82 research outputs found
Communication channel analysis and real time compressed sensing for high density neural recording devices
Next generation neural recording and Brain-
Machine Interface (BMI) devices call for high density or distributed
systems with more than 1000 recording sites. As the
recording site density grows, the device generates data on the
scale of several hundred megabits per second (Mbps). Transmitting
such large amounts of data induces significant power
consumption and heat dissipation for the implanted electronics.
Facing these constraints, efficient on-chip compression techniques
become essential to the reduction of implanted systems power
consumption. This paper analyzes the communication channel
constraints for high density neural recording devices. This paper
then quantifies the improvement on communication channel
using efficient on-chip compression methods. Finally, This paper
describes a Compressed Sensing (CS) based system that can
reduce the data rate by > 10x times while using power on
the order of a few hundred nW per recording channel
Multilayer Networks for Text Analysis with Multiple Data Types
We are interested in the widespread problem of clustering documents and
finding topics in large collections of written documents in the presence of
metadata and hyperlinks. To tackle the challenge of accounting for these
different types of datasets, we propose a novel framework based on Multilayer
Networks and Stochastic Block Models. The main innovation of our approach over
other techniques is that it applies the same non-parametric probabilistic
framework to the different sources of datasets simultaneously. The key
difference to other multilayer complex networks is the strong unbalance between
the layers, with the average degree of different node types scaling differently
with system size. We show that the latter observation is due to generic
properties of text, such as Heaps' law, and strongly affects the inference of
communities. We present and discuss the performance of our method in different
datasets (hundreds of Wikipedia documents, thousands of scientific papers, and
thousands of E-mails) showing that taking into account multiple types of
information provides a more nuanced view on topic- and document-clusters and
increases the ability to predict missing links.Comment: 17 pages, 6 figure
On Surgical Fine-tuning for Language Encoders
Fine-tuning all the layers of a pre-trained neural language encoder (either
using all the parameters or using parameter-efficient methods) is often the
de-facto way of adapting it to a new task. We show evidence that for different
downstream language tasks, fine-tuning only a subset of layers is sufficient to
obtain performance that is close to and often better than fine-tuning all the
layers in the language encoder. We propose an efficient metric based on the
diagonal of the Fisher information matrix (FIM score), to select the candidate
layers for selective fine-tuning. We show, empirically on GLUE and SuperGLUE
tasks and across distinct language encoders, that this metric can effectively
select layers leading to a strong downstream performance. Our work highlights
that task-specific information corresponding to a given downstream task is
often localized within a few layers, and tuning only those is sufficient for
strong performance. Additionally, we demonstrate the robustness of the FIM
score to rank layers in a manner that remains constant during the optimization
process.Comment: Accepted to EMNLP 202
Hybrid GRU-CNN Bilinear Parameters Initialization for Quantum Approximate Optimization Algorithm
The Quantum Approximate Optimization Algorithm (QAOA), a pivotal paradigm in
the realm of variational quantum algorithms (VQAs), offers promising
computational advantages for tackling combinatorial optimization problems.
Well-defined initial circuit parameters, responsible for preparing a
parameterized quantum state encoding the solution, play a key role in
optimizing QAOA. However, classical optimization techniques encounter
challenges in discerning optimal parameters that align with the optimal
solution. In this work, we propose a hybrid optimization approach that
integrates Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN),
and a bilinear strategy as an innovative alternative to conventional optimizers
for predicting optimal parameters of QAOA circuits. GRU serves to
stochastically initialize favorable parameters for depth-1 circuits, while CNN
predicts initial parameters for depth-2 circuits based on the optimized
parameters of depth-1 circuits. To assess the efficacy of our approach, we
conducted a comparative analysis with traditional initialization methods using
QAOA on Erd\H{o}s-R\'enyi graph instances, revealing superior optimal
approximation ratios. We employ the bilinear strategy to initialize QAOA
circuit parameters at greater depths, with reference parameters obtained from
GRU-CNN optimization. This approach allows us to forecast parameters for a
depth-12 QAOA circuit, yielding a remarkable approximation ratio of 0.998
across 10 qubits, which surpasses that of the random initialization strategy
and the PPN2 method at a depth of 10. The proposed hybrid GRU-CNN bilinear
optimization method significantly improves the effectiveness and accuracy of
parameters initialization, offering a promising iterative framework for QAOA
that elevates its performance
Ductile fracture and microstructure of a bearing steel in hot tension
382-388Ductile fracture, such as micro-cavities and micro-voids, inevitably exist and evolve under tensile stress state in metal forming. Ductile fracture sways the mechanical performance of 52100 bearing steel. It is necessary to investigate the influences of strain rate and deformation temperature on both ductile fracture and microstructure evolution. Uniaxial
hot tension tests were performed, in which specimens were stretched to failure in the temperatures range from 950 °C to 1160 °C and in the strain rates range from 0.01 /s to 1.0 /s. Specimens metallographies have been explored after hot tension. Experimental results show that the peak stress decreases when deformation temperature increases and strain rate decreases. The critical strain of stress–strain relationships increases when strain rate increases. Fracture morphology is severe at higher deformation temperatures and lower strain rates. Hot tension deformation capacity is worst at 1160 °C and a strain rate of 0.01 /s, has been caused by a larger and coarser grain structure
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