Nonparametric
  Density Estimation

abstract:
  The construction and theoretical analysis of the most popular universally
consistent nonparametric density estimators hinge on one functional property:
smoothness. In this paper we investigate the theoretical implications of
incorporating a multi-view latent variable model, a type of low-rank model,
into nonparametric density estimation. To do this we perform extensive analysis
on histogram-style estimators that integrate a multi-view model. Our analysis
culminates in showing that there exists a universally consistent
histogram-style estimator that converges to any multi-view model with a finite
number of Lipschitz continuous components at a rate of
$\widetilde{O}(1/\sqrt[3]{n})$ in $L^1$ error. In contrast, the standard
histogram estimator can converge at a rate slower than $1/\sqrt[d]{n}$ on the
same class of densities. We also introduce a new nonparametric latent variable
model based on the Tucker decomposition. A rudimentary implementation of our
estimators experimentally demonstrates a considerable performance improvement
over the standard histogram estimator. We also provide a thorough analysis of
the sample complexity of our Tucker decomposition-based model and a variety of
other results. Thus, our paper provides solid theoretical foundations for
extending low-rank techniques to the nonparametric setting


title:
Upscaling of a reaction-diffusion-convection problem with exploding
  non-linear drift

abstract:
  We study a reaction-diffusion-convection problem with nonlinear drift posed
in a domain with periodically arranged obstacles. The non-linearity in the
drift is linked to the hydrodynamic limit of a totally asymmetric simple
exclusion process (TASEP) governing a population of interacting particles
crossing a domain with obstacle. Because of the imposed large drift scaling,
this nonlinearity is expected to explode in the limit of a vanishing scaling
parameter. As main working techniques, we employ two-scale formal
homogenization asymptotics with drift to derive the corresponding upscaled
model equations as well as the structure of the effective transport tensors.
Finally, we use Schauder's fixed point theorem as well as monotonicity
arguments to study the weak solvability of the upscaled model posed in an
unbounded domain. This study wants to contribute with theoretical understanding
needed when designing thin composite materials that are resistant to high
velocity impacts.


title:
Automatic Transformation of Natural to Unified Modeling Language: A
  Systematic Review

abstract:
  Context: Processing Software Requirement Specifications (SRS) manually takes
a much longer time for requirement analysts in software engineering.
Researchers have been working on making an automatic approach to ease this
task. Most of the existing approaches require some intervention from an analyst
or are challenging to use. Some automatic and semi-automatic approaches were
developed based on heuristic rules or machine learning algorithms. However,
there are various constraints to the existing approaches of UML generation,
such as restriction on ambiguity, length or structure, anaphora,
incompleteness, atomicity of input text, requirements of domain ontology, etc.
Objective: This study aims to better understand the effectiveness of existing
systems and provide a conceptual framework with further improvement guidelines.
Method: We performed a systematic literature review (SLR). We conducted our
study selection into two phases and selected 70 papers. We conducted
quantitative and qualitative analyses by manually extracting information,
cross-checking, and validating our findings. Result: We described the existing
approaches and revealed the issues observed in these works. We identified and
clustered both the limitations and benefits of selected articles. Conclusion:
This research upholds the necessity of a common dataset and evaluation
framework to extend the research consistently. It also describes the
significance of natural language processing obstacles researchers face. In
addition, it creates a path forward for future research.


title:
Exploiting Local and Global Features in Transformer-based Extreme
  Multi-label Text Classification

abstract:
  Extreme multi-label text classification (XMTC) is the task of tagging each
document with the relevant labels from a very large space of predefined
categories. Recently, large pre-trained Transformer models have made
significant performance improvements in XMTC, which typically use the embedding
of the special CLS token to represent the entire document semantics as a global
feature vector, and match it against candidate labels. However, we argue that
such a global feature vector may not be sufficient to represent different
granularity levels of semantics in the document, and that complementing it with
the local word-level features could bring additional gains. Based on this
insight, we propose an approach that combines both the local and global
features produced by Transformer models to improve the prediction power of the
classifier. Our experiments show that the proposed model either outperforms or
is comparable to the state-of-the-art methods on benchmark datasets.


title:
The Effects of the Environment and Linear Actuators on Robot
  Morphologies

abstract:
  The field of evolutionary robotics uses principles of natural evolution to
design robots. In this paper, we study the effect of adding a new module
inspired by the skeletal muscle to the existing RoboGen framework: the linear
actuator. Additionally, we investigate how robots evolved in a plain
environment differ from robots evolved in a rough environment. We consider the
task of directed locomotion for comparing evolved robot morphologies. The
results show that the addition of the linear actuator does not have a
significant impact on the performance and morphologies of robots evolved in a
plain environment. However, we find significant differences in the morphologies
of robots evolved in a plain environment and robots evolved in a rough
environment. We find that more complex behavior and morphologies emerge when we
change the terrain of the environment.


title: 3D Path Following and L1 Adaptive Control for Underwater Vehicles

abstract:
  This paper addresses the problem of guidance and control of underwater
vehicles. A multi-level control strategy is used to determine (1) outer-loop
path-following commands and (2) inner-loop actuation commands. Specifically, a
line-of-sight path-following algorithm is used to guide the vehicle along a
three-dimensional path, and an $\mathcal{L}_1$ adaptive control algorithm is
used to determine the low-level rudder commands to accomplish path following.
The performance bounds of these outer- and inner-loop control algorithms are
presented. Numerical results obtained using a physics-based Simulink model are
used to aid in visualization of the control algorithm's performance.


title: Banach's isometric subspace problem in dimension four

abstract:
  We prove that, if all intersections of a convex body $B\subset\mathbb R^4$
with 3-dimensional linear subspaces are linearly equivalent,then $B$ is a
centered ellipsoid. This settles the case $n=3$ of an old problem by Banach:
Let $V$ be a normed vector space whose $n$-dimesional linear subspaces, for a
fixed $n<\dim V$, are all isometric. Is $V$ necessarily an inner product space?
  The dimensions $n=3$ and $\dim V=4$ are the first case where the answer was
not known, and in this case global topological methods do not help. Our proof
is based on a differential geometric approach.


title:
Active Foam: The Adaptive Mechanics of 2D Air-Liquid Foam under Cyclic
  Inflation

abstract:
  Foam is a canonical example of disordered soft matter where local force
balance leads to the competition of many metastable configurations. Here we
present an experimental and theoretical framework for "active foam" where an
individual voxel inflates and deflates periodically. We explore structural
adaptations of this disordered material with respect to added activity.
Periodic injection of local activity leads to a small number of irreversible
and reversible T1 transitions throughout the foam. Regardless of the presence
of T1 transitions, individual vertices will displace outwards and subsequently
return back to their approximate original radial position; this radial
displacement follows an inverse law. Surprisingly, each return trajectory does
not retrace its outbound path but rather encloses a finite area, with either a
CW or CCW direction - which we define as a local swirl. These swirls form
coherent patterns spanning the entire scale of the material. Using a reduced
order dynamical model, we demonstrate that swirl arises as a direct consequence
of spatial disorder in the local micro-structure. Building a first principles
model, we demonstrate that disorder and strain rate control a crossover between
cooperation and competition between swirls in adjacent vertices. Over longer
timescales, the region around the active voxel structurally adapts from a
higher-energy metastable state to a lower energy state, indicative of a
localized annealing process. Finally, we develop a statistical toy-model that
evolves edge lengths based on a set of simple rules to explore how this class
of materials adapts over time as a function of initial structure. Adding
activity to foam couples structural disorder and adaptive dynamics to encourage
the development of a new class of abiotic, cellularized active matter.

categories: cond-mat.soft cond-mat.dis-nn nlin.AO physics.flu-dyn


title: Two-sided cartesian fibrations of synthetic $(\infty,1)$-categories

abstract:
  Within the framework of Riehl-Shulman's synthetic $(\infty,1)$-category
theory, we present a theory of two-sided cartesian fibrations. Central results
are several characterizations of the two-sidedness condition \`{a} la
Chevalley, Gray, Street, and Riehl-Verity, a two-sided Yoneda Lemma, as well as
the proof of several closure properties.
  Along the way, we also define and investigate a notion of fibered or sliced
fibration which is used later to develop the two-sided case in a modular
fashion. We also briefly discuss discrete two-sided cartesian fibrations in
this setting, corresponding to $(\infty,1)$-distributors.
  The systematics of our definitions and results closely follows Riehl-Verity's
$\infty$-cosmos theory, but formulated internally to Riehl-Shulman's simplicial
extension of homotopy type theory. All the constructions and proofs in this
framework are by design invariant under homotopy equivalence. Semantically, the
synthetic $(\infty,1)$-categories correspond to internal
$(\infty,1)$-categories implemented as Rezk objects in an arbitrary given
$(\infty,1)$-topos.


title:
Probing biexciton in monolayer WS$_2$ through controlled many-body
  interaction

abstract:
  The monolayers of semiconducting transition metal dichalcogenides host
strongly bound excitonic complexes and are an excellent platform for exploring
many-body physics. Here we demonstrate a controlled kinetic manipulation of the
five-particle excitonic complex, the charged biexciton, through a systematic
dependence of the biexciton peak on excitation power, gate voltage, and
temperature using steady-state and time-resolved photoluminescence (PL). With
the help of a combination of the experimental data and a rate equation model,
we argue that the binding energy of the charged biexciton is less than the
spectral separation of its peak from the neutral exciton. We also note that
while the momentum-direct radiative recombination of the neutral exciton is
restricted within the light cone, such restriction is relaxed for a charged
biexciton recombination due to the presence of near-parallel excited and final
states in the momentum space.

categories: cond-mat.mes-hall cond-mat.mtrl-sci physics.app-ph physics.optics


title:
Comparison of non-archimedean and logarithmic mirror constructions via
  the Frobenius structure theorem

abstract:
  For a log Calabi Yau pair (X,D) with X\D affine, satisfying either assumption
1.1 of "The canonical wall structure and intrinsic mirror symmetry" or contains
a Zariski dense torus, we prove under the condition that D is the support of a
nef divisor, that structure constants defining a trace form on the mirror
algebra constructed by Gross-Siebert are given by the naive curve count defined
by Keel-Yu in definition 1.1 of "The Frobenius structure theorem for log
Calabi-Yau varieties containing a torus". As a corollary, we deduce the
equality of the mirror algebras constructed by Gross-Siebert and Keel-Yu in the
case X\D contains a Zariski dense torus. In addition, we use this result to
prove a mirror conjecture proposed by Mandel in "Fano mirror periods from the
Frobenius structure conjecture" for Fano pairs satisfying assumption 1.1.


title: Time-lapse data matching using a recurrent neural network approach

abstract:
  Time-lapse seismic data acquisition is an essential tool to monitor changes
in a reservoir due to fluid injection, such as CO$_2$ injection. By acquiring
multiple seismic surveys in the exact location, we can identify the reservoir
changes by analyzing the difference in the data. However, such analysis can be
skewed by the near-surface seasonal velocity variations, inaccuracy in the
acquisition parameters, and other inevitable noise. The common practice
(cross-equalization) to address this problem uses the part of the data where
changes are not expected to design a matching filter and then apply it to the
whole data, including the reservoir area. Like cross-equalization, we train a
recurrent neural network on parts of the data excluding the reservoir area and
then infer the reservoir-related data. The recurrent neural network can learn
the time dependency of the data, unlike the matching filter that processes the
data based on the local information obtained in the filter window. We
demonstrate the method of matching the data in various examples and compare it
with the conventional matching filter. Specifically, we start by demonstrating
the ability of the approach in matching two traces and then test the method on
a pre-stack 2D synthetic data. Then, we verify the enhancements of the 4D
signal by providing RTM images. We measure the repeatability using normalized
root mean square and predictability metrics and show that in some cases, it
overcomes the matching filter approach.


title: A-ACT: Action Anticipation through Cycle Transformations

abstract:
  While action anticipation has garnered a lot of research interest recently,
most of the works focus on anticipating future action directly through observed
visual cues only. In this work, we take a step back to analyze how the human
capability to anticipate the future can be transferred to machine learning
algorithms. To incorporate this ability in intelligent systems a question worth
pondering upon is how exactly do we anticipate? Is it by anticipating future
actions from past experiences? Or is it by simulating possible scenarios based
on cues from the present? A recent study on human psychology explains that, in
anticipating an occurrence, the human brain counts on both systems. In this
work, we study the impact of each system for the task of action anticipation
and introduce a paradigm to integrate them in a learning framework. We believe
that intelligent systems designed by leveraging the psychological anticipation
models will do a more nuanced job at the task of human action prediction.
Furthermore, we introduce cyclic transformation in the temporal dimension in
feature and semantic label space to instill the human ability of reasoning of
past actions based on the predicted future. Experiments on Epic-Kitchen,
Breakfast, and 50Salads dataset demonstrate that the action anticipation model
learned using a combination of the two systems along with the cycle
transformation performs favorably against various state-of-the-art approaches.


title: TripleNet: A Low Computing Power Platform of Low-Parameter Network

abstract:
  With the excellent performance of deep learning technology in the field of
computer vision, convolutional neural network (CNN) architecture has become the
main backbone of computer vision task technology. With the widespread use of
mobile devices, neural network models based on platforms with low computing
power are gradually being paid attention. This paper proposes a lightweight
convolutional neural network model, TripleNet, an improved convolutional neural
network based on HarDNet and ThreshNet, inheriting the advantages of small
memory usage and low power consumption of the mentioned two models. TripleNet
uses three different convolutional layers combined into a new model
architecture, which has less number of parameters than that of HarDNet and
ThreshNet. CIFAR-10 and SVHN datasets were used for image classification by
employing HarDNet, ThreshNet, and our proposed TripleNet for verification.
Experimental results show that, compared with HarDNet, TripleNet's parameters
are reduced by 66% and its accuracy rate is increased by 18%; compared with
ThreshNet, TripleNet's parameters are reduced by 37% and its accuracy rate is
increased by 5%.


title: Progressive Minimal Path Method with Embedded CNN

abstract:
  We propose Path-CNN, a method for the segmentation of centerlines of tubular
structures by embedding convolutional neural networks (CNNs) into the
progressive minimal path method. Minimal path methods are widely used for
topology-aware centerline segmentation, but usually these methods rely on weak,
hand-tuned image features. In contrast, CNNs use strong image features which
are learned automatically from images. But CNNs usually do not take the
topology of the results into account, and often require a large amount of
annotations for training. We integrate CNNs into the minimal path method, so
that both techniques benefit from each other: CNNs employ learned image
features to improve the determination of minimal paths, while the minimal path
method ensures the correct topology of the segmented centerlines, provides
strong geometric priors to increase the performance of CNNs, and reduces the
amount of annotations for the training of CNNs significantly. Our method has
lower hardware requirements than many recent methods. Qualitative and
quantitative comparison with other methods shows that Path-CNN achieves better
performance, especially when dealing with tubular structures with complex
shapes in challenging environments.


title:
Energy-independent complex single $P$-waves $NN$ potential from
  Marchenko equation

abstract:
  We extend our previous results of solving the inverse problem of quantum
scattering theory (Marchenko theory, fixed-$l$ inversion). In particular, we
apply an isosceles triangular-pulse function set for the Marchenko equation
input kernel expansion in a separable form. The separable form allows a
reduction of the Marchenko equation to a system of linear equations for the
output kernel expansion coefficients. We show that in the general case of a
single partial wave, a linear expression of the input kernel is obtained in
terms of the Fourier series coefficients of $q^{1-m}(1-S(q))$ functions in the
finite range of the momentum $0\leq q\leq\pi/h$ [$S(q)$ is the scattering
matrix, $l$ is the angular orbital momentum, $m=0,1,\dots,2l$]. Thus, we show
that the partial $S$--matrix on the finite interval determines a potential
function with $h$-step accuracy. The calculated partial potentials describe a
partial $S$--matrix with the required accuracy. The partial $S$--matrix is
unitary below the threshold of inelasticity and non--unitary (absorptive) above
the threshold. We developed a procedure and applied it to partial-wave analysis
(PWA) data of $NN$ elastic scattering up to 3 GeV. We show that
energy-independent complex partial potentials describe these data for single
$P$-waves.


title:
Broadband Optical Two-Dimensional Coherent Spectroscopy of a Rubidium
  Atomic Vapor

abstract:
  Optical two-dimensional coherent spectroscopy (2DCS) has become a powerful
tool for studying energy level structure, dynamics, and coupling in many
systems including atomic ensembles. Various types of two-dimensional (2D)
spectra, including the so-called single-quantum, zero-quantum, and
double-quantum 2D spectra, of both D lines (D$_1$ and D$_2$ transitions) of
potassium (K) atoms have been reported previously. For rubidium (Rb), a major
difference is that the D-lines are about 15 nm apart as opposed to only about 3
nm for K. Simultaneously exciting both D-lines of Rb atoms requires a broader
laser bandwidth for the experiment. Here, we report a broadband optical 2DCS
experiment in an Rb atomic vapor. A complete set of single-quantum,
zero-quantum, and double-quantum 2D spectra including both D-lines of Rb atoms
were obtained. The experimental spectra were reproduced by simulated 2D spectra
based on the perturbation solutions to the optical Bloch equations. This work
in Rb atoms complements previous 2DCS studies of K and Rb with a narrower
bandwidth that covers two D-lines of K or only a single D-line of Rb. The
broadband excitation enables the capability to perform double-quantum and
multi-quantum 2DCS of both D-lines of Rb to study many-body interactions and
correlations in comparison with K atoms.


title: Annular webs and Levi subalgebras

abstract:
  For any Levi subalgebra of the form
$\mathfrak{l}=\mathfrak{gl}_{l_{1}}\oplus\dots\oplus\mathfrak{gl}_{l_{d}}\subseteq\mathfrak{gl}_{n}$
we construct a quotient of the category of annular quantum $\mathfrak{gl}_{n}$
webs that is equivalent to the category of finite dimensional representations
of quantum $\mathfrak{l}$ generated by exterior powers of the vector
representation. This can be interpreted as an annular version of skew Howe
duality, gives a description of the representation category of $\mathfrak{l}$
by additive idempotent completion, and a web version of the generalized blob
algebra.


title:
Exploring mathematical objects from custom-tailored mathematical
  universes

abstract:
  Toposes can be pictured as mathematical universes. Besides the standard
topos, in which most of mathematics unfolds, there is a colorful host of
alternate toposes in which mathematics plays out slightly differently. For
instance, there are toposes in which the axiom of choice and the intermediate
value theorem from undergraduate calculus fail. The purpose of this
contribution is to give a glimpse of the toposophic landscape, presenting
several specific toposes and exploring their peculiar properties, and to
explicate how toposes provide distinct lenses through which the usual
mathematical objects of the standard topos can be viewed.


title: Matching Feature Sets for Few-Shot Image Classification

abstract:
  In image classification, it is common practice to train deep networks to
extract a single feature vector per input image. Few-shot classification
methods also mostly follow this trend. In this work, we depart from this
established direction and instead propose to extract sets of feature vectors
for each image. We argue that a set-based representation intrinsically builds a
richer representation of images from the base classes, which can subsequently
better transfer to the few-shot classes. To do so, we propose to adapt existing
feature extractors to instead produce sets of feature vectors from images. Our
approach, dubbed SetFeat, embeds shallow self-attention mechanisms inside
existing encoder architectures. The attention modules are lightweight, and as
such our method results in encoders that have approximately the same number of
parameters as their original versions. During training and inference, a
set-to-set matching metric is used to perform image classification. The
effectiveness of our proposed architecture and metrics is demonstrated via
thorough experiments on standard few-shot datasets -- namely miniImageNet,
tieredImageNet, and CUB -- in both the 1- and 5-shot scenarios. In all cases
but one, our method outperforms the state-of-the-art.


title: Risk-Aware Control and Optimization for High-Renewable Power Grids

abstract:
  The transition of the electrical power grid from fossil fuels to renewable
sources of energy raises fundamental challenges to the market-clearing
algorithms that drive its operations. Indeed, the increased stochasticity in
load and the volatility of renewable energy sources have led to significant
increases in prediction errors, affecting the reliability and efficiency of
existing deterministic optimization models. The RAMC project was initiated to
investigate how to move from this deterministic setting into a risk-aware
framework where uncertainty is quantified explicitly and incorporated in the
market-clearing optimizations. Risk-aware market-clearing raises challenges on
its own, primarily from a computational standpoint. This paper reviews how RAMC
approaches risk-aware market clearing and presents some of its innovations in
uncertainty quantification, optimization, and machine learning. Experimental
results on real networks are presented.


title:
A Sentinel-2 multi-year, multi-country benchmark dataset for crop
  classification and segmentation with deep learning

abstract:
  In this work we introduce Sen4AgriNet, a Sentinel-2 based time series multi
country benchmark dataset, tailored for agricultural monitoring applications
with Machine and Deep Learning. Sen4AgriNet dataset is annotated from farmer
declarations collected via the Land Parcel Identification System (LPIS) for
harmonizing country wide labels. These declarations have only recently been
made available as open data, allowing for the first time the labeling of
satellite imagery from ground truth data. We proceed to propose and standardise
a new crop type taxonomy across Europe that address Common Agriculture Policy
(CAP) needs, based on the Food and Agriculture Organization (FAO) Indicative
Crop Classification scheme. Sen4AgriNet is the only multi-country, multi-year
dataset that includes all spectral information. It is constructed to cover the
period 2016-2020 for Catalonia and France, while it can be extended to include
additional countries. Currently, it contains 42.5 million parcels, which makes
it significantly larger than other available archives. We extract two
sub-datasets to highlight its value for diverse Deep Learning applications; the
Object Aggregated Dataset (OAD) and the Patches Assembled Dataset (PAD). OAD
capitalizes zonal statistics of each parcel, thus creating a powerful
label-to-features instance for classification algorithms. On the other hand,
PAD structure generalizes the classification problem to parcel extraction and
semantic segmentation and labeling. The PAD and OAD are examined under three
different scenarios to showcase and model the effects of spatial and temporal
variability across different years and different countries.


title:
Finding the Nearest Negative Imaginary System with Application to
  Near-Optimal Controller Design

abstract:
  The negative imaginary (NI) systems theory has attracted interests due to the
robustness properties of feedback interconnected NI systems. However, a full
output optimal controller-synthesis methodology, for such class of systems, is
yet to exist. In order to develop a solution towards this problem, we first
develop a methodology to find the nearest NI system to a non NI system. This
later problem stated as follows: for any linear time invariant (LTI) system
defined by the state space matrices $(A, B, C, D)$, find the nearest NI system,
with the state space matrices $(A+\Delta_A,B+\Delta_B,C+\Delta_C,D+\Delta_D)$,
such that the norm of $(\Delta_A,\Delta_B,\Delta_C,\Delta_D)$ is minimized.
Then, this methodology will be used to find the nearest optimal controller for
a given NI plant. In other words, for a given NI system, an optimal control
methodology, such as LQG, is used to design an optimal controller that satisfy
a particular performance measure. Then, the developed methodology of finding
the nearest NI system is used, as a near-optimal control synthesis methodology,
to find the nearest NI system to the designed optimal controller. Hence, the
synthesized controller satisfy the NI property and therefore guarantee a robust
feedback loop with the negative imaginary system under control.


title: Epidemic Population Games With Nonnegligible Disease Death Rate

abstract:
  A recent article that combines normalized epidemic compartmental models and
population games put forth a system theoretic approach to capture the coupling
between a population's strategic behavior and the course of an epidemic. It
introduced a payoff mechanism that governs the population's strategic choices
via incentives, leading to the lowest endemic proportion of infectious
individuals subject to cost constraints. Under the assumption that the disease
death rate is approximately zero, it uses a Lyapunov function to prove
convergence and formulate a quasi-convex program to compute an upper bound for
the peak size of the population's infectious fraction. In this article, we
generalize these results to the case in which the disease death rate is
nonnegligible. This generalization brings on additional coupling terms in the
normalized compartmental model, leading to a more intricate Lyapunov function
and payoff mechanism. Moreover, the associated upper bound can no longer be
determined exactly, but it can be computed with arbitrary accuracy by solving a
set of convex programs.


title: Quantum quasi-Lie systems: properties and applications

abstract:
  A Lie system is a non-autonomous system of ordinary differential equations
describing the integral curves of a $t$-dependent vector field taking values in
a finite-dimensional Lie algebra of vector fields. Lie systems have been
generalised in the literature to deal with $t$-dependent Schr\"odinger
equations determined by a particular class of $t$-dependent Hamiltonian
operators, the quantum Lie systems, and other differential equations through
the so-called quasi-Lie schemes. This work extends quasi-Lie schemes and
quantum Lie systems to cope with $t$-dependent Schr\"odinger equations
associated with the here called quantum quasi-Lie systems. To illustrate our
methods, we propose and study a quantum analogue of the classical nonlinear
oscillator searched by Perelomov and we analyse a quantum one-dimensional fluid
in a trapping potential along with quantum $t$-dependent
Smorodinsky--Winternitz oscillators.


title: FIRST: FrontrunnIng Resilient Smart ConTracts

abstract:
  Owing to the meteoric rise in the usage of cryptocurrencies, there has been a
widespread adaptation of traditional financial applications such as lending,
borrowing, margin trading, and more, to the cryptocurrency realm. In some
cases, the inherently transparent and unregulated nature of cryptocurrencies
leads to attacks on users of these applications. One such attack is
frontrunning, where a malicious entity leverages the knowledge of currently
unprocessed financial transactions submitted by users and attempts to get its
own transaction(s) executed ahead of the unprocessed ones. The consequences of
this can be financial loss, inaccurate transactions, and even exposure to more
attacks. We propose FIRST, a framework that prevents frontrunning attacks, and
is built using cryptographic protocols including verifiable delay functions and
aggregate signatures. In our design, we have a federated setup for generating
the public parameters of the VDF, thus removing the need for a single trusted
setup. We formally analyze FIRST, prove its security using the Universal
Composability framework and experimentally demonstrate the effectiveness of
FIRST.


title: Model-Free and Model-Based Policy Evaluation when Causality is Uncertain

abstract:
  When decision-makers can directly intervene, policy evaluation algorithms
give valid causal estimates. In off-policy evaluation (OPE), there may exist
unobserved variables that both impact the dynamics and are used by the unknown
behavior policy. These "confounders" will introduce spurious correlations and
naive estimates for a new policy will be biased. We develop worst-case bounds
to assess sensitivity to these unobserved confounders in finite horizons when
confounders are drawn iid each period. We demonstrate that a model-based
approach with robust MDPs gives sharper lower bounds by exploiting domain
knowledge about the dynamics. Finally, we show that when unobserved confounders
are persistent over time, OPE is far more difficult and existing techniques
produce extremely conservative bounds.


title:
Waveform Design for Wireless Power Transfer with Power Amplifier and
  Energy Harvester Non-Linearities

abstract:
  Waveform optimization has shown its great potential to boost the performance
of far-field wireless power transfer (WPT). Current research has optimized
transmit waveform, adaptive to channel state information (CSI), to maximize the
harvested power in WPT while accounting for energy harvester (EH)'s
non-linearity. However, the existing transmit waveform design disregards the
non-linear high power amplifiers (HPA) at the transmitter. Driven by this, this
paper optimizes the multi-carrier waveform at the input of HPA to maximize the
harvested DC power considering both HPA's and EH's non-linearities. Two
optimization models are formulated based on whether the frequencies of the
multi-carrier waveform are concentrated within the transmit pass band or not.
Analysis and simulations show that, while EH's non-linearity boosts the power
harvesting performance, HPA's non-linearity degrades the harvested power.
Hence, the optimal waveform shifts from multi-carrier that exploits EH's
non-linearity to single-carrier that reduces HPA's detrimental non-linear
distortion as the operational regime of WPT becomes more sensitive to HPA's
non-linearity and less sensitive to EH's non-linearity (and inversely).
Simultaneously, operating towards HPA's non-linear regime by increasing the
input signal power benefits the harvested power since HPA's DC power supply is
better exploited, whereas the end-to-end power transfer efficiency (PTE) might
decrease because of the increasing non-linear degradation. Throughout the
simulations, the proposed waveforms show significant gain over those not
accounting for HPA's non-linearity, especially in frequency-flat channels. We
also compare the two proposed waveforms and show that the severity of HPA's
non-linearity dictates which of the two proposed waveforms is more beneficial.


title:
Long-tailed Extreme Multi-label Text Classification with Generated
  Pseudo Label Descriptions

abstract:
  Extreme Multi-label Text Classification (XMTC) has been a tough challenge in
machine learning research and applications due to the sheer sizes of the label
spaces and the severe data scarce problem associated with the long tail of rare
labels in highly skewed distributions. This paper addresses the challenge of
tail label prediction by proposing a novel approach, which combines the
effectiveness of a trained bag-of-words (BoW) classifier in generating
informative label descriptions under severe data scarce conditions, and the
power of neural embedding based retrieval models in mapping input documents (as
queries) to relevant label descriptions. The proposed approach achieves
state-of-the-art performance on XMTC benchmark datasets and significantly
outperforms the best methods so far in the tail label prediction. We also
provide a theoretical analysis for relating the BoW and neural models w.r.t.
performance lower bound.


title: Combinatorics of Exceptional Sequences of Type $\tilde{\mathbb{A}}_n$

abstract:
  It is known that there are infinitely many exceptional sequences of quiver
representations for Euclidean quivers. In this paper we study those of type
$\tilde{\mathbb{A}}_n$ and classify them into finitely many parametrized
families using combinatorial objects called strand, chord and arc diagrams. We
first give a bijection between exceptional collections and these diagrams, then
define parametrized families of exceptional collections, and finally show that
these families are in bijection with small strand diagrams, which may be
counted by the generalized Catalan numbers.


title: A decade of molecular cell atlases

abstract:
  The recent opinion article "A decade of molecular cell atlases" by Stephen
Quake narrates the incredible single-cell genomics technology advances that
have taken place over the last decade, and how they have translated to
increasingly resolved cell atlases. However the sequence of events described is
inaccurate and contains several omissions and errors. The errors are corrected
in this note.


title:
Dynamic physical activity recommendation on personalised mobile health
  information service: A deep reinforcement learning approach

abstract:
  Mobile health (mHealth) information service makes healthcare management
easier for users, who want to increase physical activity and improve health.
However, the differences in activity preference among the individual, adherence
problems, and uncertainty of future health outcomes may reduce the effect of
the mHealth information service. The current health service system usually
provides recommendations based on fixed exercise plans that do not satisfy the
user specific needs. This paper seeks an efficient way to make physical
activity recommendation decisions on physical activity promotion in
personalised mHealth information service by establishing data-driven model. In
this study, we propose a real-time interaction model to select the optimal
exercise plan for the individual considering the time-varying characteristics
in maximising the long-term health utility of the user. We construct a
framework for mHealth information service system comprising a personalised AI
module, which is based on the scientific knowledge about physical activity to
evaluate the individual exercise performance, which may increase the awareness
of the mHealth artificial intelligence system. The proposed deep reinforcement
learning (DRL) methodology combining two classes of approaches to improve the
learning capability for the mHealth information service system. A deep learning
method is introduced to construct the hybrid neural network combing long-short
term memory (LSTM) network and deep neural network (DNN) techniques to infer
the individual exercise behavior from the time series data. A reinforcement
learning method is applied based on the asynchronous advantage actor-critic
algorithm to find the optimal policy through exploration and exploitation.


title: Telescoping continued fractions for the error term in Stirling's formula

abstract:
  In this paper, we introduce telescoping continued fractions to find lower
bounds for the error term $r_n$ in Stirling's approximation $\displaystyle n! =
\sqrt{2\pi}n^{n+1/2}e^{-n}e^{r_n}.$ This improves lower bounds given earlier by
Ces\`{a}ro (1922), Robbins (1955), Nanjundiah (1959), Maria (1965) and Popov
(2017). The expression is in terms of a continued fraction, together with an
algorithm to find successive terms of this continued fraction. The technique we
introduce allows us to experimentally obtain upper and lower bounds for a
sequence of convergents of a continued fraction in terms of a difference of two
continued fractions.


title: A Study of Single Statement Bugs Involving Dynamic Language Features

abstract:
  Dynamic language features are widely available in programming languages to
implement functionality that can adapt to multiple usage contexts, enabling
reuse. Functionality such as data binding , object-relational mapping and user
interface builders can be heavily dependent on these features. However, their
use has risks and downsides as they affect the soundness of static analyses and
techniques that rely on such analyses (such as bug detection and automated
program repair). They can also make software more error-prone due to potential
difficulties in understanding reflective code, loss of compile-time safety and
incorrect API usage. In this paper, we set out to quantify some of the effects
of using dynamic language features in Java programs-that is, the
error-proneness of using those features with respect to a particular type of
bug known as single statement bugs. By mining 2,024 GitHub projects, we found
139 single statement bug instances (falling under 10 different bug patterns),
with the highest number of bugs belonging to three specific patterns: Wrong
Function Name, Same Function More Args and Change Identifier Used. These
results can help practitioners to quantify the risk of using dynamic techniques
over alternatives (such as code generation). We hope this classification raises
attention on choosing dynamic APIs that are likely to be error-prone, and
provides developers a better understanding when designing bug detection tools
for such feature.


title: AdaFace: Quality Adaptive Margin for Face Recognition

abstract:
  Recognition in low quality face datasets is challenging because facial
attributes are obscured and degraded. Advances in margin-based loss functions
have resulted in enhanced discriminability of faces in the embedding space.
Further, previous studies have studied the effect of adaptive losses to assign
more importance to misclassified (hard) examples. In this work, we introduce
another aspect of adaptiveness in the loss function, namely the image quality.
We argue that the strategy to emphasize misclassified samples should be
adjusted according to their image quality. Specifically, the relative
importance of easy or hard samples should be based on the sample's image
quality. We propose a new loss function that emphasizes samples of different
difficulties based on their image quality. Our method achieves this in the form
of an adaptive margin function by approximating the image quality with feature
norms. Extensive experiments show that our method, AdaFace, improves the face
recognition performance over the state-of-the-art (SoTA) on four datasets
(IJB-B, IJB-C, IJB-S and TinyFace). Code and models are released in
https://github.com/mk-minchul/AdaFace.


title: The Calder\'on problem for the fractional Dirac operator

abstract:
  We show that knowledge of the source-to-solution map for the fractional Dirac
operator acting over sections of a Hermitian vector bundle over a smooth closed
connencted Riemannian manifold of dimension $m\geq 2$ determines uniquely the
smooth structure, Riemannian metric, Hermitian bundle and connection, and its
Clifford modulo up to a isometry. We also mention several potential
applications in physics and other fields.


title:
High-resolution diffraction reveals magnetoelastic coupling and coherent
  phase separation in tetragonal CuMnAs

abstract:
  Tetragonal CuMnAs was the first antiferromagnet where reorientation of the
N\'eel vector was reported to occur by an inverse spin galvanic effect. A
complicating factor in the formation of phase-pure tetragonal CuMnAs is the
formation of an orthorhombic phase with nearly the same stoichiometry.
Pure-phase tetragonal CuMnAs has been reported to require an excess of Cu to
maintain a single phase in traditional solid state synthesis reactions. Here we
show that subtle differences in diffraction patterns signal pervasive
inhomogeneity and phase separation, even in Cu-rich Cu$_{1.18}$Mn$_{0.82}$As.
From calorimetry and magnetometry measurements, we identify two transitions
corresponding to the N\'eel temperature (T$_N$) and an antiferromagnet to weak
ferromagnet transition in Cu$_{1.18}$Mn$_{0.82}$As and
CuMn$_{0.964}$As$_{1.036}$. These transitions have clear crystallographic
signatures, directly observable in the lattice parameters upon in-situ heating
and cooling. The immiscibility and phase separation could arise from a
spinoidal decomposition that occurs at high temperatures, and the presence of a
ferromagnetic transition near room temperature warrants further investigation
of its effect on the electrical switching behavior.


title: Automatic Dialect Density Estimation for African American English

abstract:
  In this paper, we explore automatic prediction of dialect density of the
African American English (AAE) dialect, where dialect density is defined as the
percentage of words in an utterance that contain characteristics of the
non-standard dialect. We investigate several acoustic and language modeling
features, including the commonly used X-vector representation and ComParE
feature set, in addition to information extracted from ASR transcripts of the
audio files and prosodic information. To address issues of limited labeled
data, we use a weakly supervised model to project prosodic and X-vector
features into low-dimensional task-relevant representations. An XGBoost model
is then used to predict the speaker's dialect density from these features and
show which are most significant during inference. We evaluate the utility of
these features both alone and in combination for the given task. This work,
which does not rely on hand-labeled transcripts, is performed on audio segments
from the CORAAL database. We show a significant correlation between our
predicted and ground truth dialect density measures for AAE speech in this
database and propose this work as a tool for explaining and mitigating bias in
speech technology.


title: The orientations of the binary black holes in GWTC-3

abstract:
  It is expected that the orbital planes of gravitational-wave (GW) sources are
isotropically distributed. However, both physical and technical factors, such
as alternate theories of gravity with birefringence, catalog contamination, and
search algorithm limitations, could result in inferring a non-isotropic
distribution. Showing that the inferred astrophysical distribution of the
orbital orientations is indeed isotropic can thus be used to rule out some
violations of general relativity, as a null test about the purity of the GW
catalog sample, and as a check that selection effects are being properly
accounted for. We augment the default mass/spins/redshift model used by the
LIGO-Virgo-KAGRA Collaboration in their most recent analysis to also measure
the astrophysical distribution of orbital orientations. We show that the 69
binary black holes in GWTC-3 are consistent with having random orbital
orientations. The inferred distribution is highly symmetric around $\pi/2$,
with skewness $\mathcal{S}_{\rm{post}}=0.01^{+0.17}_{-0.17}$. Meanwhile, the
median of the inferred distribution has a Jensen-Shannon divergence of
$1.4\times 10^{-4}$ bits when compared to the expected isotropic distribution.