58 research outputs found
TcellSubC: An Atlas of the Subcellular Proteome of Human T Cells
We have curated an in-depth subcellular proteomic map of primary human CD4+ T cells, divided into cytosolic, nuclear and membrane fractions generated by an optimized fractionation and HiRIEF-LC-MS/MS workflow for limited amounts of primary cells. The subcellular proteome of T cells was mapped under steady state conditions, as well as upon 15 min and 1 h of T cell receptor (TCR) stimulation, respectively. We quantified the subcellular distribution of 6,572 proteins and identified a subset of 237 potentially translocating proteins, including both well-known examples and novel ones. Microscopic validation confirmed the localization of selected proteins with previously known and unknown localization, respectively. We further provide the data in an easy-to-use web platform to facilitate re-use, as the data can be relevant for basic research as well as for clinical exploitation of T cells as therapeutic targets
Spike-timing-dependent plasticity: common themes and divergent vistas
Recent experimental observations of spike-timing-dependent synaptic plasticity (STDP) have revitalized
the study of synaptic learning rules. The most
surprising aspect of these experiments lies in the observation
that synapses activated shortly after the occurrence
of a postsynaptic spike are weakened. Thus,
synaptic plasticity is sensitive to the temporal ordering
of pre- and postsynaptic activation. This temporal
asymmetry has been suggested to underlie a range of
learning tasks. In the first part of this review we
highlight some of the common themes from a range of
findings in the framework of predictive coding.As an
example of how this principle can be used in a learning
task, we discuss a recent model of cortical map formation.
In the second part of the review, we point out some
of the differences in STDP models and their functional
consequences. We discuss how differences in the weight-dependence,
the time-constants and the non-linear
properties of learning rules give rise to distinct computational
functions. In light of these computational
issues raised, we review current experimental findings
and suggest further experiments to resolve some
controversies
Evolving Neural Networks through a Reverse Encoding Tree
NeuroEvolution is one of the most competitive evolutionary learning
frameworks for designing novel neural networks for use in specific tasks, such
as logic circuit design and digital gaming. However, the application of
benchmark methods such as the NeuroEvolution of Augmenting Topologies (NEAT)
remains a challenge, in terms of their computational cost and search time
inefficiency. This paper advances a method which incorporates a type of
topological edge coding, named Reverse Encoding Tree (RET), for evolving
scalable neural networks efficiently. Using RET, two types of approaches --
NEAT with Binary search encoding (Bi-NEAT) and NEAT with Golden-Section search
encoding (GS-NEAT) -- have been designed to solve problems in benchmark
continuous learning environments such as logic gates, Cartpole, and Lunar
Lander, and tested against classical NEAT and FS-NEAT as baselines.
Additionally, we conduct a robustness test to evaluate the resilience of the
proposed NEAT algorithms. The results show that the two proposed strategies
deliver improved performance, characterized by (1) a higher accumulated reward
within a finite number of time steps; (2) using fewer episodes to solve
problems in targeted environments, and (3) maintaining adaptive robustness
under noisy perturbations, which outperform the baselines in all tested cases.
Our analysis also demonstrates that RET expends potential future research
directions in dynamic environments. Code is available from
https://github.com/HaolingZHANG/ReverseEncodingTree.Comment: Accepted to IEEE Congress on Evolutionary Computation (IEEE CEC)
2020. Lecture Presentatio
A Parameter-Efficient Learning Approach to Arabic Dialect Identification with Pre-Trained General-Purpose Speech Model
In this work, we explore Parameter-Efficient-Learning (PEL) techniques to
repurpose a General-Purpose-Speech (GSM) model for Arabic dialect
identification (ADI). Specifically, we investigate different setups to
incorporate trainable features into a multi-layer encoder-decoder GSM
formulation under frozen pre-trained settings. Our architecture includes
residual adapter and model reprogramming (input-prompting). We design a
token-level label mapping to condition the GSM for Arabic Dialect
Identification (ADI). This is challenging due to the high variation in
vocabulary and pronunciation among the numerous regional dialects. We achieve
new state-of-the-art accuracy on the ADI-17 dataset by vanilla fine-tuning. We
further reduce the training budgets with the PEL method, which performs within
1.86% accuracy to fine-tuning using only 2.5% of (extra) network trainable
parameters. Our study demonstrates how to identify Arabic dialects using a
small dataset and limited computation with open source code and pre-trained
models.Comment: Accepted to Interspeech. Code is available at:
https://github.com/Srijith-rkr/KAUST-Whisper-Adapter under MIT licens
Interpretable Self-Attention Temporal Reasoning for Driving Behavior Understanding
Performing driving behaviors based on causal reasoning is essential to ensure
driving safety. In this work, we investigated how state-of-the-art 3D
Convolutional Neural Networks (CNNs) perform on classifying driving behaviors
based on causal reasoning. We proposed a perturbation-based visual explanation
method to inspect the models' performance visually. By examining the video
attention saliency, we found that existing models could not precisely capture
the causes (e.g., traffic light) of the specific action (e.g., stopping).
Therefore, the Temporal Reasoning Block (TRB) was proposed and introduced to
the models. With the TRB models, we achieved the accuracy of ,
which outperform the state-of-the-art 3D CNNs from previous works. The
attention saliency also demonstrated that TRB helped models focus on the causes
more precisely. With both numerical and visual evaluations, we concluded that
our proposed TRB models were able to provide accurate driving behavior
prediction by learning the causal reasoning of the behaviors.Comment: Submitted to IEEE ICASSP 2020; Pytorch code will be released soo
IHCV: Discovery of Hidden Time-Dependent Control Variables in Non-Linear Dynamical Systems
Discovering non-linear dynamical models from data is at the core of science.
Recent progress hinges upon sparse regression of observables using extensive
libraries of candidate functions. However, it remains challenging to model
hidden non-observable control variables governing switching between different
dynamical regimes. Here we develop a data-efficient derivative-free method,
IHCV, for the Identification of Hidden Control Variables. First, the
performance and robustness of IHCV against noise are evaluated by benchmarking
the IHCV method using well-known bifurcation models (saddle-node,
transcritical, pitchfork, Hopf). Next, we demonstrate that IHCV discovers
hidden driver variables in the Lorenz, van der Pol, Hodgkin-Huxley, and
Fitzhugh-Nagumo models. Finally, IHCV generalizes to the case when only partial
observational is given, as demonstrated using the toggle switch model, the
genetic repressilator oscillator, and a Waddington landscape model. Our
proof-of-principle illustrates that utilizing normal forms could facilitate the
data-efficient and scalable discovery of hidden variables controlling
transitions between different dynamical regimes and non-linear models.Comment: 12 pages, 2 figure
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