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BROWSE 59,483 MULTI-DISCIPLINARY RESEARCH PREPRINTS

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FEATURED DOCUMENTS

Single-dose BNT162b2 vaccine protects against asymptomatic SARS-CoV-2 infection

Michael Weekes

and 11 more

February 24, 2021
Nick K. Jones1,2*, Lucy Rivett1,2*, Chris Workman3, Mark Ferris3, Ashley Shaw1,
Cambridge COVID-19 Collaboration1,4, Paul J. Lehner1,4, Rob Howes5, Giles
Wright3, Nicholas J. Matheson1,4,6¶, Michael P. Weekes1,7¶1 Cambridge University
NHS Hospitals Foundation Trust, Cambridge, UK2 Clinical Microbiology & Public
Health Laboratory, Public Health England, Cambridge, UK3 Occupational Health and
Wellbeing, Cambridge Biomedical Campus, Cambridge, UK4 Cambridge Institute of
Therapeutic Immunology & Infectious Disease, University of Cambridge, Cambridge,
UK5 Cambridge COVID-19 Testing Centre and AstraZeneca, Anne Mclaren Building,
Cambridge, UK6 NHS Blood and Transplant, Cambridge, UK7 Cambridge Institute for
Medical Research, University of Cambridge, Cambridge, UK*Joint first
authorship¶Joint last authorshipCorrespondence: mpw1001@cam.ac.ukThe UK has
initiated mass COVID-19 immunisation, with healthcare workers (HCWs) given early
priority because of the potential for workplace exposure and risk of onward
transmission to patients. The UK’s Joint Committee on Vaccination and
Immunisation has recommended maximising the number of people vaccinated with
first doses at the expense of early booster vaccinations, based on single dose
efficacy against symptomatic COVID-19 disease.1-3At the time of writing, three
COVID-19 vaccines have been granted emergency use authorisation in the UK,
including the BNT162b2 mRNA COVID-19 vaccine (Pfizer-BioNTech). A vital
outstanding question is whether this vaccine prevents or promotes asymptomatic
SARS-CoV-2 infection, rather than symptomatic COVID-19 disease, because
sub-clinical infection following vaccination could continue to drive
transmission. This is especially important because many UK HCWs have received
this vaccine, and nosocomial COVID-19 infection has been a persistent
problem.Through the implementation of a 24 h-turnaround PCR-based comprehensive
HCW screening programme at Cambridge University Hospitals NHS Foundation Trust
(CUHNFT), we previously demonstrated the frequent presence of pauci- and
asymptomatic infection amongst HCWs during the UK’s first wave of the COVID-19
pandemic.4 Here, we evaluate the effect of first-dose BNT162b2 vaccination on
test positivity rates and cycle threshold (Ct) values in the asymptomatic arm of
our programme, which now offers weekly screening to all staff.Vaccination of
HCWs at CUHNFT began on 8th December 2020, with mass vaccination from 8th
January 2021. Here, we analyse data from the two weeks spanning 18thto 31st
January 2021, during which: (a) the prevalence of COVID-19 amongst HCWs remained
approximately constant; and (b) we screened comparable numbers of vaccinated and
unvaccinated HCWs. Over this period, 4,408 (week 1) and 4,411 (week 2) PCR tests
were performed from individuals reporting well to work. We stratified HCWs <12
days or > 12 days post-vaccination because this was the point at which
protection against symptomatic infection began to appear in phase III clinical
trial.226/3,252 (0·80%) tests from unvaccinated HCWs were positive (Ct<36),
compared to 13/3,535 (0·37%) from HCWs <12 days post-vaccination and 4/1,989
(0·20%) tests from HCWs ≥12 days post-vaccination (p=0·023 and p=0·004,
respectively; Fisher’s exact test, Figure). This suggests a four-fold decrease
in the risk of asymptomatic SARS-CoV-2 infection amongst HCWs ≥12 days
post-vaccination, compared to unvaccinated HCWs, with an intermediate effect
amongst HCWs <12 days post-vaccination.A marked reduction in infections was also
seen when analyses were repeated with: (a) inclusion of HCWs testing positive
through both the symptomatic and asymptomatic arms of the programme (56/3,282
(1·71%) unvaccinated vs 8/1,997 (0·40%) ≥12 days post-vaccination, 4·3-fold
reduction, p=0·00001); (b) inclusion of PCR tests which were positive at the
limit of detection (Ct>36, 42/3,268 (1·29%) vs 15/2,000 (0·75%), 1·7-fold
reduction, p=0·075); and (c) extension of the period of analysis to include six
weeks from December 28th to February 7th 2021 (113/14,083 (0·80%) vs 5/4,872
(0·10%), 7·8-fold reduction, p=1x10-9). In addition, the median Ct value of
positive tests showed a non-significant trend towards increase between
unvaccinated HCWs and HCWs > 12 days post-vaccination (23·3 to 30·3, Figure),
suggesting that samples from vaccinated individuals had lower viral loads.We
therefore provide real-world evidence for a high level of protection against
asymptomatic SARS-CoV-2 infection after a single dose of BNT162b2 vaccine, at a
time of predominant transmission of the UK COVID-19 variant of concern 202012/01
(lineage B.1.1.7), and amongst a population with a relatively low frequency of
prior infection (7.2% antibody positive).5This work was funded by a Wellcome
Senior Clinical Research Fellowship to MPW (108070/Z/15/Z), a Wellcome Principal
Research Fellowship to PJL (210688/Z/18/Z), and an MRC Clinician Scientist
Fellowship (MR/P008801/1) and NHSBT workpackage (WPA15-02) to NJM. Funding was
also received from Addenbrooke’s Charitable Trust and the Cambridge Biomedical
Research Centre. We also acknowledge contributions from all staff at CUHNFT
Occupational Health and Wellbeing and the Cambridge COVID-19 Testing Centre.
SFGAN: Unsupervised Generative Adversarial Learning of 3D Scene Flow from the 3D
Scen...

Guangming Wang

and 4 more

October 04, 2021
Scene flow tracks the three-dimensional (3D) motion of each point in adjacent
point clouds. It provides fundamental 3D motion perception for autonomous
driving and server robot. Although the Red Green Blue Depth (RGBD) camera or
Light Detection and Ranging (LiDAR) capture discrete 3D points in space, the
objects and motions usually are continuous in the macro world. That is, the
objects keep themselves consistent as they flow from the current frame to the
next frame. Based on this insight, the Generative Adversarial Networks (GAN) is
utilized to self-learn 3D scene flow with no need for ground truth. The fake
point cloud of the second frame is synthesized from the predicted scene flow and
the point cloud of the first frame. The adversarial training of the generator
and discriminator is realized through synthesizing indistinguishable fake point
cloud and discriminating the real point cloud and the synthesized fake point
cloud. The experiments on Karlsruhe Institute of Technology and Toyota
Technological Institute (KITTI) scene flow dataset show that our method realizes
promising results without ground truth. Just as human, the proposed method can
identify the similar local structures of two adjacent frames even without
knowing the ground truth scene flow. Then, the local correspondence can be
correctly estimated, and further the scene flow is correctly estimated.  
Corresponding author(s) Email: wanghesheng@sjtu.edu.cn
Artificial  Intelligence Enabled Reagent-free Imaging Hematology
Analyzer            

Xin Shu

and 6 more

November 01, 2021
Leukocyte differential test is a widely performed clinical procedure for
screening infectious diseases. Existing hematology analyzers require
labor-intensive work and a panel of expensive reagents. Here we report an
artificial-intelligence enabled reagent-free imaging hematology analyzer
(AIRFIHA) modality that can accurately classify subpopulations of leukocytes
with minimal sample preparation. AIRFIHA is realized through training a two-step
residual neural network using label-free images of isolated leukocytes acquired
from a custom-built quantitative phase microscope. By leveraging the rich
information contained in quantitative phase images, we not only achieved high
accuracy in differentiating B and T lymphocytes, but also classified CD4 and CD8
cells, therefore outperforming the classification accuracy of most current
hematology analyzers. We validated the performance of AIRFIHA in a randomly
selected test set and cross-validated it across all blood donors. Owing to its
easy operation, low cost, and accurate discerning capability of complex
leukocyte subpopulations, we envision AIRFIHA is clinically translatable and can
also be deployed in resource-limited settings, e.g., during pandemic situations
for the rapid screening of infectious diseases.  Corresponding author(s) Email: 
  rjzhou@cuhk.edu.hk,  rishikesh.pandey@uconn.edu
The "easy part" of the Hard Problem: a resonance theory of consciousness

Tam Hunt

and 1 more

January 04, 2019
Tam Hunt [1], Jonathan SchoolerUniversity of California Santa
Barbara Synchronization, harmonization, vibrations, or simply resonance in its
most general sense seems to have an integral relationship with consciousness
itself. One of the possible “neural correlates of consciousness” in mammalian
brains is a combination of gamma, beta and theta synchrony. More broadly, we see
similar kinds of resonance patterns in living and non-living structures of many
types. What clues can resonance provide about the nature of consciousness more
generally? This paper provides an overview of resonating structures in the
fields of neuroscience, biology and physics and attempts to coalesce these data
into a solution to what we see as the “easy part” of the Hard Problem, which is
generally known as the “combination problem” or the “binding problem.” The
combination problem asks: how do micro-conscious entities combine into a
higher-level macro-consciousness? The proposed solution in the context of
mammalian consciousness suggests that a shared resonance is what allows
different parts of the brain to achieve a phase transition in the speed and
bandwidth of information flows between the constituent parts. This phase
transition allows for richer varieties of consciousness to arise, with the
character and content of that consciousness in each moment determined by the
particular set of constituent neurons. We also offer more general insights into
the ontology of consciousness and suggest that consciousness manifests as a
relatively smooth continuum of increasing richness in all physical processes,
distinguishing our view from emergentist materialism. We refer to this approach
as a (general) resonance theory of consciousness and offer some responses to
Chalmers’ questions about the different kinds of “combination problem.”  At the
heart of the universe is a steady, insistent beat: the sound of cycles in sync….
[T]hese feats of synchrony occur spontaneously, almost as if nature has an eerie
yearning for order. Steven Strogatz, Sync: How Order Emerges From Chaos in the
Universe, Nature and Daily Life (2003) If you want to find the secrets of the
universe, think in terms of energy, frequency and vibration.Nikola Tesla
(1942) I.               Introduction Is there an “easy part” and a “hard part”
to the Hard Problem of consciousness? In this paper, we suggest that there is.
The harder part is arriving at a philosophical position with respect to the
relationship of matter and mind. This paper is about the “easy part” of the Hard
Problem but we address the “hard part” briefly in this introduction.  We have
both arrived, after much deliberation, at the position of panpsychism or
panexperientialism (all matter has at least some associated mind/experience and
vice versa). This is the view that all things and processes have both mental and
physical aspects. Matter and mind are two sides of the same coin.  Panpsychism
is one of many possible approaches that addresses the “hard part” of the Hard
Problem. We adopt this position for all the reasons various authors have listed
(Chalmers 1996, Griffin 1997, Hunt 2011, Goff 2017). This first step is
particularly powerful if we adopt the Whiteheadian version of panpsychism
(Whitehead 1929).  Reaching a position on this fundamental question of how mind
relates to matter must be based on a “weight of plausibility” approach, rather
than on definitive evidence, because establishing definitive evidence with
respect to the presence of mind/experience is difficult. We must generally rely
on examining various “behavioral correlates of consciousness” in judging whether
entities other than ourselves are conscious – even with respect to other
humans—since the only consciousness we can know with certainty is our
own. Positing that matter and mind are two sides of the same coin explains the
problem of consciousness insofar as it avoids the problems of emergence because
under this approach consciousness doesn’t emerge. Consciousness is, rather,
always present, at some level, even in the simplest of processes, but it
“complexifies” as matter complexifies, and vice versa. Consciousness starts very
simple and becomes more complex and rich under the right conditions, which in
our proposed framework rely on resonance mechanisms. Matter and mind are two
sides of the coin. Neither is primary; they are coequal.  We acknowledge the
challenges of adopting this perspective, but encourage readers to consider the
many compelling reasons to consider it that are reviewed elsewhere (Chalmers
1996, Griffin 1998, Hunt 2011, Goff 2017, Schooler, Schooler, & Hunt, 2011;
Schooler, 2015).  Taking a position on the overarching ontology is the first
step in addressing the Hard Problem. But this leads to the related questions: at
what level of organization does consciousness reside in any particular process?
Is a rock conscious? A chair? An ant? A bacterium? Or are only the smaller
constituents, such as atoms or molecules, of these entities conscious? And if
there is some degree of consciousness even in atoms and molecules, as
panpsychism suggests (albeit of a very rudimentary nature, an important point to
remember), how do these micro-conscious entities combine into the higher-level
and obvious consciousness we witness in entities like humans and other mammals?
 This set of questions is known as the “combination problem,” another
now-classic problem in the philosophy of mind, and is what we describe here as
the “easy part” of the Hard Problem. Our characterization of this part of the
problem as “easy”[2] is, of course, more than a little tongue in cheek. The
authors have discussed frequently with each other what part of the Hard Problem
should be labeled the easier part and which the harder part. Regardless of the
labels we choose, however, this paper focuses on our suggested solution to the
combination problem.  Various solutions to the combination problem have been
proposed but none have gained widespread acceptance. This paper further
elaborates a proposed solution to the combination problem that we first
described in Hunt 2011 and Schooler, Hunt, and Schooler 2011. The proposed
solution rests on the idea of resonance, a shared vibratory frequency, which can
also be called synchrony or field coherence. We will generally use resonance and
“sync,” short for synchrony, interchangeably in this paper. We describe the
approach as a general resonance theory of consciousness or just “general
resonance theory” (GRT). GRT is a field theory of consciousness wherein the
various specific fields associated with matter and energy are the seat of
conscious awareness.  A summary of our approach appears in Appendix 1.  All
things in our universe are constantly in motion, in process. Even objects that
appear to be stationary are in fact vibrating, oscillating, resonating, at
specific frequencies. So all things are actually processes. Resonance is a
specific type of motion, characterized by synchronized oscillation between two
states.  An interesting phenomenon occurs when different vibrating processes
come into proximity: they will often start vibrating together at the same
frequency. They “sync up,” sometimes in ways that can seem mysterious, and allow
for richer and faster information and energy flows (Figure 1 offers a
schematic). Examining this phenomenon leads to potentially deep insights about
the nature of consciousness in both the human/mammalian context but also at a
deeper ontological level.
The Cream of the Crop: Biology, Breeding and Applications of Cannabis sativa

Susanne Schilling*^

and 9 more

October 01, 2020
Cannabis sativa is an extraordinarily versatile species. Hemp and its cousin
marijuana, both C. sativa, have been used for millennia as a source of fibre,
oil and for medicinal, spiritual and recreational purposes. Because the
consumption of Cannabis can have psychoactive effects, the plant has been widely
banned throughout the last century. In the past decade, evidence of its
medicinal properties did lead to the relaxation of legislation in many countries
around the world. Consequently, the genetics and development of Cannabis as well
as Cannabis-derived products are the subject of renewed attention.Here, we
review the biology of C. sativa, including recent insights from taxonomy,
morphology and genomics, with an emphasis on the genetics of cannabinoid
synthesis. Because the female Cannabis flower is of special interest as the site
of cannabinoid synthesis, we explore flower development, flowering time well as
the species’ unique sex determination system in detail. Furthermore, we outline
the tremendous medicinal, engineering, and environmental opportunities
that Cannabis bears. Together, the picture emerges that our understanding
of Cannabis biology currently progresses at an unusual speed. A future challenge
will be to preserve the multi-purpose nature of Cannabis, and to harness its
medicinal properties and sustainability advantages simultaneously.
Open Chemistry, JupyterLab, REST, and Quantum Chemistry

Marcus D. Hanwell

and 7 more

August 26, 2020
Quantum chemistry must evolve if it wants to fully leverage the benefits of the
internet age, where the world wide web offers a vast tapestry of tools that
enable users to communicate and interact with complex data at the speed and
convenience of a button press. The Open Chemistry project has developed an open
source framework that offers an end-to-end solution for producing, sharing, and
visualizing quantum chemical data interactively on the web using an array of
modern tools and approaches. These tools build on some of the best open source
community projects such as Jupyter for interactive online notebooks, coupled
with 3D accelerated visualization, state-of-the-art computational chemistry
codes including NWChem and Psi4 and emerging machine learning and data mining
tools such as ChemML and ANI. They offer flexible formats to import and export
data, along with approaches to compare computational and experimental data.
Masks for the public: laying straw men to rest

Trisha Greenhalgh

April 28, 2020
This paper responds to one by Graham Martin and colleagues, who offered a
critique of my previous publications on masks for the lay public in the Covid-19
pandemic. I address their charges that my co-authors and I had misapplied the
precautionary principle; drawn conclusions that were not supported by empirical
research; and failed to take account of potential harms. But before that, I
remind Martin et al that the evidence on mask wearing goes beyond the contested
trials and observational studies they place centre stage. I set out some key
findings from basic science, epidemiology, mathematical modelling, case studies
and natural experiments, and use this rich and diverse body of evidence as the
backdrop for my rebuttal of their narrowly-framed objections. I challenge my
critics’ apparent assumption that a particular kind of systematic review should
be valorised over narrative and real-world evidence, since stories are crucial
to both our scientific understanding and our moral imagination. I conclude by
thanking my academic adversaries for the intellectual sparring match, but exhort
them to remember our professional accountability to a society in crisis. It is
time to lay straw men to rest and engage, scientifically and morally, with the
dreadful tragedy that is unfolding across the world.
Supporting Information for "Learning Assembly Tasks in a Few Minutes by
Combining Imp...

Padmaja Kulkarni

and 3 more

October 18, 2021
This Supporting information includes interactive plots, videos, and data
captured while performing evaluation and validation experiments for our paper. 
Rethinking wellbeing: Toward a more ethical science of wellbeing that considers
curre...

Jessica mead

and 6 more

August 22, 2019
The construct of wellbeing has been criticised as a neoliberal construction of
western individualism that ignores wider systemic issues including increasing
burden of chronic disease, widening inequality, concerns over environmental
degradation and anthropogenic climate change. While these criticisms overlook
recent developments, there remains a need for biopsychosocial models that extend
theoretical grounding beyond individual wellbeing, incorporating overlapping
contextual issues relating to community and environment. Our first GENIAL
model \cite{Kemp_2017} provided a more expansive view of pathways to longevity
in the context of individual health and wellbeing, emphasising bidirectional
links to positive social ties and the impact of sociocultural factors. In this
paper, we build on these ideas and propose GENIAL 2.0, focusing on intersecting
individual-community-environmental contributions to health and wellbeing, and
laying an evidence-based, theoretical framework on which future research and
innovative therapeutic innovations could be based. We suggest that our
transdisciplinary model of wellbeing - focusing on individual, community and
environmental contributions to personal wellbeing - will help to move the
research field forward. In reconceptualising wellbeing, GENIAL 2.0 bridges the
gap between psychological science and population health health systems, and
presents opportunities for enhancing the health and wellbeing of people living
with chronic conditions. Implications for future generations including the very
survival of our species are discussed.  
Global synthesis of the effectiveness of flower strips and hedgerows on pest
control,...

Matthias Albrecht

and 42 more

April 06, 2020
Floral plantings are promoted to foster ecological intensification of
agriculture through provisioning of ecosystem services. However, a comprehensive
assessment of the effectiveness of different floral plantings, their
characteristics and consequences for crop yield across global regions is
lacking. Here we quantified the impacts of flower strips and hedgerows on pest
control and pollination services in adjacent crops using a global dataset of 529
sites. Flower strips, but not hedgerows, enhanced pest control services in
adjacent fields by 16% on average. However, effects on crop pollination and
yield were more variable. Our synthesis identifies several important drivers of
variability in effectiveness of plantings: pollination services declined
exponentially with distance from plantings, and perennial and older flower
strips with higher flowering plant diversity enhanced pollination more
effectively. These findings provide promising pathways to optimize floral
plantings to more effectively contribute to ecosystem service delivery and
ecological intensification of agriculture in the future.
Biomolecular Histology as a Novel Proxy for Ancient DNA and Protein Sequence
Preserva...

Landon A. Anderson

December 13, 2022
Researchers' ability to accurately screen fossil and subfossil specimens for
preservation of DNA and protein sequences remains limited. Thermal exposure and
geologic age are usable proxies for sequence preservation on a broad scale but
are of nominal use for specimens of similar depositional environments. Cell and
tissue biomolecular histology is thus proposed as a novel proxy for determining
sequence preservation potential of ancient specimens with improved accuracy.
Biomolecular histology as a proxy is hypothesized to elucidate why
fossils/subfossils of some depositional environments preserve sequences while
others do not and to facilitate selection of ancient specimens for use in
molecular studies.
FFP3 respirators protect healthcare workers against infection  with SARS-CoV-2

Mark Ferris

and 14 more

June 30, 2021
IntroductionConsistent with World Health Organization (WHO) advice [1], UK
Infection Protection Control guidance recommends that healthcare workers (HCWs)
caring for patients with coronavirus disease 2019 (COVID-19) should use fluid
resistant surgical masks type IIR (FRSMs) as respiratory protective equipment
(RPE), unless aerosol generating procedures (AGPs) are being undertaken or are
likely, when a filtering face piece 3 (FFP3) respirator should be used [2]. In a
recent update, an FFP3 respirator is recommended if “an unacceptable risk of
transmission remains following rigorous application of the hierarchy of control”
[3]. Conversely, guidance from the Centers for Disease Control and Prevention
(CDC) recommends that HCWs caring for patients with COVID-19 should use an N95
or higher level respirator [4]. WHO guidance suggests that a respirator, such as
FFP3, may be used for HCWs in the absence of AGPs if availability or cost is not
an issue [1].A recent systematic review undertaken for PHE concluded that:
“patients with SARS-CoV-2 infection who are breathing, talking or coughing
generate both respiratory droplets and aerosols, but FRSM (and where required,
eye protection) are considered to provide adequate staff protection” [5].
Nevertheless, FFP3 respirators are more effective in preventing aerosol
transmission than FRSMs, and observational data suggests that they may improve
protection for HCWs [6]. It has therefore been suggested that respirators should
be considered as a means of affording the best available protection [7], and
some organisations have decided to provide FFP3 (or equivalent) respirators to
HCWs caring for COVID-19 patients, despite a lack of mandate from local or
national guidelines [8].Data from the HCW testing programme at Cambridge
University Hospitals NHS Foundation Trust (CUHNFT) during the first wave of the
UK severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic
indicated a higher incidence of infection amongst HCWs caring for patients with
COVID-19, compared with those who did not [9]. Subsequent studies have confirmed
this observation [10, 11]. This disparity persisted at CUHNFT in December 2020,
despite control measures consistent with PHE guidance and audits indicating good
compliance. The CUHNFT infection control committee therefore implemented a
change of RPE for staff on “red” (COVID-19) wards from FRSMs to FFP3
respirators. In this study, we analyse the incidence of SARS-CoV-2 infection in
HCWs before and after this transition.


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Multimodal Ra...

Amir Omeradzic

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The self-adaptive, multi-recombinative (µ/µ_I , λ)-ES (Evolution Strategy) is
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Zenebe Mekonnen Adare

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Cotton is an important cash crop. Its growth and development is influenced by
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Zejie Li

and 4 more

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Transformer saturation is a common issue in megawatt converter-based grid
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Xiaochuan Fang

and 3 more

December 14, 2023
The discovery of the 'Einstein' monotile represents one of the most significant
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José Gustavo Cuervo A.1, Anthony Flagg2, Víctor López3, Iván M.
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Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative
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and 4 more

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of Electronic Healthcare Records (EHR) by generating realistic de-identified
data. However, the existing literature primarily focuses on the quality of
synthetic health data, neglecting the crucial aspect of fairness in downstream
predictions. Consequently, models trained on synthetic EHR have faced criticism
for producing biased outcomes in target tasks. These biases can arise from
either spurious correlations between features or the failure of models to
accurately represent subgroups. To address these concerns, we present
Bias-transforming Gen-erative Adversarial Networks (Bt-GAN), a GAN-based
synthetic data generator specifically designed for the healthcare domain. In
order to tackle spurious correlations (i), we propose an information-constrained
Data Generation Process (DGP) that enables the generator to learn a fair
deterministic transformation based on a well-defined notion of algorithmic
fairness. To overcome the challenge of capturing exact subgroup representations
(ii), we incentivize the generator to preserve subgroup densities through
score-based weighted sampling. This approach compels the generator to learn from
underrepresented regions of the data manifold. To evaluate the effectiveness of
our proposed method, we conduct extensive experiments using the Medical
Information Mart for Intensive Care (MIMIC-III) database. Our results
demonstrate that Bt-GAN achieves state-of-the-art accuracy while significantly
improving fairness and minimizing bias amplification. Furthermore, we perform an
in-depth explainability analysis to provide additional evidence supporting the
validity of our study. In conclusion, our research introduces a novel and
professional approach to addressing the limitations of synthetic data generation
in the healthcare domain. By incorporating fairness considerations and
leveraging advanced techniques such as GANs, we pave the way for more reliable
and unbiased predictions in healthcare applications.
Spam Unveiled: Exploring Types and Approaches in Handling Spam Messages

Nur Atikah Zolkefly

and 2 more

December 14, 2023
Spam Unveiled: Exploring Types and Approaches in Handling Spam MessagesNurul
Firzana Binti SamliFaculty of Computer and Mathematical Sciences Universiti
Teknologi Mara, UiTM Tapah, Malaysia2022923861@student.uitm.edu.myNur Atikah
Binti ZolkeflyFaculty of Computer and Mathematical Sciences Universiti Teknologi
Mara, UiTM Tapah, Malaysia
Learning from research on creative involvement of people with a communication
difficu...

Mostafa Hatem

December 14, 2023
A document by Faten Mostafa Hatem. Click on the document to view its contents.
MelSpectroNet: Enhancing Voice Authentication Security with AI-based Siamese
Model an...

Gitesh Kambli

and 2 more

December 14, 2023
Voice authentication has become critical for secure access control while
achieving usability. Background noise and increased security requirements,
however, continue to be problems. This paper presents MelSpectroNet, an
innovative voice authentication system using Siamese neural network trained on
over one million samples. It leverages mel spec-trograms for efficient feature
extraction and employs noise reduction, enhancing reliability. The model
achieves 96.62% test accuracy, demonstrating efficacy. Our methodology involves
audio denoising, meticulous spectrogram preprocessing, a tailored Siamese
architecture, and rigorous training. Testing demonstrates MelSpectroNet's
exceptional performance and ability to generalize. However, enhancing
longitudinal accuracy by accounting for natural voice variations over time still
needs exploration. Overall, MelSpectroNet pioneers highly accurate and usable
voice au-thentication with enhanced security. It balances user convenience and
stringent authentication needs. This research motivates further work to optimize
these systems for diverse conditions while advancing security and inclusiveness.
Enhancing Question Prediction with Flan T5 -A Context-Aware Language Model
Approach

Jay Oza

and 1 more

December 14, 2023
This research proposes a context-aware language model designed to predict the
subsequent user question based on a given context. Harnessing the capabilities
of Google-FLAN-T5, an advanced language model, our approach integrates a memory
mechanism to preserve the generated question within the specified context. The
model's proficiency in capturing context and generating pertinent questions
leads to an enhanced user interaction experience, fostering improved outcomes in
diverse applications. The research encompasses a systematic methodology for
constructing the machine learning model, encompassing data collection,
preprocessing, tokenization, model implementation, and fine-tuning stages. Our
model's performance evaluation is executed via comprehensive experiments,
incorporating an array of assessment metrics, including BLEU-1, BLEU-2, BLEU-3,
BLEU-4, ROUGE-1, ROUGE-2, and ROUGE-L. The results showcase the efficacy and
practical applicability of our proposed approach, underscoring its potential to
drive advancements in context-aware question generation utilizing expansive
language models and external APIs, exemplified by Cohere.
Mediterranean Sea between

Fiz F. Pérez

and 16 more

December 14, 2023
A document by Fiz F. Pérez. Click on the document to view its contents.
Signal to Noise Ratio and Spectral Sampling Constraints on Olivine Detection and
Comp...

Sebastian Alonso Perez-Lopez

and 2 more

December 14, 2023
The intermediate infrared region (IMIR, 4 – 8 µm) provides significant
advantages over the visible-shortwave infrared and mid-infrared for quantitative
determination of mafic mineral composition. In particular, olivine’s sharp
spectral features in IMIR spectra exhibit systematic shifts in wavelength
position with iron-magnesium content. Previous IMIR studies have used laboratory
data, with signal-to-noise ratios (SNRs) and spectral resolutions greater than
those expected of imaging spectrometers. Here we employ a feature fitting
algorithm to quantitatively assess the influence of SNR and sampling rate on
olivine detection and compositional interpretation from IMIR data. We
demonstrate that olivine is easily distinguished from pyroxene and other
lunar-relevant minerals across IMIR wavelengths, with the feature-fitting
algorithm effectively determining olivine composition for various synthetic,
terrestrial, Martian, and lunar samples with an average error of only 6.4 mol%.
We then apply the feature-fitting routine to degraded spectra with reduced SNRs
and sampling rates, establishing data-quality thresholds for accurate
determination of olivine composition. Spectra for the sample most relevant to
lunar exploration, an Apollo 74002 drive tube consisting of microcrystalline
olivine and glass-rich pyroclastics, required SNRs ≥ 200 for sampling rates ≤ 25
nm to predict composition within ±11 Mg# (molar Mg/[Mg+Fe] * 100) of the
sample’s true composition. Derived limits on SNRs and sampling rates will serve
as valuable inputs for the development of IMIR imaging spectrometers, enabling
comprehensive knowledge of olivine composition across the lunar surface and
providing valuable insight into the Moon’s crustal history and thermal
evolution.

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DOCUMENTS RECENTLY ACCEPTED IN SCHOLARLY JOURNALS

A New Construction Method for Keystream Generators

Çağdaş Gül

and 1 more

December 07, 2023
We introduce a new construction method of diffusion layers for Substitution
Permutation Network (SPN) structures along with its security proofs. The new
method can be used in block ciphers, stream ciphers, hash functions, and sponge
constructions. Moreover, we define a new stream cipher mode of operation through
a fixed pseudorandom permutation and provide its security proofs in the
indistinguishability model. We refer to a stream cipher as a Small Internal
State Stream (SISS) cipher if its internal state size is less than twice its key
size. There are not many studies about how to design and analyze SISS ciphers
due to the criterion on the internal state sizes, resulting from the classical
tradeoff attacks. We utilize our new mode and diffusion layer construction to
design an SISS cipher having two versions, which we call DIZY. We further
provide security analyses and hardware implementations  of DIZY. In terms of
area cost, power, and energy consumption, the hardware performance is among the
best when compared to some prominent stream ciphers, especially for frame-based
encryptions that need frequent initialization. Unlike recent SISS ciphers such
as Sprout, Plantlet, LILLE, and Fruit; DIZY does not have a keyed update
function, enabling efficient key changing.Â
Buffer Resets: A Packet Discarding Policy for Timely Physiological Data
Collection in...

Costas Michaelides

and 1 more

December 05, 2023
Affective virtual reality (VR) gaming systems rely on timely physiological data
collection, in order to generate personalized responses that enhance the
emotional impact of a video game on its user. In this context, we propose a
simple policy for timely data collection from wireless sensor nodes placed on
the human body. Our policy is applied at each sensor node. Upon each packet
arrival we check whether the buffer is full or not. If the buffer is full, then
we empty it before adding the packet. In this very simple way, we avoid buffer
congestion and impose timeliness. We simulated this aggressive buffer reset
policy using a body area network (BAN) model in OMNeT++. By varying the packet
generation rate of each node, we showed that our policy outperforms first come
first served (FCFS) and last come first served (LCFS) queueing policies in terms
of peak age, while packet reception is barely affected. Buffer resets can be
easily integrated into existing random access protocols to support timely data
collection.
Joint Detection Algorithm for Multiple Cognitive Users in Spectrum Sensing

Fanfei Meng

and 3 more

December 02, 2023
Spectrum sensing technology is a crucial aspect of modern communication
technology, serving as one of the essential techniques for efficiently utilizing
scarce information resources in tight frequency bands. This paper first
introduces three common logical circuit decision criteria in hard decisions and
analyzes their decision rigor. Building upon hard decisions, the paper further
introduces a method for multi-user spectrum sensing based on soft decisions.
Then the paper simulates the false alarm probability and detection probability
curves corresponding to the three criteria. The simulated results of multi-user
collaborative sensing indicate that the simulation process significantly reduces
false alarm probability and enhances detection probability. This approach
effectively detects spectrum resources unoccupied during idle periods,
leveraging the concept of time-division multiplexing and rationalizing the
redistribution of information resources. The entire computation process relies
on the calculation principles of power spectral density in communication theory,
involving threshold decision detection for noise power and the sum of noise and
signal power. It provides a secondary decision detection, reflecting the
perceptual decision performance of logical detection methods with relative
accuracy.
Automatic Stub Avoidance for a Powered Prosthetic Leg over Stairs and Obstacles

Shihao Cheng

and 2 more

December 02, 2023
Passive prosthetic legs require undesirable compensations from amputee users to
avoid stubbing obstacles and stairsteps. Powered prostheses can reduce those
compensations by restoring normative joint biomechanics, but the absence of user
proprioception and volitional control combined with the absence of environmental
awareness by the prosthesis increases the risk of collisions. This paper
presents a novel stub avoidance controller that automatically adjusts prosthetic
knee/ankle kinematics based on suprasensory measurements of environmental
distance from a small, lightweight, low-power, low-cost ultrasonic sensor
mounted above the prosthetic ankle. In a case study with two transfemoral
amputee participants, this control method reduced the stub rate during stair
ascent by 89.95% and demonstrated an 87.5% avoidance rate for crossing different
obstacles on level ground. No thigh kinematic compensation was required to
achieve these results. These findings demonstrate a practical perception
solution for powered prostheses to avoid collisions with stairs and obstacles
while restoring normative biomechanics during daily activities.
Temporal Early Exits for Efficient Video Object Detection

Amin Sabet

and 4 more

December 04, 2023
Efficiently transferring image-based object detectors to the domain of video
remains challenging under resource constraints. Previous efforts used feature
propagation to avoid recomputing unchanged features. However, the overhead is
significant when working with very slowly changing scenes, such as in
surveillance applications. In this paper, we propose temporal early exits to
reduce the computational complexity of video object detection. Multiple temporal
early exit modules with low computational overhead are inserted at early layers
of the backbone network to identify the semantic differences between consecutive
frames. Full computation is only required if the frame is identified as having a
semantic change to previous frames; otherwise, detection results from previous
frames are reused. Experiments on ImangeNet VID and TVnet show that the approach
can accelerate video object detection by 1.7x compared to SOTA, with a reduction
of only <1% in mAP.
Space weather in the popular media, and the opportunities the upcoming solar
maximum...

Brett A Carter

and 5 more

November 29, 2023
A document by Nicholas Violette. Click on the document to view its contents.
Novel Rotary Encoder with Multi-Axis Hall Sensors

Bruno Brajon

and 2 more

November 28, 2023
We present a novel high-accuracy rotary magnetic sensor system composed of a
two-track coded multi-pole magnet and a dual-spot multi-axes magnetic sensor.
The novelty is the measurement of two orthogonal magnetic field components in
each of the two sensing spots of the sensor, one associated with each magnet
track. As the two orthogonal signals in each spot are naturally in quadrature,
i.e. they represent a sine and a cosine signal, the measurement principle is
virtually independent of the magnet pole size and pitch. We can therefore design
a magnet with much larger poles which in turn generate stronger magnetic flux,
allowing for an increased air gap between sensor and magnet. The calibrated
encoder system was characterized to deliver 13 bits of absolute accuracy and 17
bits of resolution over the full 360° range. Article ACCEPTED for publication in
2023 IEEE Sensors.
Augmented Reality (AR) Technology on Student Engagement: An Experimental
Research Stu...

KHRITISH SWARGIARY

November 22, 2023
This experimental research aims to investigate the potential benefits of
integrating augmented reality (AR) technology into the classroom setting. The
study hypothesizes that the use of AR technology will enhance student engagement
and lead to improved learning outcomes. A sample of participants from a local
high school will be involved in this research. The research employs a
pre-test/post-test design to assess the impact of AR technology on student
engagement and learning outcomes. Data will be collected and analysed to
determine the effectiveness of AR technology in enhancing classroom education.
Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking
Neural N...

Ali Sam

and 3 more

November 21, 2023
Depression severity can be classified into distinct phases based on the Beck
depression inventory (BDI) test scores, a subjective questionnaire. However,
quantitative assessment of depression may be attained through the examination
and categorization of electroencephalography (EEG) signals. Spiking neural
networks (SNNs), as the third generation of neural networks, incorporate
biologically realistic algorithms, making them ideal for mimicking internal
brain activities while processing EEG signals. This study introduces a novel
framework that for the first time, combines an SNN architecture and a long
short-term memory (LSTM) structure to model the brainâ\euro™s underlying
structures during different stages of depression and effectively classify
individual depression levels using raw EEG signals. By employing a
brain-inspired SNN model, our research provides fresh perspectives and advances
knowledge of the neurological mechanisms underlying different levels of
depression. The methodology employed in this study includes the utilization of
the synaptic time dependent plasticity (STDP) learning rule within a
3-dimensional braintemplate structured SNN model. Furthermore, it encompasses
the tasks of classifying and predicting individual outcomes, visually
representing the structural alterations in the brain linked to the anticipated
outcomes, and offering interpretations of the findings. Notably, our method
achieves exceptional accuracy in classification, with average rates of 98% and
96% for eyes-closed and eyes-open states, respectively. These results
significantly outperform state-of-the-art deep learning methods.
Framing cognitive machines: A sociotechnical taxonomy

Pedro H. Albuquerque

and 1 more

November 20, 2023
Aims: we propose a sociotechnical taxonomy for the analysis of socio-economic
disruptions caused by technological innovations. Methodology: a
transdisciplinary principled approach is used to build the taxonomy through
categorization and characterization of technologies using concepts and
definitions originating from cybernetics, occupational science, and economics.
The sociotechnical taxonomy is then used, with the help of logical propositions,
to connect the characteristics of different categories of technologies to their
socio-economic effects, for example their externalities. Results: we offer
concrete illustrations of concepts and uses, and an Industry 5.0 case study as
an application of the taxonomy. We suggest that the taxonomy can inform the
analysis of opportunities and risks related to technological disruptions,
specially of those that result from the rise of cognitive machines.
Relay-Aided Uplink NOMA Under Non-Orthogonal CCI and Imperfect SIC in 6G
Networks

Volkan Ozduran

and 4 more

November 13, 2023
A document by Volkan Ozduran . Click on the document to view its contents.
Autonomous Advanced Aerial Mobility â\euro“An End-to-end Autonomy Framework for
UAVs...

Sakshi Mishra

and 1 more

November 13, 2023
Developing aerial robots that can both safely navigate and execute assigned
mission without any human intervention â\euro“ i.e., fully autonomous aerial
mobility of passengers and goods â\euro“ is the larger vision that guides the
research, design, and development efforts in the aerial autonomy space. However,
it is highly challenging to concurrently operationalize all types of aerial
vehicles that are operating fully autonomously sharing the airspace. Full
autonomy of the aerial transportation sector includes several aspects, such as
design of the technology that powers the vehicles, operations of multi-agent
fleets, and process of certification that meets stringent safety requirements of
aviation sector. Thereby, Autonomous Advanced Aerial Mobility is still a vague
term and its consequences for researchers and professionals are ambiguous. To
address this gap, we present a comprehensive perspective on the emerging field
of autonomous advanced aerial mobility, which involves the use of unmanned
aerial vehicles (UAVs) and electric vertical takeoff and landing (eVTOL)
aircraft for various applications, such as urban air mobility, package delivery,
and surveillance. The article proposes a scalable and extensible autonomy
framework consisting of four main blocks: sensing, perception, planning, and
controls. Furthermore, the article discusses the challenges and opportunities in
multi-agent fleet operations and management, as well as the testing, validation,
and certification aspects of autonomous aerial systems. Finally, the article
explores the potential of monolithic models for aerial autonomy and analyzes
their advantages and limitations. The perspective aims to provide a holistic
picture of the autonomous advanced aerial mobility field and its future
directions.

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