title:
The fundamental Lepage form in two independent variables: a
  generalization using order-reducibility

abstract:
  A second-order generalization of the fundamental Lepage form of geometric
calculus of variations over fibered manifolds with 2-dimensional base is
described by means of insisting on (i) equivalence relation "Lepage
differential 2-form is closed if and only if the associated Lagrangian is
trivial" and, (ii) the principal component of Lepage form, extending the
well-known Poincar\'{e}-Cartan form, preserves order prescribed by a given
Lagrangian. This approach completes several attempts of finding a Lepage
equivalent of a second-order Lagrangian possessing condition (i), which is
well-known for first-order Lagrangians in field theory due to Krupka and
Betounes.


title:
Entanglement dynamics of a hard-core quantum gas during a Joule
  expansion

abstract:
  We study the entanglement dynamics of a one-dimensional hard-core quantum gas
initially confined in a box of size $L$ with saturated density $\rho=1$. The
gas is suddenly released into a region of size $2L$ by moving one of the box
edges. We show that the analytic prediction for the entanglement entropy
obtained from quantum fluctuating hydrodynamics holds quantitatively true even
after several reflections of the gas against the box edges. We further
investigate the long time limit $t/L\gg 1$ where a Floquet picture of the
non-equilibrium dynamics emerges and hydrodynamics eventually breaks down.


title: Linear Hashing with $\ell_\infty$ guarantees and two-sided Kakeya bounds

abstract:
  We show that a randomly chosen linear map over a finite field gives a good
hash function in the $\ell_\infty$ sense. More concretely, consider a set $S
\subset \mathbb{F}_q^n$ and a randomly chosen linear map $L : \mathbb{F}_q^n
\to \mathbb{F}_q^t$ with $q^t$ taken to be sufficiently smaller than $|S|$. Let
$U_S$ denote a random variable distributed uniformly on $S$. Our main theorem
shows that, with high probability over the choice of $L$, the random variable
$L(U_S)$ is close to uniform in the $\ell_\infty$ norm. In other words, every
element in the range $\mathbb{F}_q^t$ has about the same number of elements in
$S$ mapped to it. This complements the widely-used Leftover Hash Lemma (LHL)
which proves the analog statement under the statistical, or $\ell_1$, distance
(for a richer class of functions) as well as prior work on the expected largest
'bucket size' in linear hash functions [ADMPT99]. Our proof leverages a
connection between linear hashing and the finite field Kakeya problem and
extends some of the tools developed in this area, in particular the polynomial
method.


title:
A Novel Capsule Neural Network Based Model for Drowsiness Detection
  Using Electroencephalography Signals

abstract:
  The early detection of drowsiness has become vital to ensure the correct and
safe development of several industries' tasks. Due to the transient mental
state of a human subject between alertness and drowsiness, automated drowsiness
detection is a complex problem to tackle. The electroencephalography signals
allow us to record variations in an individual's brain's electrical potential,
where each of them gives specific information about a subject's mental state.
However, due to this type of signal's nature, its acquisition, in general, is
complex, so it is hard to have a large volume of data to apply techniques of
Deep Learning for processing and classification optimally. Nevertheless,
Capsule Neural Networks are a brand-new Deep Learning algorithm proposed for
work with reduced amounts of data. It is a robust algorithm to handle the
data's hierarchical relationships, which is an essential characteristic for
work with biomedical signals. Therefore, this paper presents a Deep
Learning-based method for drowsiness detection with CapsNet by using a
concatenation of spectrogram images of the electroencephalography signals
channels. The proposed CapsNet model is compared with a Convolutional Neural
Network, which is outperformed by the proposed model, which obtains an average
accuracy of 86,44% and 87,57% of sensitivity against an average accuracy of
75,86% and 79,47% sensitivity for the CNN, showing that CapsNet is more
suitable for this kind of datasets and tasks.


title: Adaptive Merging on Phase Change Memory

abstract:
  Indexing is a well-known database technique used to facilitate data access
and speed up query processing. Nevertheless, the construction and modification
of indexes are very expensive. In traditional approaches, all records in the
database table are equally covered by the index. It is not effective, since
some records may be queried very often and some never. To avoid this problem,
adaptive merging has been introduced. The key idea is to create index
adaptively and incrementally as a side-product of query processing. As a
result, the database table is indexed partially depending on the query
workload. This paper faces a problem of adaptive merging for phase change
memory (PCM). The most important features of this memory type are: limited
write endurance and high write latency. As a consequence, adaptive merging
should be investigated from the scratch. We solve this problem in two steps.
First, we apply several PCM optimization techniques to the traditional adaptive
merging approach. We prove that the proposed method (eAM) outperforms a
traditional approach by 60%. After that, we invent the framework for adaptive
merging (PAM) and a new PCM-optimized index. It further improves the system
performance by 20% for databases where search queries interleave with data
modifications.


title: Scalable Spike-and-Slab

abstract:
  Spike-and-slab priors are commonly used for Bayesian variable selection, due
to their interpretability and favorable statistical properties. However,
existing samplers for spike-and-slab posteriors incur prohibitive computational
costs when the number of variables is large. In this article, we propose
Scalable Spike-and-Slab ($S^3$), a scalable Gibbs sampling implementation for
high-dimensional Bayesian regression with the continuous spike-and-slab prior
of George and McCulloch (1993). For a dataset with $n$ observations and $p$
covariates, $S^3$ has order $\max\{ n^2 p_t, np \}$ computational cost at
iteration $t$ where $p_t$ never exceeds the number of covariates switching
spike-and-slab states between iterations $t$ and $t-1$ of the Markov chain.
This improves upon the order $n^2 p$ per-iteration cost of state-of-the-art
implementations as, typically, $p_t$ is substantially smaller than $p$. We
apply $S^3$ on synthetic and real-world datasets, demonstrating orders of
magnitude speed-ups over existing exact samplers and significant gains in
inferential quality over approximate samplers with comparable cost.


title: Enumerative geometry of the mirror quintic

abstract:
  We evaluate the enumerative invariants of low degree on the mirror quintic
threefold.


title:
Cross-lingual Self-Supervised Speech Representations for Improved
  Dysarthric Speech Recognition

abstract:
  State-of-the-art automatic speech recognition (ASR) systems perform well on
healthy speech. However, the performance on impaired speech still remains an
issue. The current study explores the usefulness of using Wav2Vec
self-supervised speech representations as features for training an ASR system
for dysarthric speech. Dysarthric speech recognition is particularly difficult
as several aspects of speech such as articulation, prosody and phonation can be
impaired. Specifically, we train an acoustic model with features extracted from
Wav2Vec, Hubert, and the cross-lingual XLSR model. Results suggest that speech
representations pretrained on large unlabelled data can improve word error rate
(WER) performance. In particular, features from the multilingual model led to
lower WERs than filterbanks (Fbank) or models trained on a single language.
Improvements were observed in English speakers with cerebral palsy caused
dysarthria (UASpeech corpus), Spanish speakers with Parkinsonian dysarthria
(PC-GITA corpus) and Italian speakers with paralysis-based dysarthria (EasyCall
corpus). Compared to using Fbank features, XLSR-based features reduced WERs by
6.8%, 22.0%, and 7.0% for the UASpeech, PC-GITA, and EasyCall corpus,
respectively.


title: Exemplar-based Pattern Synthesis with Implicit Periodic Field Network

abstract:
  Synthesis of ergodic, stationary visual patterns is widely applicable in
texturing, shape modeling, and digital content creation. The wide applicability
of this technique thus requires the pattern synthesis approaches to be
scalable, diverse, and authentic. In this paper, we propose an exemplar-based
visual pattern synthesis framework that aims to model the inner statistics of
visual patterns and generate new, versatile patterns that meet the
aforementioned requirements. To this end, we propose an implicit network based
on generative adversarial network (GAN) and periodic encoding, thus calling our
network the Implicit Periodic Field Network (IPFN). The design of IPFN ensures
scalability: the implicit formulation directly maps the input coordinates to
features, which enables synthesis of arbitrary size and is computationally
efficient for 3D shape synthesis. Learning with a periodic encoding scheme
encourages diversity: the network is constrained to model the inner statistics
of the exemplar based on spatial latent codes in a periodic field. Coupled with
continuously designed GAN training procedures, IPFN is shown to synthesize
tileable patterns with smooth transitions and local variations. Last but not
least, thanks to both the adversarial training technique and the encoded
Fourier features, IPFN learns high-frequency functions that produce authentic,
high-quality results. To validate our approach, we present novel experimental
results on various applications in 2D texture synthesis and 3D shape synthesis.


title: Residual-guided Personalized Speech Synthesis based on Face Image

abstract:
  Previous works derive personalized speech features by training the model on a
large dataset composed of his/her audio sounds. It was reported that face
information has a strong link with the speech sound. Thus in this work, we
innovatively extract personalized speech features from human faces to
synthesize personalized speech using neural vocoder. A Face-based Residual
Personalized Speech Synthesis Model (FR-PSS) containing a speech encoder, a
speech synthesizer and a face encoder is designed for PSS. In this model, by
designing two speech priors, a residual-guided strategy is introduced to guide
the face feature to approach the true speech feature in the training. Moreover,
considering the error of feature's absolute values and their directional bias,
we formulate a novel tri-item loss function for face encoder. Experimental
results show that the speech synthesized by our model is comparable to the
personalized speech synthesized by training a large amount of audio data in
previous works.


title:
T*$\varepsilon$ -- Bounded-Suboptimal Efficient Motion Planning for
  Minimum-Time Planar Curvature-Constrained Systems

abstract:
  We consider the problem of finding collision-free paths for
curvature-constrained systems in the presence of obstacles while minimizing
execution time. Specifically, we focus on the setting where a planar system can
travel at some range of speeds with unbounded acceleration. This setting can
model many systems, such as fixed-wing drones. Unfortunately, planning for such
systems might require evaluating many (local) time-optimal transitions
connecting two close-by configurations, which is computationally expensive.
Existing methods either pre-compute all such transitions in a preprocessing
stage or use heuristics to speed up the search, thus foregoing any guarantees
on solution quality. Our key insight is that computing all the time-optimal
transitions is both~(i)~computationally expensive and~(ii)~unnecessary for many
problem instances. We show that by finding bounded-suboptimal solutions
(solutions whose cost is bounded by $1+\varepsilon$ times the cost of the
optimal solution for any user-provided $\varepsilon$) and not time-optimal
solutions, one can dramatically reduce the number of time-optimal transitions
used. We demonstrate using empirical evaluation that our planning framework can
reduce the runtime by several orders of magnitude compared to the
state-of-the-art while still providing guarantees on the quality of the
solution.


title:
Fractal geometry of the space-time difference profile in the directed
  landscape via construction of geodesic local times

abstract:
  The Directed Landscape, a random directed metric on the plane (where the
first and the second coordinates are termed spatial and temporal respectively),
was constructed in the breakthrough work of Dauvergne, Ortmann, and Vir\'ag,
and has since been shown to be the scaling limit of various integrable models
of Last Passage percolation, a central member of the Kardar-Parisi-Zhang
universality class. It exhibits several scale invariance properties making it a
natural source of rich fractal behavior. Such a study was initiated in
Basu-Ganguly-Hammond, where the difference profile i.e., the difference of
passage times from two fixed points (say $(\pm 1,0)$), was considered. Owing to
geodesic geometry, it turns out that this difference process is almost surely
locally constant. The set of non-constancy is connected to disjointness of
geodesics and inherits remarkable fractal properties. In particular, it has
been established that when only the spatial coordinate is varied, the set of
non-constancy of the difference profile has Hausdorff dimension $1/2$, and
bears a rather strong resemblance to the zero set of Brownian motion. The
arguments crucially rely on a monotonicity property, which is absent when the
temporal structure of the process is probed, necessitating the development of
new methods.
  In this paper, we put forth several new ideas, and show that the set of
non-constancy of the 2D difference profile and the 1D temporal process (when
the spatial coordinate is fixed and the temporal coordinate is varied) have
Hausdorff dimensions $5/3$ and $2/3$ respectively. A particularly crucial
ingredient in our analysis is the novel construction of a local time process
for the geodesic akin to Brownian local time, supported on the "zero set" of
the geodesic. Further, we show that the latter has Hausdorff dimension $1/3$ in
contrast to the zero set of Brownian motion which has dimension $1/2.$


title:
Mesoscopic description of hippocampal replay and metastability in
  spiking neural networks with short-term plasticity

abstract:
  Bottom-up models of functionally relevant patterns of neural activity provide
an explicit link between neuronal dynamics and computation. A prime example of
functional activity pattern is hippocampal replay, which is critical for memory
consolidation. The switchings between replay events and a low-activity state in
neural recordings suggests metastable neural circuit dynamics. As metastability
has been attributed to noise and/or slow fatigue mechanisms, we propose a
concise mesoscopic model which accounts for both. Crucially, our model is
bottom-up: it is analytically derived from the dynamics of finite-size networks
of Linear-Nonlinear Poisson neurons with short-term synaptic depression. As
such, noise is explicitly linked to spike noise and network size, and fatigue
is explicitly linked to synaptic dynamics. To derive the mesosocpic model, we
first consider a homogeneous spiking neural network and follow the temporal
coarse-graining approach of Gillespie ("chemical Langevin equation"), which can
be naturally interpreted as a stochastic neural mass model. The Langevin
equation is computationally inexpensive to simulate and enables a thorough
study of metastable dynamics in classical setups (population spikes and Up-Down
states dynamics) by means of phase-plane analysis. This stochastic neural mass
model is the basic component of our mesoscopic model for replay. We show that
our model faithfully captures the stochastic nature of individual replayed
trajectories. Moreover, compared to the deterministic Romani-Tsodyks model of
place cell dynamics, it exhibits a higher level of variability in terms of
content, direction and timing of replay events, compatible with biological
evidence and could be functionally desirable. This variability is the product
of a new dynamical regime where metastability emerges from a complex interplay
between finite-size fluctuations and local fatigue.


title: Hybrid codes (massless electron fluid)

abstract:
  Hybrid codes are widely used to model ion-scale phenomena in space plasmas.
Hybrid codes differ from full particle (PIC) codes in that the electrons are
modeled as a fluid that is usually assumed to be massless, while the electric
field is not advanced in time, but instead calculated at the new time level
from the advanced ion quantities and the magnetic field. In this chapter we
concentrate on such hybrid models with massless electrons, beginning with a
discussion of the basics of a simple hybrid code algorithm. We then show
examples of recent use of hybrid codes for large-scale space plasma simulations
of structures formed at planetary bow shock--foreshock systems, magnetic
reconnection at the magnetopause, and complex phenomena in the magnetosheath
due to the interaction of kinetic processes associated with the bow shock,
magnetic reconnection, and turbulence. A discussion then follows of a number of
other hybrid codes based on different algorithms that are presently in active
use to investigate a variety of plasma processes in space as well as some
recent work on the development of new models. We conclude with a few brief
comments concerning the future development and use of hybrid codes.


title:
Self-Supervised Speech Representations Preserve Speech Characteristics
  while Anonymizing Voices

abstract:
  Collecting speech data is an important step in training speech recognition
systems and other speech-based machine learning models. However, the issue of
privacy protection is an increasing concern that must be addressed. The current
study investigates the use of voice conversion as a method for anonymizing
voices. In particular, we train several voice conversion models using
self-supervised speech representations including Wav2Vec2.0, Hubert and
UniSpeech. Converted voices retain a low word error rate within 1% of the
original voice. Equal error rate increases from 1.52% to 46.24% on the
LibriSpeech test set and from 3.75% to 45.84% on speakers from the VCTK corpus
which signifies degraded performance on speaker verification. Lastly, we
conduct experiments on dysarthric speech data to show that speech features
relevant to articulation, prosody, phonation and phonology can be extracted
from anonymized voices for discriminating between healthy and pathological
speech.


title: MultiMAE: Multi-modal Multi-task Masked Autoencoders

abstract:
  We propose a pre-training strategy called Multi-modal Multi-task Masked
Autoencoders (MultiMAE). It differs from standard Masked Autoencoding in two
key aspects: I) it can optionally accept additional modalities of information
in the input besides the RGB image (hence "multi-modal"), and II) its training
objective accordingly includes predicting multiple outputs besides the RGB
image (hence "multi-task").
  We make use of masking (across image patches and input modalities) to make
training MultiMAE tractable as well as to ensure cross-modality predictive
coding is indeed learned by the network. We show this pre-training strategy
leads to a flexible, simple, and efficient framework with improved transfer
results to downstream tasks. In particular, the same exact pre-trained network
can be flexibly used when additional information besides RGB images is
available or when no information other than RGB is available - in all
configurations yielding competitive to or significantly better results than the
baselines. To avoid needing training datasets with multiple modalities and
tasks, we train MultiMAE entirely using pseudo labeling, which makes the
framework widely applicable to any RGB dataset.
  The experiments are performed on multiple transfer tasks (image
classification, semantic segmentation, depth estimation) and datasets
(ImageNet, ADE20K, Taskonomy, Hypersim, NYUv2). The results show an
intriguingly impressive capability by the model in cross-modal/task predictive
coding and transfer.


title:
Predictable Sharing of Last-level Cache Partitions for Multi-core
  Safety-critical Systems

abstract:
  Last-level cache (LLC) partitioning is a technique to provide temporal
isolation and low worst-case latency (WCL) bounds when cores access the shared
LLC in multicore safety-critical systems. A typical approach to cache
partitioning involves allocating a separate partition to a distinct core. A
central criticism of this approach is its poor utilization of cache storage.
Today's trend of integrating a larger number of cores exacerbates this issue
such that we are forced to consider shared LLC partitions for effective
deployments. This work presents an approach to share LLC partitions among
multiple cores while being able to provide low WCL bounds.


title: TALLFormer: Temporal Action Localization with Long-memory Transformer

abstract:
  Most modern approaches in temporal action localization divide this problem
into two parts: (i) short-term feature extraction and (ii) long-range temporal
boundary localization. Due to the high GPU memory cost caused by processing
long untrimmed videos, many methods sacrifice the representational power of the
short-term feature extractor by either freezing the backbone or using a very
small spatial video resolution. This issue becomes even worse with the recent
video transformer models, many of which have quadratic memory complexity. To
address these issues, we propose TALLFormer, a memory-efficient and end-to-end
trainable Temporal Action Localization transformer with Long-term memory. Our
long-term memory mechanism eliminates the need for processing hundreds of
redundant video frames during each training iteration, thus, significantly
reducing the GPU memory consumption and training time. These efficiency savings
allow us (i) to use a powerful video transformer-based feature extractor
without freezing the backbone or reducing the spatial video resolution, while
(ii) also maintaining long-range temporal boundary localization capability.
With only RGB frames as input and no external action recognition classifier,
TALLFormer outperforms previous state-of-the-art methods by a large margin,
achieving an average mAP of 59.1% on THUMOS14 and 35.6% on ActivityNet-1.3. The
code will be available in https://github.com/klauscc/TALLFormer.


title:
End-to-end multi-particle reconstruction in high occupancy imaging
  calorimeters with graph neural networks

abstract:
  We present an end-to-end reconstruction algorithm to build particle
candidates from detector hits in next-generation granular calorimeters similar
to that foreseen for the high-luminosity upgrade of the CMS detector. The
algorithm exploits a distance-weighted graph neural network, trained with
object condensation, a graph segmentation technique. Through a single-shot
approach, the reconstruction task is paired with energy regression. We describe
the reconstruction performance in terms of efficiency as well as in terms of
energy resolution. In addition, we show the jet reconstruction performance of
our method and discuss its inference computational cost. To our knowledge, this
work is the first-ever example of single-shot calorimetric reconstruction of
${\cal O}(1000)$ particles in high-luminosity conditions with 200 pileup.


title:
Deep Feature Screening: Feature Selection for Ultra High-Dimensional
  Data via Deep Neural Networks

abstract:
  The applications of traditional statistical feature selection methods to
high-dimension, low sample-size data often struggle and encounter challenging
problems, such as overfitting, curse of dimensionality, computational
infeasibility, and strong model assumption. In this paper, we propose a novel
two-step nonparametric approach called Deep Feature Screening (DeepFS) that can
overcome these problems and identify significant features with high precision
for ultra high-dimensional, low-sample-size data. This approach first extracts
a low-dimensional representation of input data and then applies feature
screening based on multivariate rank distance correlation recently developed by
Deb and Sen (2021). This approach combines the strengths of both deep neural
networks and feature screening, and thereby has the following appealing
features in addition to its ability of handling ultra high-dimensional data
with small number of samples: (1) it is model free and distribution free; (2)
it can be used for both supervised and unsupervised feature selection; and (3)
it is capable of recovering the original input data. The superiority of DeepFS
is demonstrated via extensive simulation studies and real data analyses.


title: Kernel-weighted specification testing under general distributions

abstract:
  Kernel-weighted test statistics have been widely used in a variety of
settings including non-stationary regression, inference on propensity score and
panel data models. We develop the limit theory for a kernel-based specification
test of a parametric conditional mean when the law of the regressors may not be
absolutely continuous to the Lebesgue measure and is contaminated with singular
components. This result is of independent interest and may be useful in other
applications that utilize kernel smoothed U-statistics. Simulations illustrate
the non-trivial impact of the distribution of the conditioning variables on the
power properties of the test statistic.


title:
Stable spontaneously-scalarized black holes in generalized scalar-tensor
  theories

abstract:
  It has been shown that the synergy of a scalar field coupling with both the
Ricci scalar and the Gauss-Bonnet invariant significantly affects the
properties of scalarized black holes and neutron stars, including their domain
of existence and the amount of scalar hair they carry. Here we study the radial
stability of scalarized black-hole solutions. We demonstrate that they are
stable against radial perturbations for Ricci couplings consistent with both a
late-time cosmological attractor and the evasion of binary pulsar constraints.
In addition, we investigate the effect of the Ricci coupling on the
hyperbolicity of the equation governing linear, radial perturbations and show
that it significantly reduces the region over which hyperbolicity is lost.


title: Bi-PPT channels are entanglement breaking

abstract:
  In a recent paper, Hirche and Leditzky introduced the notion of bi-PPT
channels which are quantum channels that stay completely positive under
composition with a transposition and such that the same property holds for one
of their complementary channels. They asked whether there are examples of such
channels that are not antidegradable. We show that this is not the case, since
bi-PPT channels are always entanglement breaking. We also show that degradable
quantum channels staying completely positive under composition with a
transposition are entanglement breaking.


title:
Bayesian Semiparametric Covariate Informed Multivariate Density
  Deconvolution

abstract:
  Estimating the marginal and joint densities of the long-term average intakes
of different dietary components is an important problem in nutritional
epidemiology. Since these variables cannot be directly measured, data are
usually collected in the form of 24-hour recalls of the intakes. The problem of
estimating the density of the latent long-term average intakes from their
observed but error contaminated recalls then becomes a problem of multivariate
deconvolution of densities. The underlying densities could potentially vary
with the subjects' demographic characteristics such as sex, ethnicity, age,
etc. The problem of density deconvolution in the presence of associated
precisely measured covariates has, however, never been considered before, not
even in the univariate setting. We present a flexible Bayesian semiparametric
approach to covariate informed multivariate deconvolution. Building on recent
advances in copula deconvolution and conditional tensor factorization
techniques, our proposed method not only allows the joint and the marginal
densities to vary flexibly with the associated predictors but also allows
automatic selection of the most influential predictors. Importantly, the method
also allows the density of interest and the density of the measurement errors
to vary with potentially different sets of predictors. We design Markov chain
Monte Carlo algorithms that enable efficient posterior inference, appropriately
accommodating uncertainty in all aspects of our analysis. The empirical
efficacy of the proposed method is illustrated through simulation experiments.
Its practical utility is demonstrated in the afore-described nutritional
epidemiology applications in estimating covariate-adjusted long term intakes of
different dietary components. Supplementary materials include substantive
additional details and R codes are also available online.


title:
A concentration inequality for the maximum vertex degree in random
  dissections

abstract:
  We obtain a concentration inequality for the maximum degree of a vertex in a
uniformly random dissection of a polygon. This resolves a conjecture posed by
Curien and Kortchemski in 2012. Our approach is based on a bijection with dual
trees and the tools of analytic combinatorics.


title:
Electronic structure, magnetic properties and pairing tendencies of the
  copper-based honeycomb lattice Na$_2$Cu$_2$TeO$_6$

abstract:
  Spin-$1/2$ chains with alternating antiferromagnetic and ferromagnetic
couplings have attracted considerable interest due to the topological character
of their spin excitations. Here, using density functional theory and density
matrix renormalization group methods, we have systematically studied the
dimerized chain system Na$_2$Cu$_2$TeO$_6$. Near the Fermi level, the dominant
states are mainly contributed by the Cu $3d_{x^2-y^2}$ orbitals highly
hybridized with the O $2p$ orbitals in the nonmagnetic phase, leading to an
"effective" single-orbital low-energy model. Furthermore, the bandwidth of the
Cu $3d_{x^2-y^2}$ states is small ($\sim 0.8$ eV), suggesting that electronic
correlations will strongly affect this system. By introducing such electronic
correlations, we found this system is a Mott insulator. Moreover, by
calculating the magnetic exchange interactions ($J_1$, $J_2$ and $J_3$), we
explained the size and sign of the exchange interactions in
Na$_2$Cu$_2$TeO$_6$, in agreement with neutron experiments. In addition, we
constructed a single-orbital Hubbard model for this dimerized chain system,
where the quantum fluctuations are taken into account. Both AFM and FM coupling
($\uparrow$-$\downarrow$-$\downarrow$-$\uparrow$) along the chain were found in
our DMRG and Lanczos calculations, in agreement with DFT and neutron results.
We also calculated the hole pairing binding energy $\Delta E$ which becomes
negative at Hubbard $U \sim 11$ eV, indicating incipient pairing tendencies.
Finally, we also looked at various cases of hole doping that always exhibit
tight pairs. Thus, we believe our results for Na$_2$Cu$_2$TeO$_6$ could provide
guidance to experimentalists and theorists working on this dimerized chain
system, such as short-range magnetic coupling, doping effects, and possible
pairing tendencies.


title:
Using Explainable Boosting Machine to Compare Idiographic and Nomothetic
  Approaches for Ecological Momentary Assessment Data

abstract:
  Previous research on EMA data of mental disorders was mainly focused on
multivariate regression-based approaches modeling each individual separately.
This paper goes a step further towards exploring the use of non-linear
interpretable machine learning (ML) models in classification problems. ML
models can enhance the ability to accurately predict the occurrence of
different behaviors by recognizing complicated patterns between variables in
data. To evaluate this, the performance of various ensembles of trees are
compared to linear models using imbalanced synthetic and real-world datasets.
After examining the distributions of AUC scores in all cases, non-linear models
appear to be superior to baseline linear models. Moreover, apart from
personalized approaches, group-level prediction models are also likely to offer
an enhanced performance. According to this, two different nomothetic approaches
to integrate data of more than one individuals are examined, one using directly
all data during training and one based on knowledge distillation.
Interestingly, it is observed that in one of the two real-world datasets,
knowledge distillation method achieves improved AUC scores (mean relative
change of +17\% compared to personalized) showing how it can benefit EMA data
classification and performance.


title:
Deep Image: A precious image based deep learning method for online
  malware detection in IoT Environment

abstract:
  The volume of malware and the number of attacks in IoT devices are rising
everyday, which encourages security professionals to continually enhance their
malware analysis tools. Researchers in the field of cyber security have
extensively explored the usage of sophisticated analytics and the efficiency of
malware detection. With the introduction of new malware kinds and attack
routes, security experts confront considerable challenges in developing
efficient malware detection and analysis solutions. In this paper, a different
view of malware analysis is considered and the risk level of each sample
feature is computed, and based on that the risk level of that sample is
calculated. In this way, a criterion is introduced that is used together with
accuracy and FPR criteria for malware analysis in IoT environment. In this
paper, three malware detection methods based on visualization techniques called
the clustering approach, the probabilistic approach, and the deep learning
approach are proposed. Then, in addition to the usual machine learning criteria
namely accuracy and FPR, a proposed criterion based on the risk of samples has
also been used for comparison, with the results showing that the deep learning
approach performed better in detecting malware


title: Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

abstract:
  Large language models can encode a wealth of semantic knowledge about the
world. Such knowledge could be extremely useful to robots aiming to act upon
high-level, temporally extended instructions expressed in natural language.
However, a significant weakness of language models is that they lack real-world
experience, which makes it difficult to leverage them for decision making
within a given embodiment. For example, asking a language model to describe how
to clean a spill might result in a reasonable narrative, but it may not be
applicable to a particular agent, such as a robot, that needs to perform this
task in a particular environment. We propose to provide real-world grounding by
means of pretrained skills, which are used to constrain the model to propose
natural language actions that are both feasible and contextually appropriate.
The robot can act as the language model's "hands and eyes," while the language
model supplies high-level semantic knowledge about the task. We show how
low-level skills can be combined with large language models so that the
language model provides high-level knowledge about the procedures for
performing complex and temporally-extended instructions, while value functions
associated with these skills provide the grounding necessary to connect this
knowledge to a particular physical environment. We evaluate our method on a
number of real-world robotic tasks, where we show the need for real-world
grounding and that this approach is capable of completing long-horizon,
abstract, natural language instructions on a mobile manipulator. The project's
website and the video can be found at https://say-can.github.io/


title: Long Movie Clip Classification with State-Space Video Models

abstract:
  Most modern video recognition models are designed to operate on short video
clips (e.g., 5-10s in length). Because of this, it is challenging to apply such
models to long movie understanding tasks, which typically require sophisticated
long-range temporal reasoning capabilities. The recently introduced video
transformers partially address this issue by using long-range temporal
self-attention. However, due to the quadratic cost of self-attention, such
models are often costly and impractical to use. Instead, we propose ViS4mer, an
efficient long-range video model that combines the strengths of self-attention
and the recently introduced structured state-space sequence (S4) layer. Our
model uses a standard Transformer encoder for short-range spatiotemporal
feature extraction, and a multi-scale temporal S4 decoder for subsequent
long-range temporal reasoning. By progressively reducing the spatiotemporal
feature resolution and channel dimension at each decoder layer, ViS4mer learns
complex long-range spatiotemporal dependencies in a video. Furthermore, ViS4mer
is $2.63\times$ faster and requires $8\times$ less GPU memory than the
corresponding pure self-attention-based model. Additionally, ViS4mer achieves
state-of-the-art results in $7$ out of $9$ long-form movie video classification
tasks on the LVU benchmark. Furthermore, we also show that our approach
successfully generalizes to other domains, achieving competitive results on the
Breakfast and the COIN procedural activity datasets. The code will be made
publicly available.


title: Monitoring social distancing with single image depth estimation

abstract:
  The recent pandemic emergency raised many challenges regarding the
countermeasures aimed at containing the virus spread, and constraining the
minimum distance between people resulted in one of the most effective
strategies. Thus, the implementation of autonomous systems capable of
monitoring the so-called social distance gained much interest. In this paper,
we aim to address this task leveraging a single RGB frame without additional
depth sensors. In contrast to existing single-image alternatives failing when
ground localization is not available, we rely on single image depth estimation
to perceive the 3D structure of the observed scene and estimate the distance
between people. During the setup phase, a straightforward calibration
procedure, leveraging a scale-aware SLAM algorithm available even on consumer
smartphones, allows us to address the scale ambiguity affecting single image
depth estimation. We validate our approach through indoor and outdoor images
employing a calibrated LiDAR + RGB camera asset. Experimental results highlight
that our proposal enables sufficiently reliable estimation of the
inter-personal distance to monitor social distancing effectively. This fact
confirms that despite its intrinsic ambiguity, if appropriately driven single
image depth estimation can be a viable alternative to other depth perception
techniques, more expensive and not always feasible in practical applications.
Our evaluation also highlights that our framework can run reasonably fast and
comparably to competitors, even on pure CPU systems. Moreover, its practical
deployment on low-power systems is around the corner.


title:
"This is my unicorn, Fluffy": Personalizing frozen vision-language
  representations

abstract:
  Large Vision & Language models pretrained on web-scale data provide
representations that are invaluable for numerous V&L problems. However, it is
unclear how they can be used for reasoning about user-specific visual concepts
in unstructured language. This problem arises in multiple domains, from
personalized image retrieval to personalized interaction with smart devices. We
introduce a new learning setup called Personalized Vision & Language (PerVL)
with two new benchmark datasets for retrieving and segmenting user-specific
"personalized" concepts "in the wild". In PerVL, one should learn personalized
concepts (1) independently of the downstream task (2) allowing a pretrained
model to reason about them with free language, and (3) does not require
personalized negative examples. We propose an architecture for solving PerVL
that operates by extending the input vocabulary of a pretrained model with new
word embeddings for the new personalized concepts. The model can then reason
about them by simply using them in a sentence. We demonstrate that our approach
learns personalized visual concepts from a few examples and can effectively
apply them in image retrieval and semantic segmentation using rich textual
queries.


title: LISA: Learning Implicit Shape and Appearance of Hands

abstract:
  This paper proposes a do-it-all neural model of human hands, named LISA. The
model can capture accurate hand shape and appearance, generalize to arbitrary
hand subjects, provide dense surface correspondences, be reconstructed from
images in the wild and easily animated. We train LISA by minimizing the shape
and appearance losses on a large set of multi-view RGB image sequences
annotated with coarse 3D poses of the hand skeleton. For a 3D point in the hand
local coordinate, our model predicts the color and the signed distance with
respect to each hand bone independently, and then combines the per-bone
predictions using predicted skinning weights. The shape, color and pose
representations are disentangled by design, allowing to estimate or animate
only selected parameters. We experimentally demonstrate that LISA can
accurately reconstruct a dynamic hand from monocular or multi-view sequences,
achieving a noticeably higher quality of reconstructed hand shapes compared to
baseline approaches. Project page:
https://www.iri.upc.edu/people/ecorona/lisa/.


title:
Joint Hand Motion and Interaction Hotspots Prediction from Egocentric
  Videos

abstract:
  We propose to forecast future hand-object interactions given an egocentric
video. Instead of predicting action labels or pixels, we directly predict the
hand motion trajectory and the future contact points on the next active object
(i.e., interaction hotspots). This relatively low-dimensional representation
provides a concrete description of future interactions. To tackle this task, we
first provide an automatic way to collect trajectory and hotspots labels on
large-scale data. We then use this data to train an Object-Centric Transformer
(OCT) model for prediction. Our model performs hand and object interaction
reasoning via the self-attention mechanism in Transformers. OCT also provides a
probabilistic framework to sample the future trajectory and hotspots to handle
uncertainty in prediction. We perform experiments on the Epic-Kitchens-55,
Epic-Kitchens-100, and EGTEA Gaze+ datasets, and show that OCT significantly
outperforms state-of-the-art approaches by a large margin. Project page is
available at https://stevenlsw.github.io/hoi-forecast .


title: MaxViT: Multi-Axis Vision Transformer

abstract:
  Transformers have recently gained significant attention in the computer
vision community. However, the lack of scalability of self-attention mechanisms
with respect to image size has limited their wide adoption in state-of-the-art
vision backbones. In this paper we introduce an efficient and scalable
attention model we call multi-axis attention, which consists of two aspects:
blocked local and dilated global attention. These design choices allow
global-local spatial interactions on arbitrary input resolutions with only
linear complexity. We also present a new architectural element by effectively
blending our proposed attention model with convolutions, and accordingly
propose a simple hierarchical vision backbone, dubbed MaxViT, by simply
repeating the basic building block over multiple stages. Notably, MaxViT is
able to "see" globally throughout the entire network, even in earlier,
high-resolution stages. We demonstrate the effectiveness of our model on a
broad spectrum of vision tasks. On image classification, MaxViT achieves
state-of-the-art performance under various settings: without extra data, MaxViT
attains 86.5\% ImageNet-1K top-1 accuracy; with ImageNet-21K pre-training, our
model achieves 88.7\% top-1 accuracy. For downstream tasks, MaxViT as a
backbone delivers favorable performance on object detection as well as visual
aesthetic assessment. We also show that our proposed model expresses strong
generative modeling capability on ImageNet, demonstrating the superior
potential of MaxViT blocks as a universal vision module. We will make the code
and models publicly available.


title:
The miniJPAS survey: Identification and characterization of the emission
  line galaxies down to $z < 0.35$ in the AEGIS field

abstract:
  The Javalambre-Physics of the Accelerating Universe Astrophysical Survey
(J-PAS) is expected to map thousands of square degrees of the northern sky with
56 narrowband filters in the upcoming years. This will make J-PAS a very
competitive and unbiased emission line survey compared to spectroscopic or
narrowband surveys with fewer filters. The miniJPAS survey covered 1 deg$^2$,
and it used the same photometric system as J-PAS, but the observations were
carried out with the pathfinder J-PAS camera. In this work, we identify and
characterize the sample of emission line galaxies (ELGs) from miniJPAS with a
redshift lower than $0.35$. Using a method based on artificial neural networks,
we detect the ELG population and measure the equivalent width and flux of the
$H\alpha$, $H\beta$, [OIII], and [NII] emission lines. We explore the
ionization mechanism using the diagrams [OIII]/H$\beta$ versus [NII]/H$\alpha$
(BPT) and EW(H$\alpha$) versus [NII]/H$\alpha$ (WHAN). We identify 1787 ELGs
($83$%) from the parent sample (2154 galaxies) in the AEGIS field. For the
galaxies with reliable EW values that can be placed in the WHAN diagram (2000
galaxies in total), we obtained that $72.8 \pm 0.4$%, $17.7 \pm 0.4$% , and
$9.4 \pm 0.2$% are star-forming (SF), active galactic nucleus (Seyfert), and
quiescent galaxies, respectively. Based on the flux of $H\alpha$ we find that
the star formation main sequence is described as $\log$ SFR $[M_\mathrm{\odot}
\mathrm{yr}^{-1}] = 0.90^{+ 0.02}_{-0.02} \log M_{\star} [M_\mathrm{\odot}]
-8.85^{+ 0.19}_{-0.20}$ and has an intrinsic scatter of $0.20^{+
0.01}_{-0.01}$. The cosmic evolution of the SFR density ($\rho_{\text{SFR}}$)
is derived at three redshift bins: $0 < z \leq 0.15$, $0.15 < z \leq 0.25$, and
$0.25 < z \leq 0.35$, which agrees with previous results that were based on
measurements of the $H\alpha$ emission line.


title:
Surface tension effects on flow dynamics and alveolar mechanics in the
  acinar region of human lung

abstract:
  Computational fluid dynamics (CFD) simulations, in-vitro setups, and
experimental ex-vivo approaches have been applied to numerous alveolar
geometries over the past years. They aimed to study and examine airflow
patterns, particle transport, and particle-alveolar wall deposition fractions.
These studies are imperative to both pharmaceutical and toxicological studies,
especially nowadays with the escalation of the menacing COVID-19 virus.
However, most of these studies ignored the surfactant layer that covers the
alveoli and the effect of the air-surfactant surface tension on flow dynamics
and air-alveolar surface mechanics. The present study employs a realistic human
breathing profile of 4.75 to emphasize the importance of the surfactant layer
by numerically comparing airflow phenomena between a surfactant-enriched and
surfactant-deficient model. The acinar model exhibits physiologically accurate
alveolar and duct dimensions extending from lung generations 18 to 23. Proximal
lung generations experience dominant recirculating flow while farther
generations in the distal alveolar region exhibit dominant radial flows. In the
surfactant-enriched model, surface tension values alternate during inhalation
and exhalation. In the surfactant-deficient model, only water coats the
alveolar walls. Results showed that surfactant deficiency in the alveoli
adversely alters airflow behavior and generates unsteady chaotic breathing
through the production of vorticities, accompanied by higher vorticity and
velocity magnitudes. In addition, high air-water surface tension in the
surfactant-deficient case was found to induce higher shear stress values on the
alveolar walls than that of the surfactant-enriched case. Overall, it was
concluded that the presence of the surfactant improves respiratory mechanics
and allows for smooth breathing and normal respiration.


title:
Modeling COVID-19 vaccine-induced immunological memory development and
  its links to antibody level and infectiousness

abstract:
  COVID-19 vaccines have proven to be effective against SARS-CoV-2 infection.
However, the dynamics of vaccine-induced immunological memory development and
neutralizing antibodies generation are not fully understood, limiting vaccine
development and vaccination regimen determination. Herein, we constructed a
mathematical model to characterize the vaccine-induced immune response based on
fitting the viral infection and vaccination datasets. With the example of
CoronaVac, we revealed the association between vaccine-induced immunological
memory development and neutralizing antibody levels. The establishment of the
intact immunological memory requires more than 6 months after the first and
second doses, after that a booster shot can induce high levels neutralizing
antibodies. By introducing the maximum viral load and recovery time after viral
infection, we quantitatively studied the protective effect of vaccines against
viral infection. Accordingly, we optimized the vaccination regimen, including
dose and vaccination timing, and predicted the effect of the fourth dose. Last,
by combining the viral transmission model, we showed the suppression of virus
transmission by vaccination, which may be instructive for the development of
public health policies.


title:
QuadraLib: A Performant Quadratic Neural Network Library for
  Architecture Optimization and Design Exploration

abstract:
  The significant success of Deep Neural Networks (DNNs) is highly promoted by
the multiple sophisticated DNN libraries. On the contrary, although some work
have proved that Quadratic Deep Neuron Networks (QDNNs) show better
non-linearity and learning capability than the first-order DNNs, their neuron
design suffers certain drawbacks from theoretical performance to practical
deployment. In this paper, we first proposed a new QDNN neuron architecture
design, and further developed QuadraLib, a QDNN library to provide architecture
optimization and design exploration for QDNNs. Extensive experiments show that
our design has good performance regarding prediction accuracy and computation
consumption on multiple learning tasks.


title:
Personalized Prediction of Future Lesion Activity and Treatment Effect
  in Multiple Sclerosis from Baseline MRI

abstract:
  Precision medicine for chronic diseases such as multiple sclerosis (MS)
involves choosing a treatment which best balances efficacy and side
effects/preferences for individual patients. Making this choice as early as
possible is important, as delays in finding an effective therapy can lead to
irreversible disability accrual. To this end, we present the first deep neural
network model for individualized treatment decisions from baseline magnetic
resonance imaging (MRI) (with clinical information if available) for MS
patients. Our model (a) predicts future new and enlarging T2 weighted (NE-T2)
lesion counts on follow-up MRI on multiple treatments and (b) estimates the
conditional average treatment effect (CATE), as defined by the predicted future
suppression of NE-T2 lesions, between different treatment options relative to
placebo. Our model is validated on a proprietary federated dataset of 1817
multi-sequence MRIs acquired from MS patients during four multi-centre
randomized clinical trials. Our framework achieves high average precision in
the binarized regression of future NE-T2 lesions on five different treatments,
identifies heterogeneous treatment effects, and provides a personalized
treatment recommendation that accounts for treatment-associated risk (e.g. side
effects, patient preference, administration difficulties).


title: Symplectic forms on Banach spaces

abstract:
  We extend and generalize the result of Kalton and Swanson ($Z_2$ is a
symplectic Banach space with no Lagrangian subspace) by showing that all higher
order Rochgberg spaces $\mathfrak R^{(n)}$ are symplectic Banach spaces with no
Lagrangian subspaces. The nontrivial symplectic structure on even spaces is the
one induced by the natural duality; while the nontrivial symplectic structure
on odd spaces requires perturbation with a complex structure. We will also
study symplectic structures on general Banach spaces and, motivated by the
unexpected appearance of complex structures, we introduce and study almost
symplectic structures.


title: Twisted sums of $c_0(I)$

abstract:
  The paper studies properties of twisted sums of a Banach space $X$ with
$c_0(\kappa)$. We first prove a representation theorem for such twisted sums
from which we will obtain, among others, the following: (a) twisted sums of
$c_0(I)$ and $c_0(\kappa)$ are either subspaces of $\ell_\infty(\kappa)$ or
trivial on a copy of $c_0(\kappa^+)$; (b) under the hypothesis $[\mathfrak p =
\mathfrak c]$, when $K$ is either a suitable Corson compact, a separable
Rosenthal compact or a scattered compact of finite height, there is a twisted
sum of $C(K)$ with $c_0(\kappa)$ that is not isomorphic to a space of
continuous functions; (c) all such twisted sums are Lindenstrauss spaces when
$X$ is a Lindenstrauss space and $G$-spaces when $X=C(K)$ with $K$ convex,
which shows tat a result of Benyamini is optimal; (d) they are isomorphically
polyhedral when $X$ is a polyhedral space with property ($\star$), which solves
a problem of Castillo and Papini.


title: Learning to Accelerate by the Methods of Step-size Planning

abstract:
  Gradient descent is slow to converge for ill-conditioned problems and
non-convex problems. An important technique for acceleration is step-size
adaptation. The first part of this paper contains a detailed review of
step-size adaptation methods, including Polyak step-size, L4, LossGrad, Adam,
IDBD, and Hypergradient descent, and the relation of step-size adaptation to
meta-gradient methods. In the second part of this paper, we propose a new class
of methods of accelerating gradient descent that have some distinctiveness from
existing techniques. The new methods, which we call {\em step-size planning},
use the {\em update experience} to learn an improved way of updating the
parameters. The methods organize the experience into $K$ steps away from each
other to facilitate planning. From the past experience, our planning algorithm,
Csawg, learns a step-size model which is a form of multi-step machine that
predicts future updates. We extends Csawg to applying step-size planning
multiple steps, which leads to further speedup. We discuss and highlight the
projection power of the diagonal-matrix step-size for future large scale
applications. We show for a convex problem, our methods can surpass the
convergence rate of Nesterov's accelerated gradient, $1 - \sqrt{\mu/L}$, where
$\mu, L$ are the strongly convex factor of the loss function $F$ and the
Lipschitz constant of $F'$, which is the theoretical limit for the convergence
rate of first-order methods. On the well-known non-convex Rosenbrock function,
our planning methods achieve zero error below 500 gradient evaluations, while
gradient descent takes about 10000 gradient evaluations to reach a $10^{-3}$
accuracy. We discuss the connection of step-size planing to planning in
reinforcement learning, in particular, Dyna architectures.


title:
Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and
  Methodologies from CNN, GAN to Attention and Transformers

abstract:
  Research studies have shown no qualms about using data driven deep learning
models for downstream tasks in medical image analysis, e.g., anatomy
segmentation and lesion detection, disease diagnosis and prognosis, and
treatment planning. However, deep learning models are not the sovereign remedy
for medical image analysis when the upstream imaging is not being conducted
properly (with artefacts). This has been manifested in MRI studies, where the
scanning is typically slow, prone to motion artefacts, with a relatively low
signal to noise ratio, and poor spatial and/or temporal resolution. Recent
studies have witnessed substantial growth in the development of deep learning
techniques for propelling fast MRI. This article aims to (1) introduce the deep
learning based data driven techniques for fast MRI including convolutional
neural network and generative adversarial network based methods, (2) survey the
attention and transformer based models for speeding up MRI reconstruction, and
(3) detail the research in coupling physics and data driven models for MRI
acceleration. Finally, we will demonstrate through a few clinical applications,
explain the importance of data harmonisation and explainable models for such
fast MRI techniques in multicentre and multi-scanner studies, and discuss
common pitfalls in current research and recommendations for future research
directions.


title: Heterogeneous Autoencoder Empowered by Quadratic Neurons

abstract:
  Inspired by the complexity and diversity of biological neurons, a quadratic
neuron is proposed to replace the inner product in the current neuron with a
simplified quadratic function. Employing such a novel type of neurons offers a
new perspective on developing deep learning. When analyzing quadratic neurons,
we find that there exists a function such that a heterogeneous network can
approximate it well with a polynomial number of neurons but a purely
conventional or quadratic network needs an exponential number of neurons to
achieve the same level of error. Encouraged by this inspiring theoretical
result on heterogeneous networks, we directly integrate conventional and
quadratic neurons in an autoencoder to make a new type of heterogeneous
autoencoders. Anomaly detection experiments confirm that heterogeneous
autoencoders perform competitively compared to other state-of-the-art models.


title:
MRI-based Multi-task Decoupling Learning for Alzheimer's Disease
  Detection and MMSE Score Prediction: A Multi-site Validation

abstract:
  Accurately detecting Alzheimer's disease (AD) and predicting mini-mental
state examination (MMSE) score are important tasks in elderly health by
magnetic resonance imaging (MRI). Most of the previous methods on these two
tasks are based on single-task learning and rarely consider the correlation
between them. Since the MMSE score, which is an important basis for AD
diagnosis, can also reflect the progress of cognitive impairment, some studies
have begun to apply multi-task learning methods to these two tasks. However,
how to exploit feature correlation remains a challenging problem for these
methods. To comprehensively address this challenge, we propose a MRI-based
multi-task decoupled learning method for AD detection and MMSE score
prediction. First, a multi-task learning network is proposed to implement AD
detection and MMSE score prediction, which exploits feature correlation by
adding three multi-task interaction layers between the backbones of the two
tasks. Each multi-task interaction layer contains two feature decoupling
modules and one feature interaction module. Furthermore, to enhance the
generalization between tasks of the features selected by the feature decoupling
module, we propose the feature consistency loss constrained feature decoupling
module. Finally, in order to exploit the specific distribution information of
MMSE score in different groups, a distribution loss is proposed to further
enhance the model performance. We evaluate our proposed method on multi-site
datasets. Experimental results show that our proposed multi-task decoupled
representation learning method achieves good performance, outperforming
single-task learning and other existing state-of-the-art methods.


title: Forestry digital twin with machine learning in Landsat 7 data

abstract:
  Modeling forests using historical data allows for more accurately evolution
analysis, thus providing an important basis for other studies. As a recognized
and effective tool, remote sensing plays an important role in forestry
analysis. We can use it to derive information about the forest, including tree
type, coverage and canopy density. There are many forest time series modeling
studies using statistic values, but few using remote sensing images. Image
prediction digital twin is an implementation of digital twin, which aims to
predict future images bases on historical data. In this paper, we propose an
LSTM-based digital twin approach for forest modeling, using Landsat 7 remote
sensing image within 20 years. The experimental results show that the
prediction twin method in this paper can effectively predict the future images
of study area.


title: Convolutional Neural Networks for Image Spam Detection

abstract:
  Spam can be defined as unsolicited bulk email. In an effort to evade
text-based filters, spammers sometimes embed spam text in an image, which is
referred to as image spam. In this research, we consider the problem of image
spam detection, based on image analysis. We apply convolutional neural networks
(CNN) to this problem, we compare the results obtained using CNNs to other
machine learning techniques, and we compare our results to previous related
work. We consider both real-world image spam and challenging image spam-like
datasets. Our results improve on previous work by employing CNNs based on a
novel feature set consisting of a combination of the raw image and Canny edges.


title:
Single Image Internal Distribution Measurement Using Non-Local
  Variational Autoencoder

abstract:
  Deep learning-based super-resolution methods have shown great promise,
especially for single image super-resolution (SISR) tasks. Despite the
performance gain, these methods are limited due to their reliance on copious
data for model training. In addition, supervised SISR solutions rely on local
neighbourhood information focusing only on the feature learning processes for
the reconstruction of low-dimensional images. Moreover, they fail to capitalize
on global context due to their constrained receptive field. To combat these
challenges, this paper proposes a novel image-specific solution, namely
non-local variational autoencoder (\texttt{NLVAE}), to reconstruct a
high-resolution (HR) image from a single low-resolution (LR) image without the
need for any prior training. To harvest maximum details for various receptive
regions and high-quality synthetic images, \texttt{NLVAE} is introduced as a
self-supervised strategy that reconstructs high-resolution images using
disentangled information from the non-local neighbourhood. Experimental results
from seven benchmark datasets demonstrate the effectiveness of the
\texttt{NLVAE} model. Moreover, our proposed model outperforms a number of
baseline and state-of-the-art methods as confirmed through extensive
qualitative and quantitative evaluations.


title:
Histogram of Oriented Gradients Meet Deep Learning: A Novel Multi-task
  Deep Network for Medical Image Semantic Segmentation

abstract:
  We present our novel deep multi-task learning method for medical image
segmentation. Existing multi-task methods demand ground truth annotations for
both the primary and auxiliary tasks. Contrary to it, we propose to generate
the pseudo-labels of an auxiliary task in an unsupervised manner. To generate
the pseudo-labels, we leverage Histogram of Oriented Gradients (HOGs), one of
the most widely used and powerful hand-crafted features for detection. Together
with the ground truth semantic segmentation masks for the primary task and
pseudo-labels for the auxiliary task, we learn the parameters of the deep
network to minimise the loss of both the primary task and the auxiliary task
jointly. We employed our method on two powerful and widely used semantic
segmentation networks: UNet and U2Net to train in a multi-task setup. To
validate our hypothesis, we performed experiments on two different medical
image segmentation data sets. From the extensive quantitative and qualitative
results, we observe that our method consistently improves the performance
compared to the counter-part method. Moreover, our method is the winner of
FetReg Endovis Sub-challenge on Semantic Segmentation organised in conjunction
with MICCAI 2021.


title: Exemplar Learning for Medical Image Segmentation

abstract:
  Medical image annotation typically requires expert knowledge and hence incurs
time-consuming and expensive data annotation costs. To reduce this burden, we
propose a novel learning scenario, Exemplar Learning (EL), to explore automated
learning processes for medical image segmentation from a single annotated image
example. This innovative learning task is particularly suitable for medical
image segmentation, where all categories of organs can be presented in one
single image for annotation all at once. To address this challenging EL task,
we propose an Exemplar Learning-based Synthesis Net (ELSNet) framework for
medical image segmentation that enables innovative exemplar-based data
synthesis, pixel-prototype based contrastive embedding learning, and
pseudo-label based exploitation of the unlabeled data. Specifically, ELSNet
introduces two new modules for image segmentation: an exemplar-guided synthesis
module, which enriches and diversifies the training set by synthesizing
annotated samples from the given exemplar, and a pixel-prototype based
contrastive embedding module, which enhances the discriminative capacity of the
base segmentation model via contrastive self-supervised learning. Moreover, we
deploy a two-stage process for segmentation model training, which exploits the
unlabeled data with predicted pseudo segmentation labels. To evaluate this new
learning framework, we conduct extensive experiments on several organ
segmentation datasets and present an in-depth analysis. The empirical results
show that the proposed exemplar learning framework produces effective
segmentation results.


title: Quantum teleportation between two quantum spin Hall insulator rings

abstract:
  A quantum teleportation between two quantum spin Hall insulator (QSHI) rings
is proposed. A qubit state is generated in the first QSHI ring and the second
is used as a quantum channel in entangled QSHI state. In the intersection of
outer edges is placed a joint detector, allowing a Bell state analysis. After
the Bell state measurement, Alice, that is composed originally of a qubit in
the first QSHI ring and one spin state in the second QSHI ring, allows Bob,
that is composed of the other spin edge state in the second QSHI ring, to
realize the corresponding control of the tunneling amplitudes in the second
QSHI ring, what implements a complete quantum teleportation between the QSHI
rings. It is also showed that Bob's table, used after the classical
communication by Alice, is the same independently the way the qubit is
generated in the first QSHI ring. This description indicates that a quantum
teleportation can be fully implemented in the context of two-dimensional (2D)
topological edge states and opens perspectives in the scenario of quantum
information in topological matter, decoherence free systems, as well as for the
development of quantum technologies associated to spintronics and quantum
computation.

categories: quant-ph cond-mat.mes-hall cond-mat.mtrl-sci cond-mat.str-el


title:
BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed
  Cluster

abstract:
  Most AI projects start with a Python notebook running on a single laptop;
however, one usually needs to go through a mountain of pains to scale it to
handle larger dataset (for both experimentation and production deployment).
These usually entail many manual and error-prone steps for the data scientists
to fully take advantage of the available hardware resources (e.g., SIMD
instructions, multi-processing, quantization, memory allocation optimization,
data partitioning, distributed computing, etc.). To address this challenge, we
have open sourced BigDL 2.0 at https://github.com/intel-analytics/BigDL/ under
Apache 2.0 license (combining the original BigDL and Analytics Zoo projects);
using BigDL 2.0, users can simply build conventional Python notebooks on their
laptops (with possible AutoML support), which can then be transparently
accelerated on a single node (with up-to 9.6x speedup in our experiments), and
seamlessly scaled out to a large cluster (across several hundreds servers in
real-world use cases). BigDL 2.0 has already been adopted by many real-world
users (such as Mastercard, Burger King, Inspur, etc.) in production.


title: Estimating Fine-Grained Noise Model via Contrastive Learning

abstract:
  Image denoising has achieved unprecedented progress as great efforts have
been made to exploit effective deep denoisers. To improve the denoising
performance in realworld, two typical solutions are used in recent trends:
devising better noise models for the synthesis of more realistic training data,
and estimating noise level function to guide non-blind denoisers. In this work,
we combine both noise modeling and estimation, and propose an innovative noise
model estimation and noise synthesis pipeline for realistic noisy image
generation. Specifically, our model learns a noise estimation model with
fine-grained statistical noise model in a contrastive manner. Then, we use the
estimated noise parameters to model camera-specific noise distribution, and
synthesize realistic noisy training data. The most striking thing for our work
is that by calibrating noise models of several sensors, our model can be
extended to predict other cameras. In other words, we can estimate
cameraspecific noise models for unknown sensors with only testing images,
without laborious calibration frames or paired noisy/clean data. The proposed
pipeline endows deep denoisers with competitive performances with
state-of-the-art real noise modeling methods.


title: Strong Solution of Modified Anistropic 3D-Navier-Stokes Equations

abstract:
  In this paper we study the anisotropic incompressible Navier-Stokes equations
with a logarithm damping $\alpha \log(e+|u|^2)|u|^2u$ in $H^{0.1}$, where we
used new methods, new tools and Fourier analysis.


title: Are There Any Real Problems With Quantum Gravity?

abstract:
  We present a short, general and accessible introduction to quantizing gravity
in the Heisenberg picture. We then apply this formalism to the scenario where
two spatially superposed masses interact through the gravitational field. We
discuss some of the consequences for quantum gravity including the spin of the
graviton, the notions of locality and causality, as well as going beyond the
linear regime. We use the Schwinger action principle and the Heisenberg
representation which we believe make the issues involved clearer. We conclude
by commenting on a number of traditionally discussed apparent problems with
quantizing gravity (only to, ultimately, deny their very existence).


title:
RestoreX-AI: A Contrastive Approach towards Guiding Image Restoration
  via Explainable AI Systems

abstract:
  Modern applications such as self-driving cars and drones rely heavily upon
robust object detection techniques. However, weather corruptions can hinder the
object detectability and pose a serious threat to their navigation and
reliability. Thus, there is a need for efficient denoising, deraining, and
restoration techniques. Generative adversarial networks and transformers have
been widely adopted for image restoration. However, the training of these
methods is often unstable and time-consuming. Furthermore, when used for object
detection (OD), the output images generated by these methods may provide
unsatisfactory results despite image clarity. In this work, we propose a
contrastive approach towards mitigating this problem, by evaluating images
generated by restoration models during and post training. This approach
leverages OD scores combined with attention maps for predicting the usefulness
of restored images for the OD task. We conduct experiments using two novel
use-cases of conditional GANs and two transformer methods that probe the
robustness of the proposed approach on multi-weather corruptions in the OD
task. Our approach achieves an averaged 178 percent increase in mAP between the
input and restored images under adverse weather conditions like dust tornadoes
and snowfall. We report unique cases where greater denoising does not improve
OD performance and conversely where noisy generated images demonstrate good
results. We conclude the need for explainability frameworks to bridge the gap
between human and machine perception, especially in the context of robust
object detection for autonomous vehicles.


title: Numerov and phase-integral methods for charmonium

abstract:
  This paper applies the Numerov and phase-integral methods to the stationary
Schrodinger equation that studies bound states of charm anti-charm quarks. The
former is a numerical method well suited for a matrix form of second-order
ordinary di erential equations, and can be applied whenever the stationary
states admit a Taylor-series expansion. The latter is an analytic method that
provides, in principle, even exact solutions of the stationary Schrodinger
equation, and well suited for applying matched asymptotic expansions and higher
order quantization conditions. The Numerov method is found to be always in
agreement with the early results of Eichten et al., whereas an original
evaluation of the phase-integral quantization condition clarifies under which
conditions the previous results in the literature on higher-order terms can be
obtained.


title:
Meta-Learning Approaches for a One-Shot Collective-Decision Aggregation:
  Correctly Choosing how to Choose Correctly

abstract:
  Aggregating successfully the choices regarding a given decision problem made
by the multiple collective members into a single solution is essential for
exploiting the collective's intelligence and for effective crowdsourcing. There
are various aggregation techniques, some of which come down to a simple and
sometimes effective deterministic aggregation rule. However, it has been shown
that the efficiency of those techniques is unstable under varying conditions
and within different domains. Other methods mainly rely on learning from the
decision-makers previous responses or the availability of additional
information about them. In this study, we present two one-shot
machine-learning-based aggregation approaches. The first predicts, given
multiple features about the collective's choices, including meta-cognitive
ones, which aggregation method will be best for a given case. The second
directly predicts which decision is optimal, given, among other things, the
selection made by each method. We offer a meta-cognitive feature-engineering
approach for characterizing a collective decision-making case in a
context-sensitive fashion. In addition, we offer a new aggregation method, the
Devil's-Advocate aggregator, to deal with cases in which standard aggregation
methods are predicted to fail. Experimental results show that using either of
our proposed approaches increases the percentage of successfully aggregated
cases (i.e., cases in which the correct answer is returned) significantly,
compared to the uniform application of each rule-based aggregation method. We
also demonstrate the importance of the Devil's Advocate aggregator.


title: Performance Portable Solid Mechanics via Matrix-Free $p$-Multigrid

abstract:
  Finite element analysis of solid mechanics is a foundational tool of modern
engineering, with low-order finite element methods and assembled sparse
matrices representing the industry standard for implicit analysis. We use
performance models and numerical experiments to demonstrate that high-order
methods greatly reduce the costs to reach engineering tolerances while enabling
effective use of GPUs. We demonstrate the reliability, efficiency, and
scalability of matrix-free $p$-multigrid methods with algebraic multigrid
coarse solvers through large deformation hyperelastic simulations of multiscale
structures. We investigate accuracy, cost, and execution time on multi-node CPU
and GPU systems for moderate to large models using AMD MI250X (OLCF Crusher),
NVIDIA A100 (NERSC Perlmutter), and V100 (LLNL Lassen and OLCF Summit),
resulting in order of magnitude efficiency improvements over a broad range of
model properties and scales. We discuss efficient matrix-free representation of
Jacobians and demonstrate how automatic differentiation enables rapid
development of nonlinear material models without impacting debuggability and
workflows targeting GPUs.


title: Forward Signal Propagation Learning

abstract:
  We propose a new learning algorithm for propagating a learning signal and
updating neural network parameters via a forward pass, as an alternative to
backpropagation. In forward signal propagation learning (sigprop), there is
only the forward path for learning and inference, so there are no additional
structural or computational constraints on learning, such as feedback
connectivity, weight transport, or a backward pass, which exist under
backpropagation. Sigprop enables global supervised learning with only a forward
path. This is ideal for parallel training of layers or modules. In biology,
this explains how neurons without feedback connections can still receive a
global learning signal. In hardware, this provides an approach for global
supervised learning without backward connectivity. Sigprop by design has better
compatibility with models of learning in the brain and in hardware than
backpropagation and alternative approaches to relaxing learning constraints. We
also demonstrate that sigprop is more efficient in time and memory than they
are. To further explain the behavior of sigprop, we provide evidence that
sigprop provides useful learning signals in context to backpropagation. To
further support relevance to biological and hardware learning, we use sigprop
to train continuous time neural networks with Hebbian updates and train spiking
neural networks without surrogate functions.


title:
Mathematical Heritage of Sergey Naboko: Functional Models of
  Non-Self-Adjoint Operators

abstract:
  This is an overview of mathematical heritage of Sergey Naboko in the area of
functional models of non-self-adjoint operators. It covers the works by Sergey
in model construction, the analysis of absolutely continuous and singular
spectra and the construction of the scattering theory in model terms.


title:
Distinguishing Homophenes Using Multi-Head Visual-Audio Memory for Lip
  Reading

abstract:
  Recognizing speech from silent lip movement, which is called lip reading, is
a challenging task due to 1) the inherent information insufficiency of lip
movement to fully represent the speech, and 2) the existence of homophenes that
have similar lip movement with different pronunciations. In this paper, we try
to alleviate the aforementioned two challenges in lip reading by proposing a
Multi-head Visual-audio Memory (MVM). Firstly, MVM is trained with audio-visual
datasets and remembers audio representations by modelling the
inter-relationships of paired audio-visual representations. At the inference
stage, visual input alone can extract the saved audio representation from the
memory by examining the learned inter-relationships. Therefore, the lip reading
model can complement the insufficient visual information with the extracted
audio representations. Secondly, MVM is composed of multi-head key memories for
saving visual features and one value memory for saving audio knowledge, which
is designed to distinguish the homophenes. With the multi-head key memories,
MVM extracts possible candidate audio features from the memory, which allows
the lip reading model to consider the possibility of which pronunciations can
be represented from the input lip movement. This also can be viewed as an
explicit implementation of the one-to-many mapping of viseme-to-phoneme.
Moreover, MVM is employed in multi-temporal levels to consider the context when
retrieving the memory and distinguish the homophenes. Extensive experimental
results verify the effectiveness of the proposed method in lip reading and in
distinguishing the homophenes.


title: Lip to Speech Synthesis with Visual Context Attentional GAN

abstract:
  In this paper, we propose a novel lip-to-speech generative adversarial
network, Visual Context Attentional GAN (VCA-GAN), which can jointly model
local and global lip movements during speech synthesis. Specifically, the
proposed VCA-GAN synthesizes the speech from local lip visual features by
finding a mapping function of viseme-to-phoneme, while global visual context is
embedded into the intermediate layers of the generator to clarify the ambiguity
in the mapping induced by homophene. To achieve this, a visual context
attention module is proposed where it encodes global representations from the
local visual features, and provides the desired global visual context
corresponding to the given coarse speech representation to the generator
through audio-visual attention. In addition to the explicit modelling of local
and global visual representations, synchronization learning is introduced as a
form of contrastive learning that guides the generator to synthesize a speech
in sync with the given input lip movements. Extensive experiments demonstrate
that the proposed VCA-GAN outperforms existing state-of-the-art and is able to
effectively synthesize the speech from multi-speaker that has been barely
handled in the previous works.


title:
Existence of a positive hyperbolic Reeb orbit in three spheres with
  finite free group actions

abstract:
  Let $(Y,\lambda)$ be a non-degenerate contact three manifold. D.
Cristfaro-Gardiner, M. Hutshings and D. Pomerleano showed that if
$c_{1}(\xi=\mathrm{Ker}\lambda)$ is torsion, then the Reeb vector field of
$(Y,\lambda)$ has infinity many Reeb orbits otherwise $(Y,\lambda)$ is a lens
space or three sphere with exaxtly two simple elliptic orbits. In the same
paper, they also showed that if $b_{1}(Y)>0$, $(Y,\lambda)$ has a simple
positive hyperbolic orbit directly from the isomorhphism between Seiberg-Witten
Floer homology and Embedded contact homology. In addition to this, they asked
whether $(Y,\lambda)$ with infinity many simple orbits also has a positive
hyperbolic orbit under $b_{1}(Y)=0$. In the present paper, we answer this
question under $Y \simeq S^{3}$ with nontrivial finite free group actions,
especially lens spaces $(L(p,q),\lambda)$ with odd $p$ as quotient spaces of
$S^{3}$.


title: Generalized Zero Shot Learning For Medical Image Classification

abstract:
  In many real world medical image classification settings we do not have
access to samples of all possible disease classes, while a robust system is
expected to give high performance in recognizing novel test data. We propose a
generalized zero shot learning (GZSL) method that uses self supervised learning
(SSL) for: 1) selecting anchor vectors of different disease classes; and 2)
training a feature generator. Our approach does not require class attribute
vectors which are available for natural images but not for medical images. SSL
ensures that the anchor vectors are representative of each class. SSL is also
used to generate synthetic features of unseen classes. Using a simpler
architecture, our method matches a state of the art SSL based GZSL method for
natural images and outperforms all methods for medical images. Our method is
adaptable enough to accommodate class attribute vectors when they are available
for natural images.


title:
Analyzing the Effects of Handling Data Imbalance on Learned Features
  from Medical Images by Looking Into the Models

abstract:
  One challenging property lurking in medical datasets is the imbalanced data
distribution, where the frequency of the samples between the different classes
is not balanced. Training a model on an imbalanced dataset can introduce unique
challenges to the learning problem where a model is biased towards the highly
frequent class. Many methods are proposed to tackle the distributional
differences and the imbalanced problem. However, the impact of these approaches
on the learned features is not well studied. In this paper, we look deeper into
the internal units of neural networks to observe how handling data imbalance
affects the learned features. We study several popular cost-sensitive
approaches for handling data imbalance and analyze the feature maps of the
convolutional neural networks from multiple perspectives: analyzing the
alignment of salient features with pathologies and analyzing the
pathology-related concepts encoded by the networks. Our study reveals
differences and insights regarding the trained models that are not reflected by
quantitative metrics such as AUROC and AP and show up only by looking at the
models through a lens.


title:
Two-dimensional representation of time for a quantum particle in a
  Bose-Einstein condensate

abstract:
  A quantitative quantum field approach for interacting particles using a
complex two dimensional representation of time is presented. The representation
of a two-dimensional complex valued time arises from the constraint of particle
number conservation and can be used to account simultaneously for both
oscillations of the quantum state of a particle in a Bose-Einstein condensate
as well as coherence times of the particle in the atomic cloud below the
critical temperature. It is illustrated that, in contrast to so far established
theories of purely imaginary complex time, two dimensional complex time has a
preferred direction in positive direction of the imaginary axis below the
critical temperature in agreement with the observation of a spontaneously
broken phase gauge symmetry of the underlying fugacity spectrum. The results
reduce to the standard scheme of purely imaginary time above the critical
temperature.


title:
Gan-Based Joint Activity Detection and Channel Estimation For Grant-free
  Random Access

abstract:
  Joint activity detection and channel estimation (JADCE) for grant-free random
access is a critical issue that needs to be addressed to support massive
connectivity in IoT networks. However, the existing model-free learning method
can only achieve either activity detection or channel estimation, but not both.
In this paper, we propose a novel model-free learning method based on
generative adversarial network (GAN) to tackle the JADCE problem. We adopt the
U-net architecture to build the generator rather than the standard GAN
architecture, where a pre-estimated value that contains the activity
information is adopted as input to the generator. By leveraging the properties
of the pseudoinverse, the generator is refined by using an affine projection
and a skip connection to ensure the output of the generator is consistent with
the measurement. Moreover, we build a two-layer fully-connected neural network
to design pilot matrix for reducing the impact of receiver noise. Simulation
results show that the proposed method outperforms the existing methods in high
SNR regimes, as both data consistency projection and pilot matrix optimization
improve the learning ability.


title:
A high-order tensor completion algorithm based on Fully-Connected Tensor
  Network weighted optimization

abstract:
  Tensor completion aimes at recovering missing data, and it is one of the
popular concerns in deep learning and signal processing. Among the higher-order
tensor decomposition algorithms, the recently proposed fully-connected tensor
network decomposition (FCTN) algorithm is the most advanced. In this paper, by
leveraging the superior expression of the fully-connected tensor network (FCTN)
decomposition, we propose a new tensor completion method named the fully
connected tensor network weighted optization(FCTN-WOPT). The algorithm performs
a composition of the completed tensor by initialising the factors from the FCTN
decomposition. We build a loss function with the weight tensor, the completed
tensor and the incomplete tensor together, and then update the completed tensor
using the lbfgs gradient descent algorithm to reduce the spatial memory
occupation and speed up iterations. Finally we test the completion with
synthetic data and real data (both image data and video data) and the results
show the advanced performance of our FCTN-WOPT when it is applied to
higher-order tensor completion.


title:
Transient motion classification through turbid volumes via parallelized
  single-photon detection and deep contrastive embedding

abstract:
  Fast noninvasive probing of spatially varying decorrelating events, such as
cerebral blood flow beneath the human skull, is an essential task in various
scientific and clinical settings. One of the primary optical techniques used is
diffuse correlation spectroscopy (DCS), whose classical implementation uses a
single or few single-photon detectors, resulting in poor spatial localization
accuracy and relatively low temporal resolution. Here, we propose a technique
termed Classifying Rapid decorrelation Events via Parallelized single photon
dEtection (CREPE)}, a new form of DCS that can probe and classify different
decorrelating movements hidden underneath turbid volume with high sensitivity
using parallelized speckle detection from a $32\times32$ pixel SPAD array. We
evaluate our setup by classifying different spatiotemporal-decorrelating
patterns hidden beneath a 5mm tissue-like phantom made with rapidly
decorrelating dynamic scattering media. Twelve multi-mode fibers are used to
collect scattered light from different positions on the surface of the tissue
phantom. To validate our setup, we generate perturbed decorrelation patterns by
both a digital micromirror device (DMD) modulated at multi-kilo-hertz rates, as
well as a vessel phantom containing flowing fluid. Along with a deep
contrastive learning algorithm that outperforms classic unsupervised learning
methods, we demonstrate our approach can accurately detect and classify
different transient decorrelation events (happening in 0.1-0.4s) underneath
turbid scattering media, without any data labeling. This has the potential to
be applied to noninvasively monitor deep tissue motion patterns, for example
identifying normal or abnormal cerebral blood flow events, at multi-Hertz rates
within a compact and static detection probe.


title: On Explaining Multimodal Hateful Meme Detection Models

abstract:
  Hateful meme detection is a new multimodal task that has gained significant
traction in academic and industry research communities. Recently, researchers
have applied pre-trained visual-linguistic models to perform the multimodal
classification task, and some of these solutions have yielded promising
results. However, what these visual-linguistic models learn for the hateful
meme classification task remains unclear. For instance, it is unclear if these
models are able to capture the derogatory or slurs references in multimodality
(i.e., image and text) of the hateful memes. To fill this research gap, this
paper propose three research questions to improve our understanding of these
visual-linguistic models performing the hateful meme classification task. We
found that the image modality contributes more to the hateful meme
classification task, and the visual-linguistic models are able to perform
visual-text slurs grounding to a certain extent. Our error analysis also shows
that the visual-linguistic models have acquired biases, which resulted in
false-positive predictions.


title: Robust Stuttering Detection via Multi-task and Adversarial Learning

abstract:
  By automatic detection and identification of stuttering, speech pathologists
can track the progression of disfluencies of persons who stutter (PWS). In this
paper, we investigate the impact of multi-task (MTL) and adversarial learning
(ADV) to learn robust stutter features. This is the first-ever preliminary
study where MTL and ADV have been employed in stuttering identification (SI).
We evaluate our system on the SEP-28k stuttering dataset consisting of 20 hours
(approx) of data from 385 podcasts. Our methods show promising results and
outperform the baseline in various disfluency classes. We achieve up to 10%,
6.78%, and 2% improvement in repetitions, blocks, and interjections
respectively over the baseline.


title:
Tracking Urbanization in Developing Regions with Remote Sensing
  Spatial-Temporal Super-Resolution

abstract:
  Automated tracking of urban development in areas where construction
information is not available became possible with recent advancements in
machine learning and remote sensing. Unfortunately, these solutions perform
best on high-resolution imagery, which is expensive to acquire and infrequently
available, making it difficult to scale over long time spans and across large
geographies. In this work, we propose a pipeline that leverages a single
high-resolution image and a time series of publicly available low-resolution
images to generate accurate high-resolution time series for object tracking in
urban construction. Our method achieves significant improvement in comparison
to baselines using single image super-resolution, and can assist in extending
the accessibility and scalability of building construction tracking across the
developing world.


title:
Feature robustness and sex differences in medical imaging: a case study
  in MRI-based Alzheimer's disease detection

abstract:
  Convolutional neural networks have enabled significant improvements in
medical image-based disease classification. It has, however, become
increasingly clear that these models are susceptible to performance degradation
due to spurious correlations and dataset shifts, which may lead to
underperformance on underrepresented patient groups, among other problems. In
this paper, we compare two classification schemes on the ADNI MRI dataset: a
very simple logistic regression model that uses manually selected volumetric
features as inputs, and a convolutional neural network trained on 3D MRI data.
We assess the robustness of the trained models in the face of varying dataset
splits, training set sex composition, and stage of disease. In contrast to
earlier work on diagnosing lung diseases based on chest x-ray data, we do not
find a strong dependence of model performance for male and female test subjects
on the sex composition of the training dataset. Moreover, in our analysis, the
low-dimensional model with manually selected features outperforms the 3D CNN,
thus emphasizing the need for automatic robust feature extraction methods and
the value of manual feature specification (based on prior knowledge) for
robustness.


title:
Experimental quantum adversarial learning with programmable
  superconducting qubits

abstract:
  Quantum computing promises to enhance machine learning and artificial
intelligence. Different quantum algorithms have been proposed to improve a wide
spectrum of machine learning tasks. Yet, recent theoretical works show that,
similar to traditional classifiers based on deep classical neural networks,
quantum classifiers would suffer from the vulnerability problem: adding tiny
carefully-crafted perturbations to the legitimate original data samples would
facilitate incorrect predictions at a notably high confidence level. This will
pose serious problems for future quantum machine learning applications in
safety and security-critical scenarios. Here, we report the first experimental
demonstration of quantum adversarial learning with programmable superconducting
qubits. We train quantum classifiers, which are built upon variational quantum
circuits consisting of ten transmon qubits featuring average lifetimes of 150
$\mu$s, and average fidelities of simultaneous single- and two-qubit gates
above 99.94% and 99.4% respectively, with both real-life images (e.g., medical
magnetic resonance imaging scans) and quantum data. We demonstrate that these
well-trained classifiers (with testing accuracy up to 99%) can be practically
deceived by small adversarial perturbations, whereas an adversarial training
process would significantly enhance their robustness to such perturbations. Our
results reveal experimentally a crucial vulnerability aspect of quantum
learning systems under adversarial scenarios and demonstrate an effective
defense strategy against adversarial attacks, which provide a valuable guide
for quantum artificial intelligence applications with both near-term and future
quantum devices.


title: Suppression of Higgs Mixing by Quantum Zeno Effect

abstract:
  The Higgs portal interaction to a singlet sector of the standard model (SM)
gauge group is widely-studied. In this Letter, we show that a quantum effect is
important if the Higgs field mixes with another singlet scalar field whose
decay rate is larger than the mass difference between the two mass eigenstates.
This effect may be interpreted as the quantum Zeno effect. In either the
quantum mechanics or the quantum field theory, we show that the resulting
propagating mode is not the eigenstate of the mass matrix, but it is
approximately the eigenstate of the interaction. As a consequence, the
decoupling of the mixing effect happens at the infinity limit of the decay
width of the exotic scalar even if the na\"{i}ve mixing parameter is not small.
With a finite decay width of the exotic scalar, we derive the effective mass of
the propagating mode in the SM sector, its decay rate, and the couplings at the
1-loop level. It turns out that the mixed mass eigenstates can mimic the
discovered 125 GeV Higgs boson. This fuzzy Higgs boson can be obtained in a
simple perturbative renormalizable model. It is consistent with the 125 GeV SM
Higgs boson when the mass difference is smaller than ${\cal O}(0.1)$GeV (${\cal
O}(1)$GeV) for ${\cal O}(1)$ (${\cal O}(0.01)$) mixing. We argue the possible
natural scenario for the tiny mass splitting and the possibility that the upper
bound of the mass difference is larger for a strongly-coupled singlet sector.
To probe the fuzzy Higgs boson scenario, it is difficult to directly produce
the singlet sector particles. Nevertheless, the future Higgs factories may
probe this scenario by precisely measuring the Higgs boson invisible decay rate
and the deviation of the Higgs coupling. Applications of the mechanism are also
mentioned.