title:
Optimizing the Long-Term Behaviour of Deep Reinforcement Learning for
  Pushing and Grasping

abstract:
  We investigate the "Visual Pushing for Grasping" (VPG) system by Zeng et al.
and the "Hourglass" system by Ewerton et al., an evolution of the former. The
focus of our work is the investigation of the capabilities of both systems to
learn long-term rewards and policies. Zeng et al. original task only needs a
limited amount of foresight. Ewerton et al. attain their best performance using
an agent which only takes the most immediate action under consideration. We are
interested in the ability of their models and training algorithms to accurately
predict long-term Q-Values. To evaluate this ability, we design a new bin
sorting task and reward function. Our task requires agents to accurately
estimate future rewards and therefore use high discount factors in their
Q-Value calculation. We investigate the behaviour of an adaptation of the VPG
training algorithm on our task. We show that this adaptation can not accurately
predict the required long-term action sequences. In addition to the limitations
identified by Ewerton et al., it suffers from the known Deep Q-Learning problem
of overestimated Q-Values. In an effort to solve our task, we turn to the
Hourglass models and combine them with the Double Q-Learning approach. We show
that this approach enables the models to accurately predict long-term action
sequences when trained with large discount factors. Our results show that the
Double Q-Learning technique is essential for training with very high discount
factors, as the models Q-Value predictions diverge otherwise. We also
experiment with different approaches for discount factor scheduling, loss
calculation and exploration procedures. Our results show that the latter
factors do not visibly influence the model's performance for our task.


title:
Noncontact measurement method of linear and angular displacement based
  on dual-beam feedback interferometric system

abstract:
  This study describes a unique optical approach for the noncontact measurement
of linear and angular displacement. Compared to previous methods, the sensor
system here based on the dual-beam phase-modulated feedback interferometry
provides higher sensitivity for non-cooperative targets and a wider range
concerning the angle measurement. The amount of linear and angular displacement
is calculated by tracing the phase changes of the differential beams.
Performance of the proposed method is evaluated via testing a prototype system.
The prototype has a 35 nm and 0.15" stability over 1 hour, with a resolution of
1 nm and 0.02" correspondingly, according to the experimental data. The
linearity is 5.58*10^{-6} in the range of 100 mm and 1.34*10^{-4} in the range
of 360{\deg}, indicating that the proposed method may possess considerable
potential for high-precision metrological applications.


title: Position-based Prompting for Health Outcome Generation

abstract:
  Probing Pre-trained Language Models (PLMs) using prompts has indirectly
implied that language models (LMs) can be treated as knowledge bases. To this
end, this phenomena has been effective especially when these LMs are fine-tuned
towards not just data of a specific domain, but also to the style or linguistic
pattern of the prompts themselves. We observe that, satisfying a particular
linguistic pattern in prompts is an unsustainable constraint that unnecessarily
lengthens the probing task, especially because, they are often manually
designed and the range of possible prompt template patterns can vary depending
on the prompting objective and domain. We therefore explore an idea of using a
position-attention mechanism to capture positional information of each word in
a prompt relative to the mask to be filled, hence avoiding the need to
re-construct prompts when the prompts linguistic pattern changes. Using our
approach, we demonstrate the ability of eliciting answers to rare prompt
templates (in a case study on health outcome generation) such as Postfix and
Mixed patterns whose missing information is respectively at the start and in
multiple random places of the prompt. More so, using various biomedical PLMs,
our approach consistently outperforms a baseline in which the default mask
language model (MLM) representation is used to predict masked tokens.


title:
Fast electrons interacting with chiral matter: mirror symmetry breaking
  of quantum decoherence and lateral momentum transfer

abstract:
  Photons experience mirror asymmetry of macroscopic chiral media, as in
circular dichroism and polarization rotation, since left and right handed
circular polarizations differently couple with matter handedness. Conversely,
free relativistic electrons with vanishing orbital angular momentum have no
handedness so the question arises whether they could sense chirality of
geometrically symmetric macroscopic samples. In this Letter, we show that
matter chirality breaks mirror symmetry of the scattered electrons quantum
decoherence, even when the incident electron wave function and the sample shape
have a common reflection symmetry plane. This is physically possible since the
wave function transverse smearing triggers electron sensitivity to the spatial
asymmetry of the electromagnetic interaction with the sample, as results from
our non-perturbative analysis of the scattered electron reduced density matrix,
in the framework of macroscopic quantum electrodynamics. Furthermore, we prove
that mirror asymmetry also shows up in the distribution of the electron lateral
momentum, orthogonal to the geometric symmetry plane, whose non-vanishing mean
value reveals that the electron experiences a lateral mechanical interaction
entirely produced by matter chirality.


title:
Novel Increase of Superconducting Critical Temperature of an
  Iron-Superconductor due to Ion Implantation

abstract:
  Energetic ion irradiation usually decreases superconducting critical
temperature(Tc), with the few exceptions involving increases up to a few K
only. However, our recent 2.5X10^15 Ar/cm2 irradiations by 1.5 MeV Ar6+
enhanced Tc of the single crystal Fe-superconductor Ba(Fe0.943Co0.057)2As2 by
8.2 K from its initial onset Tc of ~16.9 K as measured from the real part of
the magnetic susceptibility, matching measurements from the imaginary part,
electrical resistivity and magnetization. Ozaki et al. (2016) explained their
Tc increase of 0.5 K in FeSe0.5Te0.5 films with the thickness (t) < the
irradiating proton range (R), as due to a nanoscale compressive strain
developed from radiation damage of the lattice. Here, Ar irradiation with t > R
results in an Ar implanted layer in our crystal. Implanted inert gas atoms
often agglomerate into high-pressure bubbles to exert a large compressive
strain on the lattice. We suggest that this additional compressive strain could
be the reason for such a large (~49%) Tc increase.


title:
Formation of black holes in the pair-instability mass gap: evolution of
  a post-collision star

abstract:
  The detection of GW190521 by the LIGO-Virgo collaboration revealed the
existence of black holes (BHs) in the pair-instability (PI) mass gap. Here, we
investigate the formation of BHs in the PI mass gap via star -- star collisions
in young stellar clusters. To avoid PI, the stellar-collision product must have
a relatively small core and a massive envelope. We generate our initial
conditions from the outputs of a hydro-dynamical simulation of the collision
between a core helium burning star ($\sim 58$ M$_\odot$) and a main-sequence
star ($\sim 42$ M$_\odot$). The hydro-dynamical simulation allows us to take
into account the mass lost during the collision ($\sim 12$ M$_\odot$) and to
build the chemical composition profile of the post-collision star. We then
evolve the collision product with the stellar evolution codes PARSEC and MESA.
We find that the post-collision star evolves through all the stellar burning
phases until core collapse, avoiding PI. At the onset of core collapse, the
post-collision product is a blue super-giant star. We estimate a total mass
loss of about 1 M$_\odot$ during the post-collision evolution, due to stellar
winds and shocks induced by neutrino emission in a failed supernova. The final
BH mass is $\approx{87}$ M$_\odot$. Therefore, we confirm that the collision
scenario is a suitable formation channel to populate the PI mass gap.


title:
Formation of black holes in the pair-instability mass gap:
  Hydrodynamical simulation of a massive star collision

abstract:
  The detection of GW190521 by the LIGO-Virgo collaboration proved the
existence of black holes in the theoretically predicted pair-instability gap of
their mass spectrum. Some recent studies suggest that such massive black holes
could be produced by the collision of an evolved star with a carbon-oxygen core
and a main sequence star. Such a post-coalescence star could end its life
avoiding the pair-instability regime and with a direct collapse of its very
massive envelope. It is still not clear, however, how the collision shapes the
structure of the newly produced star and how much mass is actually lost in the
impact. We investigated this issue by means of hydrodynamical simulations with
the smoothed particle hydrodynamics code StarSmasher, finding that the
collision can remove up to 12% of the initial mass of the colliding stars. This
is a non-negligible percentage of the initial mass and could affect the further
evolution of the stellar remnant, particularly in terms of the final mass of a
possibly forming black hole, if the core avoids the pair-instability regime. We
also found that the main sequence star can plunge down to the outer boundary of
the carbon-oxygen core of the primary, changing the inner chemical composition
of the remnant. The collision expels the outer layers of the primary, leaving a
remnant with an helium-enriched envelope (reaching He fractions of about 0.4 at
the surface). These more complex abundance profiles can be directly used in
stellar evolution simulations of the collision product.


title: Deep Understanding based Multi-Document Machine Reading Comprehension

abstract:
  Most existing multi-document machine reading comprehension models mainly
focus on understanding the interactions between the input question and
documents, but ignore following two kinds of understandings. First, to
understand the semantic meaning of words in the input question and documents
from the perspective of each other. Second, to understand the supporting cues
for a correct answer from the perspective of intra-document and
inter-documents. Ignoring these two kinds of important understandings would
make the models oversee some important information that may be helpful for
inding correct answers. To overcome this deiciency, we propose a deep
understanding based model for multi-document machine reading comprehension. It
has three cascaded deep understanding modules which are designed to understand
the accurate semantic meaning of words, the interactions between the input
question and documents, and the supporting cues for the correct answer. We
evaluate our model on two large scale benchmark datasets, namely TriviaQA Web
and DuReader. Extensive experiments show that our model achieves
state-of-the-art results on both datasets.


title: Covariance matrix preparation for quantum principal component analysis

abstract:
  Principal component analysis (PCA) is a dimensionality reduction method in
data analysis that involves diagonalizing the covariance matrix of the dataset.
Recently, quantum algorithms have been formulated for PCA based on
diagonalizing a density matrix. These algorithms assume that the covariance
matrix can be encoded in a density matrix, but a concrete protocol for this
encoding has been lacking. Our work aims to address this gap. Assuming
amplitude encoding of the data, with the data given by the ensemble $\{p_i,|
\psi_i \rangle\}$, then one can easily prepare the ensemble average density
matrix $\overline{\rho} = \sum_i p_i |\psi_i\rangle \langle \psi_i |$. We first
show that $\overline{\rho}$ is precisely the covariance matrix whenever the
dataset is centered. For quantum datasets, we exploit global phase symmetry to
argue that there always exists a centered dataset consistent with
$\overline{\rho}$, and hence $\overline{\rho}$ can always be interpreted as a
covariance matrix. This provides a simple means for preparing the covariance
matrix for arbitrary quantum datasets or centered classical datasets. For
uncentered classical datasets, our method is so-called "PCA without centering",
which we interpret as PCA on a symmetrized dataset. We argue that this closely
corresponds to standard PCA, and we derive equations and inequalities that
bound the deviation of the spectrum obtained with our method from that of
standard PCA. We numerically illustrate our method for the MNIST handwritten
digit dataset. We also argue that PCA on quantum datasets is natural and
meaningful, and we numerically implement our method for molecular ground-state
datasets.


title:
Superconductivity in multiorbital systems with repulsive interactions:
  Hund's pairing vs. spin-fluctuation pairing

abstract:
  Hund's pairing refers to Cooper pairing generated by onsite interactions that
become attractive due to large Hund's exchange $J$. This is possible in
multiorbital systems even when all local bare interactions are repulsive, since
attraction in specific channels are given by certain linear combinations of
interaction parameters. On the other hand, pairing processes such as the
exchange of spin fluctuations, are also present. We compare these two
mechanisms on an equal footing using electronic bands appropriate for different
classes of multiorbital systems over a wide range of interaction parameters. We
find that for systems without clear nesting features, the superconducting state
generated by the Hund's mechanism agrees well with that from the full
fluctuation exchange vertex when Hund's exchange and spin-orbit coupling are
sufficiently large. On the other hand, for systems characterized by a peaked
finite-momentum particle-hole susceptibility, spin-fluctuation pairing
generally dominates over Hund's pairing. We conclude that Hund's pairing states
are unlikely to be realized in systems like Sr$_2$RuO$_4$ and generic
iron-based superconductors.


title:
Generalised Latent Assimilation in Heterogeneous Reduced Spaces with
  Machine Learning Surrogate Models

abstract:
  Reduced-order modelling and low-dimensional surrogate models generated using
machine learning algorithms have been widely applied in high-dimensional
dynamical systems to improve the algorithmic efficiency. In this paper, we
develop a system which combines reduced-order surrogate models with a novel
data assimilation (DA) technique used to incorporate real-time observations
from different physical spaces. We make use of local smooth surrogate functions
which link the space of encoded system variables and the one of current
observations to perform variational DA with a low computational cost. The new
system, named Generalised Latent Assimilation can benefit both the efficiency
provided by the reduced-order modelling and the accuracy of data assimilation.
A theoretical analysis of the difference between surrogate and original
assimilation cost function is also provided in this paper where an upper bound,
depending on the size of the local training set, is given. The new approach is
tested on a high-dimensional CFD application of a two-phase liquid flow with
non-linear observation operators that current Latent Assimilation methods can
not handle. Numerical results demonstrate that the proposed assimilation
approach can significantly improve the reconstruction and prediction accuracy
of the deep learning surrogate model which is nearly 1000 times faster than the
CFD simulation.


title: On the Effectiveness of Pretrained Models for API Learning

abstract:
  Developers frequently use APIs to implement certain functionalities, such as
parsing Excel Files, reading and writing text files line by line, etc.
Developers can greatly benefit from automatic API usage sequence generation
based on natural language queries for building applications in a faster and
cleaner manner. Existing approaches utilize information retrieval models to
search for matching API sequences given a query or use RNN-based
encoder-decoder to generate API sequences. As it stands, the first approach
treats queries and API names as bags of words. It lacks deep comprehension of
the semantics of the queries. The latter approach adapts a neural language
model to encode a user query into a fixed-length context vector and generate
API sequences from the context vector.
  We want to understand the effectiveness of recent Pre-trained Transformer
based Models (PTMs) for the API learning task. These PTMs are trained on large
natural language corpora in an unsupervised manner to retain contextual
knowledge about the language and have found success in solving similar Natural
Language Processing (NLP) problems. However, the applicability of PTMs has not
yet been explored for the API sequence generation task. We use a dataset that
contains 7 million annotations collected from GitHub to evaluate the PTMs
empirically. This dataset was also used to assess previous approaches. Based on
our results, PTMs generate more accurate API sequences and outperform other
related methods by around 11%. We have also identified two different
tokenization approaches that can contribute to a significant boost in PTMs'
performance for the API sequence generation task.


title:
Unsignalized Intersection Management Strategy for Mixed Autonomy Traffic
  Streams

abstract:
  With the rapid development of connected and automated vehicles (CAVs) and
intelligent transportation infrastructure, CAVs and connected human-driven
vehicles (CHVs) will coexist on the roads in the future for a long time. This
paper comprehensively considers the different traffic characteristics of CHVs
and CAVs, and systemically investigates the unsignalized intersection
management strategy from upper decision-making level to lower execution level.
Combined with the designed vehicle planning and control algorithm, the
unsignalized intersection management strategy consists of two parts: the
heuristic priority queues based right of way allocation (HPQ) algorithm, and
the vehicle planning and control algorithm. In the HPQ algorithm, a vehicle
priority management model considering the difference between CAVs and CHVs is
built to design the right of way management for CAVs and CHVs, respectively. In
the lower level for vehicle planning and control algorithm, different control
modes of CAVs are designed according to the upper level decision made by the
HPQ algorithm. Moreover, the vehicle control execution is realized by the model
predictive controller combined with the geographical environment constraints
and the unsignalized intersection management strategy. The proposed strategy is
evaluated by simulations, which show that the proposed intersection management
strategy can effectively reduce travel time and improve traffic efficiency. The
intersection management strategy captures the real-world balance between
efficiency and safety for potential future intelligent traffic systems.


title:
Multi-Task Distributed Learning using Vision Transformer with Random
  Patch Permutation

abstract:
  The widespread application of artificial intelligence in health research is
currently hampered by limitations in data availability. Distributed learning
methods such as federated learning (FL) and shared learning (SL) are introduced
to solve this problem as well as data management and ownership issues with
their different strengths and weaknesses. The recent proposal of federated
split task-agnostic (FeSTA) learning tries to reconcile the distinct merits of
FL and SL by enabling the multi-task collaboration between participants through
Vision Transformer (ViT) architecture, but they suffer from higher
communication overhead. To address this, here we present a multi-task
distributed learning using ViT with random patch permutation. Instead of using
a CNN based head as in FeSTA, p-FeSTA adopts a randomly permuting simple patch
embedder, improving the multi-task learning performance without sacrificing
privacy. Experimental results confirm that the proposed method significantly
enhances the benefit of multi-task collaboration, communication efficiency, and
privacy preservation, shedding light on practical multi-task distributed
learning in the field of medical imaging.


title:
Tidally driven tectonic activity as a parameter in exoplanet
  habitability

abstract:
  Aims. On Earth, plate tectonics play an integral role in driving the
long-term carbon cycle; however, on tidally locked rocky exoplanets alternative
tectonic mechanisms driven by tidal stress and tidal heating could serve in an
analogous way. Methods. We calculate tidal stress and tidal heating rates to
model the likelihood of tectonic activity maintaining stable climates suitable
for surface liquid water on tidally locked rocky exoplanets with radii
${R}_{p}$ $\le$ 1.23R$_\oplus$. Results. Applying the tidal models to our
sample of 767 tidally locked rocky exoplanets reveals that $\sim$10% of
exoplanets, including Proxima Cen b and GJ 1061 d from the circumstellar
habitable zone (CHZ), pass the tidal stress subduction threshold for mobile lid
tectonic activity and reside within the optimal tidal heating zone. This subset
of exoplanets could sustain tidally induced temperate mobile lid tectonic
activity comparable to plate tectonics on Earth, aiding in maintaining the
presence of surface liquid water. Furthermore, $\sim$40% of exoplanets from our
sample located in the CHZ would be unable to maintain the tectonic activity
needed to stabilise the climate and are unlikely to retain surface liquid
water. When broadening our modelling to establish the overlap between tidal
stress, tidal heating, and the CHZ, to discover optimal regions to target for
future observations, we determine that tidally driven tectonic activity
conducive to the maintenance of surface liquid water occurs predominantly
around M dwarfs, and identify intersections, where both mobile lid and optimal
tidal heating could be sustained on eccentric (e>0.1) Earth-sized exoplanets
(${R}_{p}$ = 1.0-1.23R$_\oplus$) orbiting in the CHZ of low-mass M dwarfs.


title:
A Hard and Soft Hybrid Slicing Framework for Service Level Agreement
  Guarantee via Deep Reinforcement Learning

abstract:
  Network slicing is a critical driver for guaranteeing the diverse service
level agreements (SLA) in 5G and future networks. Recently, deep reinforcement
learning (DRL) has been widely utilized for resource allocation in network
slicing. However, existing related works do not consider the performance loss
associated with the initial exploration phase of DRL. This paper proposes a new
performance-guaranteed slicing strategy with a soft and hard hybrid slicing
setting. Mainly, a common slice setting is applied to guarantee slices' SLA
when training the neural network. Moreover, the resource of the common slice
tends to precisely redistribute to slices with the training of DRL until it
converges. Furthermore, experiment results confirm the effectiveness of our
proposed slicing framework: the slices' SLA of the training phase can be
guaranteed, and the proposed algorithm can achieve the near-optimal performance
in terms of the SLA satisfaction ratio, isolation degree and spectrum
maximization after convergence.


title:
Survey on Automated Short Answer Grading with Deep Learning: from Word
  Embeddings to Transformers

abstract:
  Automated short answer grading (ASAG) has gained attention in education as a
means to scale educational tasks to the growing number of students. Recent
progress in Natural Language Processing and Machine Learning has largely
influenced the field of ASAG, of which we survey the recent research
advancements. We complement previous surveys by providing a comprehensive
analysis of recently published methods that deploy deep learning approaches. In
particular, we focus our analysis on the transition from hand engineered
features to representation learning approaches, which learn representative
features for the task at hand automatically from large corpora of data. We
structure our analysis of deep learning methods along three categories: word
embeddings, sequential models, and attention-based methods. Deep learning
impacted ASAG differently than other fields of NLP, as we noticed that the
learned representations alone do not contribute to achieve the best results,
but they rather show to work in a complementary way with hand-engineered
features. The best performance are indeed achieved by methods that combine the
carefully hand-engineered features with the power of the semantic descriptions
provided by the latest models, like transformers architectures. We identify
challenges and provide an outlook on research direction that can be addressed
in the future


title:
AI-aided Traffic Control Scheme for M2M Communications in the Internet
  of Vehicles

abstract:
  Due to the rapid growth of data transmissions in internet of vehicles (IoV),
finding schemes that can effectively alleviate access congestion has become an
important issue. Recently, many traffic control schemes have been studied.
Nevertheless, the dynamics of traffic and the heterogeneous requirements of
different IoV applications are not considered in most existing studies, which
is significant for the random access resource allocation. In this paper, we
consider a hybrid traffic control scheme and use proximal policy optimization
(PPO) method to tackle it. Firstly, IoV devices are divided into various
classes based on delay characteristics. The target of maximizing the successful
transmission of packets with the success rate constraint is established. Then,
the optimization objective is transformed into a markov decision process (MDP)
model. Finally, the access class barring (ACB) factors are obtained based on
the PPO method to maximize the number of successful access devices. The
performance of the proposal algorithm in respect of successful events and delay
compared to existing schemes is verified by simulations.


title: Integrating Rankings into Quantized Scores in Peer Review

abstract:
  In peer review, reviewers are usually asked to provide scores for the papers.
The scores are then used by Area Chairs or Program Chairs in various ways in
the decision-making process. The scores are usually elicited in a quantized
form to accommodate the limited cognitive ability of humans to describe their
opinions in numerical values. It has been found that the quantized scores
suffer from a large number of ties, thereby leading to a significant loss of
information. To mitigate this issue, conferences have started to ask reviewers
to additionally provide a ranking of the papers they have reviewed. There are
however two key challenges. First, there is no standard procedure for using
this ranking information and Area Chairs may use it in different ways
(including simply ignoring them), thereby leading to arbitrariness in the
peer-review process. Second, there are no suitable interfaces for judicious use
of this data nor methods to incorporate it in existing workflows, thereby
leading to inefficiencies. We take a principled approach to integrate the
ranking information into the scores. The output of our method is an updated
score pertaining to each review that also incorporates the rankings. Our
approach addresses the two aforementioned challenges by: (i) ensuring that
rankings are incorporated into the updates scores in the same manner for all
papers, thereby mitigating arbitrariness, and (ii) allowing to seamlessly use
existing interfaces and workflows designed for scores. We empirically evaluate
our method on synthetic datasets as well as on peer reviews from the ICLR 2017
conference, and find that it reduces the error by approximately 30% as compared
to the best performing baseline on the ICLR 2017 data.


title:
QCRI's COVID-19 Disinformation Detector: A System to Fight the COVID-19
  Infodemic in Social Media

abstract:
  Fighting the ongoing COVID-19 infodemic has been declared as one of the most
important focus areas by the World Health Organization since the onset of the
COVID-19 pandemic. While the information that is consumed and disseminated
consists of promoting fake cures, rumors, and conspiracy theories to spreading
xenophobia and panic, at the same time there is information (e.g., containing
advice, promoting cure) that can help different stakeholders such as
policy-makers. Social media platforms enable the infodemic and there has been
an effort to curate the content on such platforms, analyze and debunk them.
While a majority of the research efforts consider one or two aspects (e.g.,
detecting factuality) of such information, in this study we focus on a
multifaceted approach, including an
API,\url{https://app.swaggerhub.com/apis/yifan2019/Tanbih/0.8.0/} and a demo
system,\url{https://covid19.tanbih.org}, which we made freely and publicly
available. We believe that this will facilitate researchers and different
stakeholders. A screencast of the API services and demo is
available.\url{https://youtu.be/zhbcSvxEKMk}


title: Reliable Transiently-Powered Communication

abstract:
  Frequent power failures can introduce significant packet losses during
communication among energy harvesting batteryless wireless sensors. Nodes
should be aware of the energy level of their neighbors to guarantee the success
of communication and avoid wasting energy. This paper presents TRAP
(TRAnsiently-powered Protocol) that allows nodes to communicate only if the
energy availability on both sides of the communication channel is sufficient
before packet transmission. TRAP relies on a novel modulator circuit, which
operates without microcontroller intervention and transmits the energy status
almost for free over the radiofrequency backscatter channel. Our experimental
results showed that TRAP avoids failed transmissions introduced by the power
failures and ensures reliable intermittent communication among batteryless
sensors.


title:
A Survey of Multi-task Learning in Natural Language Processing:
  Regarding Task Relatedness and Training Methods

abstract:
  Multi-task learning (MTL) has become increasingly popular in natural language
processing (NLP) because it improves the performance of related tasks by
exploiting their commonalities and differences. Nevertheless, it is still not
understood very well how multi-task learning can be implemented based on the
relatedness of training tasks. In this survey, we review recent advances of
multi-task learning methods in NLP, with the aim of summarizing them into two
general multi-task training methods based on their task relatedness: (i) joint
training and (ii) multi-step training. We present examples in various NLP
downstream applications, summarize the task relationships and discuss future
directions of this promising topic.


title:
Consequences of a minimal length in a pseudo-complex extension of
  General Relativity

abstract:
  The effects of a minimal length are investigated within an algebraically
extended theory of General Relativity (GR). Former attempts, to include a
minimal length in GR are first resumed, with a conformal factor of the metric
as a consequence. Effective potentials for various black hole masses (as ratios
to the minimal length) are deduced. It is found that the existence of a minimal
length has, for a small mass black hole, important effects on the effective
potential near the event horizon, creating barriers which inhibit that
particles can pass the event horizon. Further, a new limit for the minimal mass
of a black hole is derive


title: A Fermionic Portal to Vector Dark Matter from a New Gauge Sector

abstract:
  We present a class of new physics scenarios wherein the Standard Model (SM)
is extended with a new $SU(2)_D$ dark gauge sector. These frameworks typically
include a $\mathbb{Z}_2$ parity under which states transforming non-trivially
under $SU(2)_D$ have different parity assignments. After spontaneous breaking
of the $SU(2)_D$ symmetry via a new scalar doublet, the ensuing massive vector
bosons with non-zero D-isospin are Dark Matter (DM) candidates.
Non-renormalisable kinetic mixing in the gauge sector or a Higgs portal are not
required to connect the dark sector to the SM. We introduce instead a
Vector-Like (VL) fermion doublet of $SU(2)_D$, the members of which are
singlets of the SM Electro-Weak (EW) gauge group, which mediates the
interactions between the dark sector and the SM itself. This class of Fermion
Portal Vector DM (FPVDM) allows multiple realisations, depending on the
properties of the the VL partner and the scalar potential. As a practical
example, here, we discuss in detail a realisation involving a VL top partner
assuming no mixing between the two scalars of the theory. We then provide
bounds from collider and astroparticle observables. The origin of the
$\mathbb{Z}_2$ symmetry in this class of models can be justified in the form of
a dark EW sector or through an underlying composite structure. The new class of
FPVDM models we suggest here has a large number of phenomenological
applications depending on the mediator properties and can be used for collider
and non-collider studies alike, some of which we outline here for future
studies.


title:
Interval Bound Propagation$\unicode{x2013}$aided
  Few$\unicode{x002d}$shot Learning

abstract:
  Few-shot learning aims to transfer the knowledge acquired from training on a
diverse set of tasks, from a given task distribution, to generalize to unseen
tasks, from the same distribution, with a limited amount of labeled data. The
underlying requirement for effective few-shot generalization is to learn a good
representation of the task manifold. One way to encourage this is to preserve
local neighborhoods in the feature space learned by the few-shot learner. To
this end, we introduce the notion of interval bounds from the provably robust
training literature to few-shot learning. The interval bounds are used to
characterize neighborhoods around the training tasks. These neighborhoods can
then be preserved by minimizing the distance between a task and its respective
bounds. We further introduce a novel strategy to artificially form new tasks
for training by interpolating between the available tasks and their respective
interval bounds, to aid in cases with a scarcity of tasks. We apply our
framework to both model-agnostic meta-learning as well as prototype-based
metric-learning paradigms. The efficacy of our proposed approach is evident
from the improved performance on several datasets from diverse domains in
comparison to a sizable number of recent competitors.


title:
Forecasting lifetime and performance of a novel NVM last-level cache
  with compression

abstract:
  Non-volatile memory (NVM) technologies are interesting alternatives for
building the on-chip Last-Level Cache (LLC). Their advantages, compared to SRAM
memory, are higher density and lower static power, but each write operation
slightly wears out the bitcell, to the point of losing its storage capacity. In
this context, this paper proposes a novel NV-LLC organization leveraging data
compression. Data compression reduces the size of the blocks and, together with
wear-leveling mechanisms, can defer the degradation of such a NV-LLC. Moreover,
as capacity is reduced by write wear, data compression enables degraded cache
frames to allocate blocks whose compressed size is adequate.
  From a methodological point of view, although different approaches are used
in the literature to analyze the degradation of a NV-LLC, none of them allows
to study in detail its temporal evolution. In this sense, this work proposes a
forecasting procedure that combines detailed simulation and prediction,
enabling an accurate analysis of different cache content mechanisms
(replacement, wear leveling, compression, etc.) on the temporal evolution of
the indices of interest, such as the effective capacity of the NV-LLC or the
system IPC.
  The proposed NV-LLC organization has a small added cost compared to that of a
baseline NV-LLC without compression in terms of area, latency and energy
consumption, and increases more than 6 times the time a NV-LLC takes to reach
50% effective capacity.


title: Many-to-many Splatting for Efficient Video Frame Interpolation

abstract:
  Motion-based video frame interpolation commonly relies on optical flow to
warp pixels from the inputs to the desired interpolation instant. Yet due to
the inherent challenges of motion estimation (e.g. occlusions and
discontinuities), most state-of-the-art interpolation approaches require
subsequent refinement of the warped result to generate satisfying outputs,
which drastically decreases the efficiency for multi-frame interpolation. In
this work, we propose a fully differentiable Many-to-Many (M2M) splatting
framework to interpolate frames efficiently. Specifically, given a frame pair,
we estimate multiple bidirectional flows to directly forward warp the pixels to
the desired time step, and then fuse any overlapping pixels. In doing so, each
source pixel renders multiple target pixels and each target pixel can be
synthesized from a larger area of visual context. This establishes a
many-to-many splatting scheme with robustness to artifacts like holes.
Moreover, for each input frame pair, M2M only performs motion estimation once
and has a minuscule computational overhead when interpolating an arbitrary
number of in-between frames, hence achieving fast multi-frame interpolation. We
conducted extensive experiments to analyze M2M, and found that it significantly
improves efficiency while maintaining high effectiveness.


title:
Habitat-Web: Learning Embodied Object-Search Strategies from Human
  Demonstrations at Scale

abstract:
  We present a large-scale study of imitating human demonstrations on tasks
that require a virtual robot to search for objects in new environments -- (1)
ObjectGoal Navigation (e.g. 'find & go to a chair') and (2) Pick&Place (e.g.
'find mug, pick mug, find counter, place mug on counter'). First, we develop a
virtual teleoperation data-collection infrastructure -- connecting Habitat
simulator running in a web browser to Amazon Mechanical Turk, allowing remote
users to teleoperate virtual robots, safely and at scale. We collect 80k
demonstrations for ObjectNav and 12k demonstrations for Pick&Place, which is an
order of magnitude larger than existing human demonstration datasets in
simulation or on real robots.
  Second, we attempt to answer the question -- how does large-scale imitation
learning (IL) (which hasn't been hitherto possible) compare to reinforcement
learning (RL) (which is the status quo)? On ObjectNav, we find that IL (with no
bells or whistles) using 70k human demonstrations outperforms RL using 240k
agent-gathered trajectories. The IL-trained agent demonstrates efficient
object-search behavior -- it peeks into rooms, checks corners for small
objects, turns in place to get a panoramic view -- none of these are exhibited
as prominently by the RL agent, and to induce these behaviors via RL would
require tedious reward engineering. Finally, accuracy vs. training data size
plots show promising scaling behavior, suggesting that simply collecting more
demonstrations is likely to advance the state of art further. On Pick&Place,
the comparison is starker -- IL agents achieve ${\sim}$18% success on episodes
with new object-receptacle locations when trained with 9.5k human
demonstrations, while RL agents fail to get beyond 0%. Overall, our work
provides compelling evidence for investing in large-scale imitation learning.
  Project page: https://ram81.github.io/projects/habitat-web.


title:
Origin of Crystallization Suppression in a New Amorphous Molecular
  White-Light-Generating Material

abstract:
  The microscopic structure of a new infrared-driven amorphous
white-light-generating material was explored by X-ray diffraction, EXAFS and
Reverse Monte Carlo simulation. In this material, structural disorder appears
to be prerequisite for this nonlinear optical effect. The results are
consistent with quantum chemical predictions, but it is also found that the
molecular cores are distorted, which is identified as a crystallization
inhibitor. Sulfur atoms thereby form a uniform vibrational network, which may
be responsible for the high capability of the material to absorb infrared
radiation.


title: Distributed Reinforcement Learning for Robot Teams: A Review

abstract:
  Purpose of review: Recent advances in sensing, actuation, and computation
have opened the door to multi-robot systems consisting of hundreds/thousands of
robots, with promising applications to automated manufacturing, disaster
relief, harvesting, last-mile delivery, port/airport operations, or search and
rescue. The community has leveraged model-free multi-agent reinforcement
learning (MARL) to devise efficient, scalable controllers for multi-robot
systems (MRS). This review aims to provide an analysis of the state-of-the-art
in distributed MARL for multi-robot cooperation.
  Recent findings: Decentralized MRS face fundamental challenges, such as
non-stationarity and partial observability. Building upon the "centralized
training, decentralized execution" paradigm, recent MARL approaches include
independent learning, centralized critic, value decomposition, and
communication learning approaches. Cooperative behaviors are demonstrated
through AI benchmarks and fundamental real-world robotic capabilities such as
multi-robot motion/path planning.
  Summary: This survey reports the challenges surrounding decentralized
model-free MARL for multi-robot cooperation and existing classes of approaches.
We present benchmarks and robotic applications along with a discussion on
current open avenues for research.


title:
Finding magnetic north: an extraordinary magnetic field detection in
  Polaris and first results of a magnetic survey of classical Cepheids

abstract:
  Classical Cepheids are essential objects in the study of stellar evolution
and cosmology; however, we know little about their magnetic properties. We
report the detection of Stokes $V$ features interpreted as Zeeman signatures in
four classical Cepheids using high-resolution spectropolarimetric observations
obtained with ESPaDOnS at CFHT. Eight observations of $\eta$ Aql were acquired
in 2017 covering its 7.2 d pulsation period, and single observations of
Polaris, $\zeta$ Gem, $\delta$ Cep and RT Aur were obtained in 2020 as part of
our ongoing systematic survey. We use mean circular polarization Stokes $V$
profiles generated using the Least-Squares Deconvolution procedure to diagnose
Zeeman signatures and measure mean longitudinal field strengths $\langle
B_{z}\rangle$. We detect magnetic signatures across all pulsation phases of
$\eta$ Aql ($-0.89\pm0.47$ G$\,<\langle B_{z}\rangle<1.27\pm 0.40$ G), as well
as in the single observations of Polaris ($0.59\pm0.16$ G), $\zeta$ Gem
($0.41\pm0.16$ G) and $\delta$ Cep ($0.43\pm0.19$ G). The Stokes $V$ profile of
Polaris is detected at extremely high S/N and implies a complex magnetic field
topology. It stands in stark contrast to all other detected Stokes $V$
profiles, which show unusual approximately unipolar positive circular
polarization lobes analogous to those observed in some Am stars.


title:
Validating a Cortisol-Inspired Framework for Human-Robot Interaction
  with a Replication of the Still Face Paradigm

abstract:
  When interacting with others in our everyday life, we prefer the company of
those who share with us the same desire of closeness and intimacy (or lack
thereof), since this determines if our interaction will be more o less
pleasant. This sort of compatibility can be inferred by our innate attachment
style. The attachment style represents our characteristic way of thinking,
feeling and behaving in close relationship, and other than behaviourally, it
can also affect us biologically via our hormonal dynamics. When we are looking
how to enrich human-robot interaction (HRI), one potential solution could be
enabling robots to understand their partners' attachment style, which could
then improve the perception of their partners and help them behave in an
adaptive manner during the interaction. We propose to use the relationship
between the attachment style and the cortisol hormone, to endow the humanoid
robot iCub with an internal cortisol inspired framework that allows it to infer
participant's attachment style by the effect of the interaction on its cortisol
levels (referred to as R-cortisol). In this work, we present our cognitive
framework and its validation during the replication of a well-known paradigm on
hormonal modulation in human-human interaction (HHI) - the Still Face paradigm.


title:
Extremal bounds for Dirichlet polynomials with random multiplicative
  coefficients

abstract:
  For $X(n)$ a Steinhaus random multiplicative function, we study the maximal
size of the random Dirichlet polynomial $$ D_N(t) = \frac1{\sqrt{N}} \sum_{n
\leq N} X(n) n^{it}, $$ with $t$ in various ranges. In particular, for fixed
$C>0$ and any small $\varepsilon>0$ we show that, with high probability, $$
  \exp( (\log N)^{1/2-\varepsilon} ) \ll \sup_{|t| \leq N^C} |D_N(t)| \ll \exp(
(\log N)^{1/2+\varepsilon}). $$


title: Non-Gaussian superradiant transition via three-body ultrastrong coupling

abstract:
  We introduce a class of quantum optical Hamiltonian characterized by
three-body couplings, and propose a circuit-QED scheme based on
state-of-the-art technology that implements the considered model. Unlike
two-body light-matter interactions, this three-body coupling Hamiltonian is
exclusively composed of terms which do not conserve the particle number. We
explore the three-body ultrastrong coupling regime, showing the emergence of a
superradiant phase transition which is of first order, is characterized by the
breaking of a $\mathbb{Z}_2\times \mathbb{Z}_2$ symmetry and has a strongly
non-Gaussian nature. Indeed, in contrast to what is observed in any
two-body-coupling model, in proximity of the transition the ground state
exhibits a divergent coskewness, i.e., quantum correlations that cannot be
captured within semiclassical and Gaussian approximations. Furthermore, we
demonstrate the robustness of our findings by including dissipative processes
in the model, showing that the steady-state of the system inherits from the
ground states the most prominent features of the transition.


title:
DeepXPalm: Tilt and Position Rendering using Palm-worn Haptic Display
  and CNN-based Tactile Pattern Recognition

abstract:
  Telemanipulation of deformable objects requires high precision and dexterity
from the users, which can be increased by kinesthetic and tactile feedback.
However, the object shape can change dynamically, causing ambiguous perception
of its alignment and hence errors in the robot positioning. Therefore, the tilt
angle and position classification problem has to be solved to present a clear
tactile pattern to the user. This work presents a telemanipulation system for
plastic pipettes consisting of a multi-contact haptic device LinkGlide to
deliver haptic feedback at the users' palm and two tactile sensors array
embedded in the 2-finger Robotiq gripper. We propose a novel approach based on
Convolutional Neural Networks (CNN) to detect the tilt and position while
grasping deformable objects. The CNN generates a mask based on recognized tilt
and position data to render further multi-contact tactile stimuli provided to
the user during the telemanipulation. The study has shown that using the CNN
algorithm and the preset mask, tilt, and position recognition by users is
increased from 9.67% using the direct data to 82.5%.


title:
Global displacement induced by rigid motion simulation during MRI
  acquisition

abstract:
  Purpose: Simulation of motion in the frequency domain during the MRI
acquisition process is an important tool to study the effect of object motion.
It is natural in this context to rely on a voxel-to-voxel difference metric
between the original image and its motion-simulated version to quantify the
alteration. These metrics are very sensitive to any mis-coregistration between
the two images. As we observed global displacements of the corrupted image for
specific simulated motions, we focused this work on the critical choice of a
reference position of the object for the motion simulation that avoids any
global displacement. Methods: We used the motion simulation framework and we
studied the different proposed solutions. Classically, the position around the
k-space center is considered as a reference with two analytical solutions to
correct for global image shifts: demeaning the image in the Fourier domain
either using the exact position at k-space center or using a weighted average
around the center.
  Results: We demonstrated with motion simulated examples that these analytical
solutions did not yield the same results as a direct co-registration method.
Indeed, short transitions around the k-space center did not induce any global
displacement, in contrast with what both analytical solutions assumed. We also
observed a dependence of the global shift to both the motion amplitude and the
translation direction. Conclusion: In the absence of a theoretical solution, we
concluded that an extra co-registration step after motion simulation was needed
in order to compare the simulation with its reference image.


title: The Word Problem is Solvable for 3-free Artin groups

abstract:
  We give an algorithm to solve the word problem for Artin groups that do not
contain any relations of length 3. Furthermore, we prove that, given two
geodesic words representing the same element, one can obtain one from the other
by using a set of homogeneous relations that never increase the word length.


title: Magneto-inertial range dominated by magnetic helicity in space plasmas

abstract:
  Magneto-inertial range dominated by magnetic helicity has been studied using
results of the numerical simulations, laboratory measurements, solar, solar
wind, and Earth's magnetosphere observations (spacecraft measurements). The
spectral data have been compared with the theoretical results obtained on the
basis of the distributed chaos notion in the frames of the
Kolmogorov-Iroshnikov phenomenology. The transition from magnetohydrodynamics
to kinetics in the solar wind and at the special events (reconnection and
Kelvin-Helmholtz instability) in Earth's magnetosphere has been discussed in
this context.


title: Temporal Alignment for History Representation in Reinforcement Learning

abstract:
  Environments in Reinforcement Learning are usually only partially observable.
To address this problem, a possible solution is to provide the agent with
information about the past. However, providing complete observations of
numerous steps can be excessive. Inspired by human memory, we propose to
represent history with only important changes in the environment and, in our
approach, to obtain automatically this representation using self-supervision.
Our method (TempAl) aligns temporally-close frames, revealing a general, slowly
varying state of the environment. This procedure is based on contrastive loss,
which pulls embeddings of nearby observations to each other while pushing away
other samples from the batch. It can be interpreted as a metric that captures
the temporal relations of observations. We propose to combine both common
instantaneous and our history representation and we evaluate TempAl on all
available Atari games from the Arcade Learning Environment. TempAl surpasses
the instantaneous-only baseline in 35 environments out of 49. The source code
of the method and of all the experiments is available at
https://github.com/htdt/tempal.


title: Reconstructing Bayesian Networks on a Quantum Annealer

abstract:
  Bayesian networks are widely used probabilistic graphical models, whose
structure is hard to learn starting from the generated data. O'Gorman et al.
have proposed an algorithm to encode this task, i.e., the Bayesian network
structure learning (BSNL), into a form that can be solved through quantum
annealing, but they have not provided an experimental evaluation of it. In this
paper, we present (i) an implementation in Python of O'Gorman's algorithm, (ii)
a divide et impera approach that allows addressing BNSL problems of larger
sizes in order to overcome the limitations imposed by the current
architectures, and (iii) their empirical evaluation. Specifically, several
problems with an increasing number of variables have been used in the
experiments. The results have shown the effectiveness of O'Gorman's formulation
for BNSL instances of small sizes, and the superiority of the divide et impera
approach on the direct execution of O'Gorman's algorithm.


title: Geometric aspects of Young Integral: decomposition of flows

abstract:
  In this paper we study geometric aspects of dynamics generated by Young
differential equations (YDE) driven by $\alpha$-H\"older trajectories with
$\alpha \in (1/2, 1)$. We present a number of properties and geometrical
constructions on this low regularity context: Young It\^o geometrical formula,
horizontal lift in principal fibre bundles, parallel transport, covariant
derivative, development and anti-development, among others. Our main
application here is a geometrical decomposition of flows generated by YDEs
according to diffeomorphisms generated by complementary distributions
(integrable or not). The proof of existence of this decomposition is based on
an Young It\^o-Kunita formula for $\alpha$-H{\"o}lder paths proved by
Castrequini and Catuogno (Chaos Solitons Fractals, 2022).


title: Visualizing Deep Neural Networks with Topographic Activation Maps

abstract:
  Machine Learning with Deep Neural Networks (DNNs) has become a successful
tool in solving tasks across various fields of application. The success of DNNs
is strongly connected to their high complexity in terms of the number of
network layers or of neurons in each layer, which severely complicates to
understand how DNNs solve their learned task. To improve the explainability of
DNNs, we adapt methods from neuroscience because this field has a rich
experience in analyzing complex and opaque systems. In this work, we draw
inspiration from how neuroscience uses topographic maps to visualize the
activity of the brain when it performs certain tasks. Transferring this
approach to DNNs can help to visualize and understand their internal processes
more intuitively, too. However, the inner structures of brains and DNNs differ
substantially. Therefore, to be able to visualize activations of neurons in
DNNs as topographic maps, we research techniques to layout the neurons in a
two-dimensional space in which neurons of similar activity are in the vicinity
of each other. In this work, we introduce and compare different methods to
obtain a topographic layout of the neurons in a network layer. Moreover, we
demonstrate how to use the resulting topographic activation maps to identify
errors or encoded biases in DNNs or data sets. Our novel visualization
technique improves the transparency of DNN-based algorithmic decision-making
systems and is accessible to a broad audience because topographic maps are
intuitive to interpret without expert-knowledge in Machine Learning.


title:
FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to
  System Heterogeneity

abstract:
  Federated Learning (FL) is an emerging framework for distributed processing
of large data volumes by edge devices subject to limited communication
bandwidths, heterogeneity in data distributions and computational resources, as
well as privacy considerations. In this paper, we introduce a new FL protocol
termed FedADMM based on primal-dual optimization. The proposed method leverages
dual variables to tackle statistical heterogeneity, and accommodates system
heterogeneity by tolerating variable amount of work performed by clients.
FedADMM maintains identical communication costs per round as FedAvg/Prox, and
generalizes them via the augmented Lagrangian. A convergence proof is
established for nonconvex objectives, under no restrictions in terms of data
dissimilarity or number of participants per round of the algorithm. We
demonstrate the merits through extensive experiments on real datasets, under
both IID and non-IID data distributions across clients. FedADMM consistently
outperforms all baseline methods in terms of communication efficiency, with the
number of rounds needed to reach a prescribed accuracy reduced by up to 87%.
The algorithm effectively adapts to heterogeneous data distributions through
the use of dual variables, without the need for hyperparameter tuning, and its
advantages are more pronounced in large-scale systems.


title:
A Monolithic Eulerian Formulation for non-Classical Fluid-Structure
  Interaction (nCFSI): Modeling and Simulation

abstract:
  In this paper a new monolithic Eulerian formulation in the framework of
non-classical continuum is presented for the analysis of fluid-strucuture
interaction problems. In this respect, Cosserat continuum theory taking into
account the micro-rotational degrees of freedom of the particles is considered.
Continuum description of the model and variational formulation of the governing
flow dynamics for non-classical -fluid-structure interaction nCFSI is
presented. The model is analyzed by computing a well known benchmark problem by
Hecht and Pironneau (2017). The algorithmic description is presented and
implemented using FreeFEM++. Code is validated with the benchmark solution of
Turek and Hron (2006) in case of flow around a flag attached with cylinder. New
microstructural behavior of the solution is studied and numerical simulations
and results are shown in the form of figures. Some interesting feature of the
flow is observed and microstructural characteristics are discussed.


title:
Global well-posedness of a three-dimensional
  Brinkman-Forchheimer-B\'enard convection model in porous media

abstract:
  We consider three-dimensional (3D) Boussinesq convection system of an
incompressible fluid in a closed sample of a porous medium. Specifically, we
introduce and analyze a 3D Brinkman-Forchheimer-B\'enard convection problem
describing the behavior of an incompressible fluid in a porous medium between
two plates heated from the bottom and cooled from the top. We show the
existence and uniqueness of global in-time solutions, and the existence of
absorbing balls in $L^2$ and $H^1$. Eventually, we comment on the applicability
of a data assimilation algorithm to our system.


title:
Literature Review on Image Compression, Tracking, Adaptive Training and
  3D Data Transmission

abstract:
  The literature review presented below on Image Compression, Transmission of
3D data over wireless networks and tracking of objects is the in depth study of
Research Papers done in Multimedia lab. Most of the papers presented in this
literature review have tackled the problems present in the conventional system
and offered an optimal and practical solution.


title: Efficient Multiscale Object-based Superpixel Framework

abstract:
  Superpixel segmentation can be used as an intermediary step in many
applications, often to improve object delineation and reduce computer workload.
However, classical methods do not incorporate information about the desired
object. Deep-learning-based approaches consider object information, but their
delineation performance depends on data annotation. Additionally, the
computational time of object-based methods is usually much higher than desired.
In this work, we propose a novel superpixel framework, named Superpixels
through Iterative CLEarcutting (SICLE), which exploits object information being
able to generate a multiscale segmentation on-the-fly. SICLE starts off from
seed oversampling and repeats optimal connectivity-based superpixel delineation
and object-based seed removal until a desired number of superpixels is reached.
It generalizes recent superpixel methods, surpassing them and other
state-of-the-art approaches in efficiency and effectiveness according to
multiple delineation metrics.


title: Voltage-Driven Exchange Resonance Achieving 100% Mechanical Efficiency

abstract:
  Magnetic resonances driven by current-induced torques are crucial tools to
study magnetic materials but are very limited in frequency and mechanical
efficiency. We propose an alternative mechanism, voltage-induced torque, to
realize high efficiency in generating high-frequency magnetization dynamics.
When a ferromagnet (FM)-topological insulator (TI)-FM trilayer heterostructure
is operated as an adiabatic quantum motor, voltage-induced torque arises from
the adiabatic motion of gapped topological electrons on the two interfaces and
act oppositely on the two FM layers, which can excite the exchange mode where
the two FM layers precess with a $\pi$ phase difference. The exchange mode
resonance, bearing a much higher frequency than the ferromagnetic resonance, is
accompanied by topological charge pumping, leading to a sharp peak in
electrical admittance at the resonance point. Because the output current is
purely adiabatic while dissipative current vanishes identically, the proposed
voltage-driven exchange resonance entails a remarkably high mechanical
efficiency close to unity, which is impossible in any current-driven systems.


title:
Practical Issues and Challenges in CSI-based Integrated Sensing and
  Communication

abstract:
  Next-generation mobile communication network (i.e., 6G) has been envisioned
to go beyond classical communication functionality and provide integrated
sensing and communication (ISAC) capability to enable more emerging
applications, such as smart cities, connected vehicles, AIoT and health
care/elder care. Among all the ISAC proposals, the most practical and promising
approach is to empower existing wireless network (e.g., WiFi, 4G/5G) with the
augmented ability to sense the surrounding human and environment, and evolve
wireless communication networks into intelligent communication and sensing
network (e.g., 6G). In this paper, based on our experience on CSI-based
wireless sensing with WiFi/4G/5G signals, we intend to identify ten major
practical and theoretical problems that hinder real deployment of ISAC
applications, and provide possible solutions to those critical challenges.
Hopefully, this work will inspire further research to evolve existing
WiFi/4G/5G networks into next-generation intelligent wireless network (i.e.,
6G).


title: Abstracting Noisy Robot Programs

abstract:
  Abstraction is a commonly used process to represent some low-level system by
a more coarse specification with the goal to omit unnecessary details while
preserving important aspects. While recent work on abstraction in the situation
calculus has focused on non-probabilistic domains, we describe an approach to
abstraction of probabilistic and dynamic systems. Based on a variant of the
situation calculus with probabilistic belief, we define a notion of
bisimulation that allows to abstract a detailed probabilistic basic action
theory with noisy actuators and sensors by a possibly deterministic basic
action theory. By doing so, we obtain abstract Golog programs that omit
unnecessary details and which can be translated back to a detailed program for
actual execution. This simplifies the implementation of noisy robot programs,
opens up the possibility of using deterministic reasoning methods (e.g.,
planning) on probabilistic problems, and provides domain descriptions that are
more easily understandable and explainable.


title:
Introduction to Hamiltonian Formulation of General Relativity and
  Homogeneous Cosmologies

abstract:
  General relativity is traditionally introduced in the covariant (Lagrangian)
formalism. Just like in classical mechanics, there exists an equivalent
description of general relativity using the Hamiltonian approach. The purpose
of the present work is to give a pedagogical introduction to this formalism
where we stick to classical Physics throughout. We build towards this goal by
covering the mathematical pre-requisites as well as the $3+1-$decomposition of
spacetime before discussing the Arnowitt-Deser-Misner (ADM) formalism (a
Hamiltonian approach) of general relativity. Then we proceed to give a brief
but self-contained introduction to homogeneous (but not necessarily isotropic)
universes and discuss the associated Bianchi classification. We first study
their dynamics in the Lagrangian formulation, followed by the Hamiltonian
formulation to show the equivalence of both approaches. In order to get hold of
the ADM formalism, we present a variety of examples: (i) free \& massless
scalar field coupled to homogeneous (in particular, Bianchi IX) universe, (ii)
scalar field with a potential term coupled to Bianchi IX universe, (iii)
electromagnetic field coupled to gravity in general, and (iv) electromagnetic
field coupled to Bianchi IX universe. Through these examples, we also introduce
the associated important concepts \& the dynamics of the system in
consideration using the Hamiltonian approach. We have tried to be detailed and
self-contained in this work but we do expect as a prerequisite to have
familiarity with the basic concepts in general relativity up to the point of
deriving the FRWL cosmological solution from the Einstein field equations.


title: Updating Industrial Robots for Emerging Technologies

abstract:
  Industrial arms need to evolve beyond their standard shape to embrace new and
emerging technologies. In this paper, we shall first perform an analysis of
four popular but different modern industrial robot arms. By seeing the common
trends we will try to extrapolate and expand these trends for the future. Here,
particular focus will be on interaction based on augmented reality (AR) through
head-mounted displays (HMD), but also through smartphones. Long-term
human-robot interaction and personalization of said interaction will also be
considered. The use of AR in human-robot interaction has proven to enhance
communication and information exchange. A basic addition to industrial arm
design would be the integration of QR markers on the robot, both for accessing
information and adding tracking capabilities to more easily display AR
overlays. In a recent example of information access, Mercedes Benz added QR
markers on their cars to help rescue workers estimate the best places to cut
and evacuate people after car crashes. One has also to deal with safety in an
environment that will be more and more about collaboration. The QR markers can
therefore be combined with RF-based ranging modules, developed in the
EU-project SafeLog, that can be used both for safety as well as for tracking of
human positions while in close proximity interactions with the industrial arms.
The industrial arms of the future should also be intuitive to program and
interact with. This would be achieved through AR and head mounted displays as
well as the already mentioned RF-based person tracking. Finally, a more
personalized interaction between the robots and humans can be achieved through
life-long learning AI and disembodied, personalized agents. We propose a design
that not only exists in the physical world, but also partly in the digital
world of mixed reality.


title: Geometrically robust linear optics from non-Abelian geometric phases

abstract:
  We construct a unified operator framework for quantum holonomies generated
from bosonic systems. For a system whose Hamiltonian is bilinear in the
creation and annihilation operators, we find a holonomy group determined only
by a set of selected orthonormal modes obeying a stronger version of the
adiabatic theorem. This photon-number independent description offers deeper
insight as well as a computational advantage when compared to the standard
formalism on geometric phases. In particular, a strong analogy between quantum
holonomies and linear optical networks can be drawn. This relation provides an
explicit recipe how any linear optical quantum computation can be made
geometrically robust in terms of adiabatic or nonadiabatic geometric phases.


title: Identification of radiopure tungsten for low background applications

abstract:
  In this article we explore the availability of radiopure tungsten and its
potential as high density shield material for low background applications. For
compact shield designs, conventionally, lead is used. Metallic tungsten and
tungsten pseudo-alloys reach higher densities up to 19.3 gcm$^{-3}$ and do not
exhibit a significant $^{210}$Pb activity, which is a typical intrinsic
contamination in lead. Within several $\gamma$-ray screening campaigns we were
able to identify tungsten samples with activities similar or better than 1
mBqkg$^{-1}$ in $^{232}$Th, $^{40}$K, $^{60}$Co and the second part of the
$^{238}$U decay chain. In cooperation with a manufacturer we further reduced a
persisting contamination in the first part of the $^{238}$U decay chain by a
factor of $\sim$2.5 down to (305$\pm$30) mBqkg$^{-1}$. With Monte Carlo
simulations, the construction of prototype tungsten-based setups and dedicated
measurements, the shield capability of tungsten in comparison to lead was
extensively studied. Specifically, the impact of cosmogenic radiation on the
background at shallow depth was investigated. We showed that a 6-40% reduction
(depending on the exact shield configuration) in the muon-induced neutron
fluence is achievable by replacing lead with an equivalent amount of tungsten
regarding the suppression of external $\gamma$-radiation. Overall, many
benefits using tungsten especially for low energy applications below a few 100
keV are found. The pseudo-tungsten alloy presented in this work paves the way
for several applications especially regarding background suppression in
particle and astroparticle physics search programs.


title:
End-to-End Zero-Shot HOI Detection via Vision and Language Knowledge
  Distillation

abstract:
  Most existing Human-Object Interaction~(HOI) Detection methods rely heavily
on full annotations with predefined HOI categories, which is limited in
diversity and costly to scale further. We aim at advancing zero-shot HOI
detection to detect both seen and unseen HOIs simultaneously. The fundamental
challenges are to discover potential human-object pairs and identify novel HOI
categories. To overcome the above challenges, we propose a novel end-to-end
zero-shot HOI Detection (EoID) framework via vision-language knowledge
distillation. We first design an Interactive Score module combined with a
Two-stage Bipartite Matching algorithm to achieve interaction distinguishment
for human-object pairs in an action-agnostic manner. Then we transfer the
distribution of action probability from the pretrained vision-language teacher
as well as the seen ground truth to the HOI model to attain zero-shot HOI
classification. Extensive experiments on HICO-Det dataset demonstrate that our
model discovers potential interactive pairs and enables the recognition of
unseen HOIs. Finally, our method outperforms the previous SOTA by 8.92% on
unseen mAP and 10.18% on overall mAP under UA setting, by 6.02% on unseen mAP
and 9.1% on overall mAP under UC setting. Moreover, our method is generalizable
to large-scale object detection data to further scale up the action sets. The
source code will be available at: https://github.com/mrwu-mac/EoID.


title:
Leveraging pre-trained language models for conversational information
  seeking from text

abstract:
  Recent advances in Natural Language Processing, and in particular on the
construction of very large pre-trained language representation models, is
opening up new perspectives on the construction of conversational information
seeking (CIS) systems. In this paper we investigate the usage of in-context
learning and pre-trained language representation models to address the problem
of information extraction from process description documents, in an incremental
question and answering oriented fashion. In particular we investigate the usage
of the native GPT-3 (Generative Pre-trained Transformer 3) model, together with
two in-context learning customizations that inject conceptual definitions and a
limited number of samples in a few shot-learning fashion. The results highlight
the potential of the approach and the usefulness of the in-context learning
customizations, which can substantially contribute to address the "training
data challenge" of deep learning based NLP techniques the BPM field. It also
highlight the challenge posed by control flow relations for which further
training needs to be devised.


title:
The Almost Sure Essential Spectrum of the Doubling Map Model is
  Connected

abstract:
  We consider discrete Schr\"odinger operators on the half line with potentials
generated by the doubling map and continuous sampling functions. We show that
the essential spectrum of these operators is always connected. This result is
obtained by computing the subgroup of the range of the Schwartzman homomorphism
associated with homotopy classes of continuous maps on the suspension of the
standard solenoid that factor through the suspension of the doubling map and
then showing that this subgroup characterizes the topological structure of the
spectrum.


title:
A $C^{m,\omega}$ Whitney Extension Theorem for Horizontal Curves in the
  Heisenberg Group

abstract:
  We characterize which mappings from a compact subset of $\mathbb{R}$ into the
Heisenberg group can be extended to a $C^{m,\omega}$ horizontal curve for a
given modulus of continuity $\omega$. We motivate our characterization by
showing that the $C^{m,\omega}$ extension property fails if we instead use a
more direct analogue of the conditions from the $C^{m}$ case.


title:
Cloudy and Cloud-free Thermal Phase Curves with PICASO: Applications to
  WASP-43b

abstract:
  We present new functionality within PICASO, a state-of-the-art radiative
transfer model for exoplanet and brown dwarf atmospheres, by developing a new
pipeline that computes phase-resolved thermal emission (thermal phase curves)
from three-dimensional (3D) models. Because PICASO is coupled to Virga, an
open-source cloud code, we are able to produce cloudy phase curves with
different sedimentation efficiencies ($f_{sed}$) and cloud condensate species.
We present the first application of this new algorithm to hot Jupiter WASP-43b.
Previous studies of the thermal emission of WASP-43b from Kataria et al. (2015)
found good agreement between cloud-free models and dayside thermal emission,
but an overestimation of the nightside flux, for which clouds have been
suggested as a possible explanation. We use the temperature and vertical wind
structure from cloud-free 3D general circulation models of Kataria et al.
(2015) and post-process it using PICASO, assuming that clouds form and affect
the spectra. We compare our models to results from Kataria et al. (2015),
including the Hubble Space Telescope (HST) Wide-Field Camera 3 (WFC3)
observations of WASP-43b from Stevenson et al. (2014). In addition, we compute
phase curves for Spitzer at 3.6 and 4.5 $\mu m$ and compare them to
observations from Stevenson et al. (2017). We are able to closely recover the
cloud-free results, even though PICASO utilizes a coarse spatial grid. We find
that cloudy phase curves provide much better agreement with the WFC3 and
Spitzer nightside data, while still closely matching the dayside emission. This
work provides the community with a convenient user-friendly tool to interpret
phase-resolved observations of exoplanet atmospheres using 3D models.


title:
Conversational agents for fostering curiosity-driven learning in
  children

abstract:
  Curiosity is an important factor that favors independent and individualized
learning in children. Research suggests that it is also a competence that can
be fostered by training specific metacognitive skills and information-searching
behaviors. In this light, we develop a conversational agent that helps children
generate curiosity-driven questions, and encourages their use to lead
autonomous explorations and gain new knowledge. The study was conducted with 51
primary school students who interacted with either a neutral agent or an
incentive agent that helped curiosity-driven questioning by offering specific
semantic cues. Results showed a significant increase in the number and the
quality of the questions generated with the incentive agent. This interaction
also resulted in longer explorations and stronger learning progress. Together,
our results suggest that the more our agent is able to train children's
curiosity-related metacognitive skills, the better they can maintain their
information-searching behaviors and the more new knowledge they are likely to
acquire.


title:
Evaluating Procedures for Establishing Generative Adversarial
  Network-based Stochastic Image Models in Medical Imaging

abstract:
  Modern generative models, such as generative adversarial networks (GANs),
hold tremendous promise for several areas of medical imaging, such as
unconditional medical image synthesis, image restoration, reconstruction and
translation, and optimization of imaging systems. However, procedures for
establishing stochastic image models (SIMs) using GANs remain generic and do
not address specific issues relevant to medical imaging. In this work,
canonical SIMs that simulate realistic vessels in angiography images are
employed to evaluate procedures for establishing SIMs using GANs. The GAN-based
SIM is compared to the canonical SIM based on its ability to reproduce those
statistics that are meaningful to the particular medically realistic SIM
considered. It is shown that evaluating GANs using classical metrics and
medically relevant metrics may lead to different conclusions about the fidelity
of the trained GANs. This work highlights the need for the development of
objective metrics for evaluating GANs.


title:
Towards Landau-Ginzburg models for cominuscule spaces via the
  exceptional cominuscule family

abstract:
  We present projective Landau-Ginzburg models for the exceptional cominuscule
homogeneous spaces $\mathbb{OP}^2 = E_6^\mathrm{sc}/P_6$ and
$E_7^\mathrm{sc}/P_7$, known respectively as the Cayley plane and the
Freudenthal variety. These models are defined on the complement
$X^\vee_\mathrm{can}$ of an anti-canonical divisor of the "Langlands dual
homogeneous spaces" $\mathbb{X}^\vee = P^\vee\backslash G^\vee$ in terms of
generalized Pl\"ucker coordinates, analogous to the canonical models defined
for Grassmannians, quadrics and Lagrangian Grassmannians in arXiv:1307.1085,
arXiv:1404.4844, arXiv:1304.4958. We prove that these models for the
exceptional family are isomorphic to the Lie-theoretic mirror models defined in
arXiv:math/0511124 using a restriction to an algebraic torus, also known as the
Lusztig torus, as proven in arXiv:1912.09122. We also give a cluster structure
on $\mathbb{C}[\mathbb{X}^\vee]$, prove that the Pl\"ucker coordinates form a
Khovanskii basis for a valuation defined using the Lusztig torus, and compute
the Newton-Okounkov body associated to this valuation. Although we present our
methods for the exceptional types, they generalize immediately to the members
of other cominuscule families.


title: Study of alternative locations for the SPS Beam Dump Facility

abstract:
  As part of the main focus of the BDF Working Group in 2021, this document
reports on the study of alternative locations and possible optimisation that
may accompany the reuse of existing facilities with the aim of significantly
reducing the costs of the facility. Building on the BDF/SHiP Comprehensive
Design Study (CDS), the assessment rests on the generic requirements and
constraints that allow preserving the physics reach of the facility by making
use of the $4\times 10^{19}$ protons per year at 400\,GeV that are currently
not exploited at the SPS and for which no existing facility is compatible. The
options considered involve the underground areas TCC4, TNC, and ECN3. Recent
improvements of the BDF design at the current location (referred to as
`TT90-TCC9-ECN4') are also mentioned together with ideas for yet further
improvements. The assessments of the alternative locations compiled the large
amount of information that is already available together with a set of
conceptual studies that were performed during 2021.
  The document concludes with a qualitative comparison of the options,
summarising the associated benefits and challenges of each option, such that a
recommendation can be made about which location is to be pursued. The most
critical location-specific studies required to specify the implementation and
cost for each option are identified so that the detailed investigation of the
retained option can be completed before the end of 2022.


title:
Capacity Analysis of Intersections When CAVs Crossing in a Collaborative
  and Lane-Free Order

abstract:
  Connected and autonomous vehicles (CAVs) improve the throughput of
intersections by crossing in a lane-free order as compared to the signalised
crossing of human drivers. However, it is challenging to quantify such an
improvement because the available frameworks to analyse the capacity (i.e., the
maximum throughput) of the conventional intersections does not apply to the
lane-free ones. This paper proposes a novel theoretical framework to
numerically simulate and compare the capacity of lane-free and conventional
intersections. The results show that the maximum number of vehicles passing
through a lane-free intersection is up to seven times more than a signalised
intersection managed by the state-of-the-art max-pressure and Webster
algorithms. A sensitivity analysis shows that, in contrast to the signalised
intersections, the capacity of the lane-free intersections improves by an
increase in initial speed, the maximum permissible speed and acceleration of
vehicles.


title: Strong Admissibility, a Tractable Algorithmic Approach (proofs)

abstract:
  Much like admissibility is the key concept underlying preferred semantics,
strong admissibility is the key concept underlying grounded semantics, as
membership of a strongly admissible set is sufficient to show membership of the
grounded extension. As such, strongly admissible sets and labellings can be
used as an explanation of membership of the grounded extension, as is for
instance done in some of the proof procedures for grounded semantics. In the
current paper, we present two polynomial algorithms for constructing relatively
small strongly admissible labellings, with associated min-max numberings, for a
particular argument. These labellings can be used as relatively small
explanations for the argument's membership of the grounded extension. Although
our algorithms are not guaranteed to yield an absolute minimal strongly
admissible labelling for the argument (as doing do would have implied an
exponential complexity), our best performing algorithm yields results that are
only marginally bigger. Moreover, the runtime of this algorithm is an order of
magnitude smaller than that of the existing approach for computing an absolute
minimal strongly admissible labelling for a particular argument. As such, we
believe that our algorithms can be of practical value in situations where the
aim is to construct a minimal or near-minimal strongly admissible labelling in
a time-efficient way.


title: On the Correctness of Speculative Consensus

abstract:
  The introduction of Bitcoin fueled the development of blockchain-based
resilient data management systems that are resilient against failures, enable
federated data management, and can support data provenance. The key factor
determining the performance of such resilient data management systems is the
consensus protocol used by the system to replicate client transactions among
all participants. Unfortunately, existing high-throughput consensus protocols
are costly and impose significant latencies on transaction processing, which
rules out their usage in responsive high-performance data management systems.
  In this work, we improve on this situation by introducing the
Proof-of-Execution consensus protocol (PoE), a consensus protocol designed for
high-performance low-latency resilient data management. PoE introduces
speculative execution, which minimizes latencies by starting execution before
consensus is reached, and PoE introduces proof-of-executions to guarantee
successful execution to clients. Furthermore, PoE introduces a single-round
check-commit protocol to reduce the overall communication costs of consensus.
Hence, we believe that PoE is a promising step towards flexible general-purpose
low-latency resilient data management systems.


title: Embeddings between partial combinatory algebras

abstract:
  Partial combinatory algebras are algebraic structures that serve as
generalized models of computation. In this paper, we study embeddings of pcas.
In particular, we systematize the embeddings between relativizations of
Kleene's models, of van Oosten's sequential computation model, and of Scott's
graph model, showing that an embedding between two relativized models exists if
and only if there exists a particular reduction between the oracles. We obtain
a similar result for the lambda calculus, showing in particular that it cannot
be embedded in Kleene's first model.


title: Phase Retrieval: From Computational Imaging to Machine Learning

abstract:
  The non-linear equation of phase retrieval appears in many different
scenarios, from X-ray imaging to optical computing. Yet, recovering a signal
from phaseless measurements remains a challenge today. Many algorithms have
been proposed, from flexible gradient descent routines to more specialized
spectral methods. Recent advances in machine learning have revitalized the
study of phase retrieval in two ways. First, the analogy between phase
retrieval and single-layer neural networks enables significant theoretical
advances. Second, deep-learning regularization opens up new possibilities for
phase retrieval applications and computational imaging. Here, we review phase
retrieval under a unifying framework. We focus on computational imaging
applications, provide an overview of recent phase retrieval algorithms, and
discuss recent theoretical results.


title:
Quasi-Static Approximation Error of Electric Field Analysis for
  Transcranial Current Stimulation

abstract:
  Objective: Numerical modeling of electric fields induced by transcranial
alternating current stimulation (tACS) is currently a part of the standard
procedure to predict and understand neural response. Quasi-static approximation
for electric field calculations is generally applied to reduce the
computational cost. This study aims to analyze and quantify the validity of the
approximation over a broad frequency range. Methods: We performed
electromagnetic modeling studies using an anatomical head models. We considered
approximations assuming either a purely ohmic medium (i.e., static formulation)
or a lossy dielectric medium (quasi-static formulation). The results were
compared with the solution of Maxwell's equations for the cases of harmonic and
pulsed signals. Finally, we analyzed the effect of electrode positioning on
these errors. Results: Our findings demonstrate that the quasi-static
approximation is valid and produces a relative error below 1% up to 1.43 MHz.
The largest error is introduced in the static case, where the error is over 1%
across the entire considered spectrum and as high as 20% in the brain at 10 Hz.
Finally, we highlight the special importance of considering the capacitive
effect of tissues for pulsed waveforms, which allows to avoid significant
signal distortion induced by the purely ohmic approximation. Conclusion:
Quasi-static approximation remains valid in the frequency range currently used
for tACS. However, neglecting the permittivity introduces significant error for
both harmonic and non-harmonic signals. Significance: The results point out
that reliable low frequency dielectric data are needed for tCS numerical
modeling. Quasi-static approximation should be favored over the static one
given the low additional computational cost.


title:
Goodbye Tracking? Impact of iOS App Tracking Transparency and Privacy
  Labels

abstract:
  Tracking is a highly privacy-invasive data collection practice that has been
ubiquitous in mobile apps for many years due to its role in supporting
advertising-based revenue models. In defence of user privacy, Apple introduced
two significant changes with iOS 14: App Tracking Transparency (ATT), a
mandatory opt-in system for enabling tracking on iOS, and Privacy Nutrition
Labels, which disclose what kinds of data each app processes. This paper
studies two versions of 1,759 iOS apps from the UK App Store: one version from
before iOS 14 and one that has been updated to comply with the new rules.
  We find that Apple's new policies, as promised, prevent the collection of the
Identifier for Advertisers (IDFA), an identifier used to facilitate cross-app
user tracking. However, many apps still collect device information that can be
used to track users at a group level (cohort tracking) or identify individuals
probabilistically (fingerprinting). We find real-world evidence of apps
computing and agreeing on a fingerprinting-derived identifier through the use
of server-side code, thereby violating Apple's policies and exposing the limits
of what ATT can do against tracking on iOS. This is especially concerning
because we explicitly refused opt-in to tracking in our study, and consent is a
legal requirement for tracking under EU and UK data protection law. We find
that Apple itself engages in some forms of tracking and exempts invasive data
practices like first-party tracking and credit scoring from its new rules. We
also find that the new Privacy Nutrition Labels were sometimes inaccurate,
especially in less popular apps.
  Overall, our findings suggest that, while tracking individual users is more
difficult now, the changes reinforce existing market power of gatekeeper
companies with access to large troves of first-party data.


title:
Fixed Sets of Automorphisms of Countable, Arithmetically Saturated
  Structures

abstract:
  If an automorphism f of a structure M is such that fix(f^k) = fix(f) for all
positive k, then M|fix(f) is a substructure of M. The possible isomorphism
types of such M|fix(f) are characterized when M is countable and arithmetically
saturated.


title:
Mapping the Multilingual Margins: Intersectional Biases of Sentiment
  Analysis Systems in English, Spanish, and Arabic

abstract:
  As natural language processing systems become more widespread, it is
necessary to address fairness issues in their implementation and deployment to
ensure that their negative impacts on society are understood and minimized.
However, there is limited work that studies fairness using a multilingual and
intersectional framework or on downstream tasks. In this paper, we introduce
four multilingual Equity Evaluation Corpora, supplementary test sets designed
to measure social biases, and a novel statistical framework for studying
unisectional and intersectional social biases in natural language processing.
We use these tools to measure gender, racial, ethnic, and intersectional social
biases across five models trained on emotion regression tasks in English,
Spanish, and Arabic. We find that many systems demonstrate statistically
significant unisectional and intersectional social biases.


title:
Practical Digital Disguises: Leveraging Face Swaps to Protect Patient
  Privacy

abstract:
  With rapid advancements in image generation technology, face swapping for
privacy protection has emerged as an active area of research. The ultimate
benefit is improved access to video datasets, e.g. in healthcare settings.
Recent literature has proposed deep network-based architectures to perform
facial swaps and reported the associated reduction in facial recognition
accuracy. However, there is not much reporting on how well these methods
preserve the types of semantic information needed for the privatized videos to
remain useful for their intended application. Our main contribution is a novel
end-to-end face swapping pipeline for recorded videos of standardized
assessments of autism symptoms in children. Through this design, we are the
first to provide a methodology for assessing the privacy-utility trade-offs for
the face swapping approach to patient privacy protection. Our methodology can
show, for example, that current deep network based face swapping is
bottle-necked by face detection in real world videos, and the extent to which
gaze and expression information is preserved by face swaps relative to baseline
privatization methods such as blurring.


title: Quantum variational learning for quantum error-correcting codes

abstract:
  Quantum error-correcting codes (QECCs) are believed to be a necessity for
large-scale fault-tolerant quantum computation. In the past two decades,
various methods of QECC constructions have been developed, leading to many good
families of codes. However, the majority of these codes are not suitable for
near-term quantum devices. Here we present VarQEC, a noise-resilient
variational quantum algorithm to search for quantum codes with a
hardware-efficient encoding circuit. The cost functions are inspired by the
most general and fundamental requirements of a QECC, the Knill-Laflamme
conditions. Given the target noise channel (or the target code parameters) and
the hardware connectivity graph, we optimize a shallow variational quantum
circuit to prepare the basis states of an eligible code. In principle, VarQEC
can find quantum codes for any error model, whether additive or non-additive,
degenerate or non-degenerate, pure or impure. We have verified its
effectiveness by (re)discovering some symmetric and asymmetric codes, e.g.,
$((n,2^{n-6},3))_2$ for $n$ from 7 to 14. We also found new $((6,2,3))_2$ and
$((7,2,3))_2$ codes that are not equivalent to any stabilizer code, and
extensive numerical evidence with VarQEC suggests that a $((7,3,3))_2$ code
does not exist. Furthermore, we found many new channel-adaptive codes for error
models involving nearest-neighbor correlated errors. Our work sheds new light
on the understanding of QECC in general, which may also help to enhance
near-term device performance with channel-adaptive error-correcting codes.


title: Emotional Speech Recognition with Pre-trained Deep Visual Models

abstract:
  In this paper, we propose a new methodology for emotional speech recognition
using visual deep neural network models. We employ the transfer learning
capabilities of the pre-trained computer vision deep models to have a mandate
for the emotion recognition in speech task. In order to achieve that, we
propose to use a composite set of acoustic features and a procedure to convert
them into images. Besides, we present a training paradigm for these models
taking into consideration the different characteristics between acoustic-based
images and regular ones. In our experiments, we use the pre-trained VGG-16
model and test the overall methodology on the Berlin EMO-DB dataset for
speaker-independent emotion recognition. We evaluate the proposed model on the
full list of the seven emotions and the results set a new state-of-the-art.


title:
Sliced gradient-enhanced Kriging for high-dimensional function
  approximation

abstract:
  Gradient-enhanced Kriging (GE-Kriging) is a well-established surrogate
modelling technique for approximating expensive computational models. However,
it tends to get impractical for high-dimensional problems due to the large
inherent correlation matrix and the associated high-dimensional hyper-parameter
tuning problem. To address these issues, we propose a new method in this paper,
called sliced GE-Kriging (SGE-Kriging) for reducing both the size of the
correlation matrix and the number of hyper-parameters. Firstly, we perform a
derivative-based global sensitivity analysis to detect the relative importance
of each input variable with respect to model response. Then, we propose to
split the training sample set into multiple slices, and invoke Bayes' theorem
to approximate the full likelihood function via a sliced likelihood function,
in which multiple small correlation matrices are utilized to describe the
correlation of the sample set. Additionally, we replace the original
high-dimensional hyper-parameter tuning problem with a low-dimensional
counterpart by learning the relationship between the hyper-parameters and the
global sensitivity indices. Finally, we validate SGE-Kriging by means of
numerical experiments with several benchmarks problems. The results show that
the SGE-Kriging model features an accuracy and robustness that is comparable to
the standard one but comes at much less training costs. The benefits are most
evident in high-dimensional problems.


title:
Transfinite Modal Logic: a Semi-quantitative Explanation for Bayesian
  Reasoning

abstract:
  Bayesian reasoning plays a significant role both in human rationality and in
machine learning. In this paper, we introduce transfinite modal logic, which
combines modal logic with ordinal arithmetic, in order to formalize Bayesian
reasoning semi-quantitatively. Technically, we first investigate some
nontrivial properties of ordinal arithmetic, which then enable us to expand
normal modal logic's semantics naturally and elegantly onto the novel
transfinite modal logic, while still keeping the ordinary definition of Kripke
models totally intact. Despite all the transfinite mathematical definition, we
argue that in practice, this logic can actually fit into a completely finite
interpretation as well. We suggest that transfinite modal logic captures the
essence of Bayesian reasoning in a rather clear and simple form, in particular,
it provides a perfect explanation for Sherlock Holmes' famous saying, "When you
have eliminated the impossible, whatever remains, however improbable, must be
the truth." We also prove a counterpart of finite model property theorem for
our logic.


title: RF Signal Transformation and Classification using Deep Neural Networks

abstract:
  Deep neural networks (DNNs) designed for computer vision and natural language
processing tasks cannot be directly applied to the radio frequency (RF)
datasets. To address this challenge, we propose to convert the raw RF data to
data types that are suitable for off-the-shelf DNNs by introducing a
convolutional transform technique. In addition, we propose a simple 5-layer
convolutional neural network architecture (CONV-5) that can operate with raw RF
I/Q data without any transformation. Further, we put forward an RF dataset,
referred to as RF1024, to facilitate future RF research. RF1024 consists of 8
different RF modulation classes with each class having 1000/200 training/test
samples. Each sample of the RF1024 dataset contains 1024 complex I/Q values.
Lastly, the experiments are performed on the RadioML2016 and RF1024 datasets to
demonstrate the improved classification performance.