7,454 research outputs found
Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniques
The rapid growth of demanding applications in domains applying multimedia
processing and machine learning has marked a new era for edge and cloud
computing. These applications involve massive data and compute-intensive tasks,
and thus, typical computing paradigms in embedded systems and data centers are
stressed to meet the worldwide demand for high performance. Concurrently, the
landscape of the semiconductor field in the last 15 years has constituted power
as a first-class design concern. As a result, the community of computing
systems is forced to find alternative design approaches to facilitate
high-performance and/or power-efficient computing. Among the examined
solutions, Approximate Computing has attracted an ever-increasing interest,
with research works applying approximations across the entire traditional
computing stack, i.e., at software, hardware, and architectural levels. Over
the last decade, there is a plethora of approximation techniques in software
(programs, frameworks, compilers, runtimes, languages), hardware (circuits,
accelerators), and architectures (processors, memories). The current article is
Part I of our comprehensive survey on Approximate Computing, and it reviews its
motivation, terminology and principles, as well it classifies and presents the
technical details of the state-of-the-art software and hardware approximation
techniques.Comment: Under Review at ACM Computing Survey
StabJGL: a stability approach to sparsity and similarity selection in multiple network reconstruction
In recent years, network models have gained prominence for their ability to
capture complex associations. In statistical omics, networks can be used to
model and study the functional relationships between genes, proteins, and other
types of omics data. If a Gaussian graphical model is assumed, a gene
association network can be determined from the non-zero entries of the inverse
covariance matrix of the data. Due to the high-dimensional nature of such
problems, integrative methods that leverage similarities between multiple
graphical structures have become increasingly popular. The joint graphical
lasso is a powerful tool for this purpose, however, the current AIC-based
selection criterion used to tune the network sparsities and similarities leads
to poor performance in high-dimensional settings. We propose stabJGL, which
equips the joint graphical lasso with a stable and accurate penalty parameter
selection approach that combines the notion of model stability with
likelihood-based similarity selection. The resulting method makes the powerful
joint graphical lasso available for use in omics settings, and outperforms the
standard joint graphical lasso, as well as state-of-the-art joint methods, in
terms of all performance measures we consider. Applying stabJGL to proteomic
data from a pan-cancer study, we demonstrate the potential for novel
discoveries the method brings. A user-friendly R package for stabJGL with
tutorials is available on Github at https://github.com/Camiling/stabJGL
Bayesian Forecasting in Economics and Finance: A Modern Review
The Bayesian statistical paradigm provides a principled and coherent approach
to probabilistic forecasting. Uncertainty about all unknowns that characterize
any forecasting problem -- model, parameters, latent states -- is able to be
quantified explicitly, and factored into the forecast distribution via the
process of integration or averaging. Allied with the elegance of the method,
Bayesian forecasting is now underpinned by the burgeoning field of Bayesian
computation, which enables Bayesian forecasts to be produced for virtually any
problem, no matter how large, or complex. The current state of play in Bayesian
forecasting in economics and finance is the subject of this review. The aim is
to provide the reader with an overview of modern approaches to the field, set
in some historical context; and with sufficient computational detail given to
assist the reader with implementation.Comment: The paper is now published online at:
https://doi.org/10.1016/j.ijforecast.2023.05.00
Using machine learning to predict pathogenicity of genomic variants throughout the human genome
Geschätzt mehr als 6.000 Erkrankungen werden durch Veränderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das Spleißen der mRNA in eine andere Isoform begünstigen. All diese Prozesse müssen überprüft werden, um die zum beschriebenen Phänotyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer Pathogenität.
Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier präsentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst außerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores.
Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells für das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. Außerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-Spleißing verbessern. Außerdem werden Varianteneffektmodelle aufgrund eines neuen, auf Allelhäufigkeit basierten, Trainingsdatensatz entwickelt.
Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfügbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity.
Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants.
The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency.
In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org
An Exploration of the Relationship Between Disability Status Disclosure, Accommodation Use, and Student Success: Curricular and Co-Curricular Implications
Colleges and universities rely on the individualized accommodation process to ensure access for students with disabilities, however, there is ample evidence that educational inequity is pervasive. This study used a critical and comparative quantitative methodology (n=6,500) to investigate data from a large urban community college, analyzing the relationship between final grades and accommodation eligibility and use across academic disciplines and curricular modalities (in-person vs. on-line) to identify implications for the academic success of students with disabilities. Results indicate disability inequity varies across racial identity groups and racial inequity persists across disability status groups. Results also indicate that accommodation may be most impactful for students with lower cumulative grade point averages, students taking courses at the 100 level, students taking online courses, and students taking courses in disciplines such as math. There appear to be benefits to a connection with Disability Services even when students do not notify faculty of their eligibility for accommodation. Recommendations include the inclusion of disability as a demographic within institutional reporting; professional development for faculty, staff, and student leaders that goes beyond compliance to address implications of the intersections of gender, race, identity, and disability; and inclusion of disabled student voices to improve access and inclusion throughout curricular and co-curricular programs and activities
The relationship between literacy outcomes & social-contextual variables for students in low and middle income countries
This dissertation explores the relationship between social-contextual variables and literacy outcomes in Sierra Leone. To ensure the quality of the review, the assessment tool utilized to measure literacy outcomes, in this case, the Early Grade Reading Assessment (EGRA), will be evaluated for appropriateness, using an examination of descriptive statistics, Rasch methodology, and correlations with social-contextual variables. While the assessment may be of acceptable quality, student reading levels are low. However, despite these low scores, students’ achievement was potentially positively impacted by reading with parents and the language spoken at home and at school
Dissecting the mechanisms of transport of herpes simplex virus between Langerhans Cells & dendritic cells in epidermis and dermis following infection of human genital mucosa and skin
Herpes Simplex Virus (HSV) is a sexually transmitted infection (STI) that the World Health Organisation (WHO) has deemed a priority for a vaccine. CD8 and CD4T cells are important in the control and clearance of HSV, however no known vaccine has been able to stimulate CD8T cells. The dermal dendritic cells (dDCs) are suspected to play a role.
Previously the host lab has shown in human tissue that HSV-1 infection of Langerhans cells (LCs) caused apoptosis and migration of LCs to the dermis, where they were phagocytosed by dDCs (termed HSV viral relay). Very little is known about the mechanisms of this relay. The host lab has also identified a second resident epidermal immune cell, Epi-cDC2s, which are infectable by HSV. This thesis aims to unravel the mechanisms involved in the relay.
RNA-seq and cell surface phenotyping on human dDCs subsets showed that was differential chemokine receptor expression. Bead-based immunoassays were used to determine the chemokines produced by HSV-1 infected LCs and Epi-cDC2s,and showed HSV infected LCs produced increased CXCR3 ligands, while HSV infected Epi-cDC2s produced increased CCR5 ligands. The importance of these chemokine axes was investigated using chemotaxis assays.
An cyclic immunofluorescent microscopy panel was then developed to investigate whether this migration could be seen in situ in HSV infected foreskin explants. Underneath epidermal foci of infection, there was migration of both cDC1s and cDC2s towards the basement membrane. Under foci of infection there was a greater proportion of cDC2s clustering with LCs.
The uptake of HSV infected epidermal cells by the dDC subsets was examined using imaging cytometry. Preliminary results suggest that there were no significant differences between the ability of dDCs to phagocytose HSV infected epidermal cells.
Understanding the mechanisms and the role of each dDC subset in the HSV viral relay will determine which dDC subsets are crucial for CD8 and CD4 T cell stimulation
Histopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness
Clear cell renal cell carcinomas (ccRCCs) represent ∼75% of RCC cases and account for most RCC-associated deaths. Inter- and intratumoral heterogeneity (ITH) results in varying prognosis and treatment outcomes. To obtain the most comprehensive profile of ccRCC, we perform integrative histopathologic, proteogenomic, and metabolomic analyses on 305 ccRCC tumor segments and 166 paired adjacent normal tissues from 213 cases. Combining histologic and molecular profiles reveals ITH in 90% of ccRCCs, with 50% demonstrating immune signature heterogeneity. High tumor grade, along with BAP1 mutation, genome instability, increased hypermethylation, and a specific protein glycosylation signature define a high-risk disease subset, where UCHL1 expression displays prognostic value. Single-nuclei RNA sequencing of the adverse sarcomatoid and rhabdoid phenotypes uncover gene signatures and potential insights into tumor evolution. In vitro cell line studies confirm the potential of inhibiting identified phosphoproteome targets. This study molecularly stratifies aggressive histopathologic subtypes that may inform more effective treatment strategies
On Transforming Reinforcement Learning by Transformer: The Development Trajectory
Transformer, originally devised for natural language processing, has also
attested significant success in computer vision. Thanks to its super expressive
power, researchers are investigating ways to deploy transformers to
reinforcement learning (RL) and the transformer-based models have manifested
their potential in representative RL benchmarks. In this paper, we collect and
dissect recent advances on transforming RL by transformer (transformer-based RL
or TRL), in order to explore its development trajectory and future trend. We
group existing developments in two categories: architecture enhancement and
trajectory optimization, and examine the main applications of TRL in robotic
manipulation, text-based games, navigation and autonomous driving. For
architecture enhancement, these methods consider how to apply the powerful
transformer structure to RL problems under the traditional RL framework, which
model agents and environments much more precisely than deep RL methods, but
they are still limited by the inherent defects of traditional RL algorithms,
such as bootstrapping and "deadly triad". For trajectory optimization, these
methods treat RL problems as sequence modeling and train a joint state-action
model over entire trajectories under the behavior cloning framework, which are
able to extract policies from static datasets and fully use the long-sequence
modeling capability of the transformer. Given these advancements, extensions
and challenges in TRL are reviewed and proposals about future direction are
discussed. We hope that this survey can provide a detailed introduction to TRL
and motivate future research in this rapidly developing field.Comment: 26 page
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