504 research outputs found
Anisotropic Deformation in the Compressions of Single Crystalline Copper Nanoparticles
Atomistic simulations are performed to probe the anisotropic deformation in
the compressions of face-centred-cubic metallic nanoparticles. In the elastic
regime, the compressive load-depth behaviors can be characterized by the
classical Hertzian model or flat punch model, depending on the surface
configuration beneath indenter. On the onset of plasticity, atomic-scale
surface steps serve as the source of heterogeneous dislocation in nanoparticle,
which is distinct from indenting bulk materials. Under [111] compression, the
gliding of jogged dislocation takes over the dominant plastic deformation. The
plasticity is governed by nucleation and exhaustion of extended dislocation
ribbons in [110] compression. Twin boundary migration mainly sustain the
plastic deformation under [112] compression. This study is helpful to extract
the mechanical properties of metallic nanoparticles and understand their
anisotropic deformation behaviors.Comment: 25 pages, 9 figure
Adai: Separating the Effects of Adaptive Learning Rate and Momentum Inertia
Adaptive Momentum Estimation (Adam), which combines Adaptive Learning Rate
and Momentum, is the most popular stochastic optimizer for accelerating the
training of deep neural networks. However, empirically Adam often generalizes
worse than Stochastic Gradient Descent (SGD). We unveil the mystery of this
behavior based on the diffusion theoretical framework. Specifically, we
disentangle the effects of Adaptive Learning Rate and Momentum of the Adam
dynamics on saddle-point escaping and minima selection. We prove that Adaptive
Learning Rate can escape saddle points efficiently, but cannot select flat
minima as SGD does. In contrast, Momentum provides a drift effect to help the
training process pass through saddle points, and almost does not affect flat
minima selection. This theoretically explains why SGD (with Momentum)
generalizes better, while Adam generalizes worse but converges faster.
Furthermore, motivated by the analysis, we design a novel adaptive optimization
framework named Adaptive Inertia, which uses parameter-wise adaptive inertia to
accelerate the training and provably favors flat minima as well as SGD. Our
extensive experiments demonstrate that the proposed adaptive inertia method can
generalize significantly better than SGD and conventional adaptive gradient
methods.Comment: 28 pages, 11 figures, Adam, Adaptive Inerti
Applying Back Propagation Algorithm and Analytic Hierarchy Process to Environment Assessment
This paper designs a new and scientific environmental quality assessment
method, and takes Saihan dam as an example to explore the environmental
improvement degree to the local and Beijing areas. AHP method is used to assign
values to each weight 7 primary indicators and 21 secondary indicators were
used to establish an environmental quality assessment model. The conclusion
shows that after the establishment of Saihan dam, the local environmental
quality has been improved by 7 times, and the environmental quality in Beijing
has been improved by 13%. Then the future environmental index is predicted.
Finally the Spearson correlation coefficient is analyzed, and it is proved that
correlation is 99% when the back-propagation algorithm is used to test and
prove that the error is little
End-to-End Delay Minimization based on Joint Optimization of DNN Partitioning and Resource Allocation for Cooperative Edge Inference
Cooperative inference in Mobile Edge Computing (MEC), achieved by deploying
partitioned Deep Neural Network (DNN) models between resource-constrained user
equipments (UEs) and edge servers (ESs), has emerged as a promising paradigm.
Firstly, we consider scenarios of continuous Artificial Intelligence (AI) task
arrivals, like the object detection for video streams, and utilize a serial
queuing model for the accurate evaluation of End-to-End (E2E) delay in
cooperative edge inference. Secondly, to enhance the long-term performance of
inference systems, we formulate a multi-slot stochastic E2E delay optimization
problem that jointly considers model partitioning and multi-dimensional
resource allocation. Finally, to solve this problem, we introduce a
Lyapunov-guided Multi-Dimensional Optimization algorithm (LyMDO) that decouples
the original problem into per-slot deterministic problems, where Deep
Reinforcement Learning (DRL) and convex optimization are used for joint
optimization of partitioning decisions and complementary resource allocation.
Simulation results show that our approach effectively improves E2E delay while
balancing long-term resource constraints.Comment: 7 pages, 9 figures, 1 table, 1 algorithm, to be published in IEEE
98th Vehicular Technology Conference (VTC2023-Fall
Distinguishing Neural Speech Synthesis Models Through Fingerprints in Speech Waveforms
Recent strides in neural speech synthesis technologies, while enjoying
widespread applications, have nonetheless introduced a series of challenges,
spurring interest in the defence against the threat of misuse and abuse.
Notably, source attribution of synthesized speech has value in forensics and
intellectual property protection, but prior work in this area has certain
limitations in scope. To address the gaps, we present our findings concerning
the identification of the sources of synthesized speech in this paper. We
investigate the existence of speech synthesis model fingerprints in the
generated speech waveforms, with a focus on the acoustic model and the vocoder,
and study the influence of each component on the fingerprint in the overall
speech waveforms. Our research, conducted using the multi-speaker LibriTTS
dataset, demonstrates two key insights: (1) vocoders and acoustic models impart
distinct, model-specific fingerprints on the waveforms they generate, and (2)
vocoder fingerprints are the more dominant of the two, and may mask the
fingerprints from the acoustic model. These findings strongly suggest the
existence of model-specific fingerprints for both the acoustic model and the
vocoder, highlighting their potential utility in source identification
applications.Comment: Submitted to ICASSP 202
Harnessing the Power of David against Goliath: Exploring Instruction Data Generation without Using Closed-Source Models
Instruction tuning is instrumental in enabling Large Language Models~(LLMs)
to follow user instructions to complete various open-domain tasks. The success
of instruction tuning depends on the availability of high-quality instruction
data. Owing to the exorbitant cost and substandard quality of human annotation,
recent works have been deeply engaged in the exploration of the utilization of
powerful closed-source models to generate instruction data automatically.
However, these methods carry potential risks arising from the usage
requirements of powerful closed-source models, which strictly forbid the
utilization of their outputs to develop machine learning models. To deal with
this problem, in this work, we explore alternative approaches to generate
high-quality instruction data that do not rely on closed-source models. Our
exploration includes an investigation of various existing instruction
generation methods, culminating in the integration of the most efficient
variant with two novel strategies to enhance the quality further. Evaluation
results from two benchmarks and the GPT-4 model demonstrate the effectiveness
of our generated instruction data, which can outperform Alpaca, a method
reliant on closed-source models. We hope that more progress can be achieved in
generating high-quality instruction data without using closed-source models
Overexpression of candidate tumor suppressor ECRG4 inhibits glioma proliferation and invasion
<p>Abstract</p> <p>Background</p> <p>ECRG4 has been shown to be a candidate tumor suppressor in several tumors, but its role in glioma remains poorly understood. In this study, we examined the mRNA expression of ECRG4 and investigated its biological role in glioma cells.</p> <p>Methods</p> <p>Real-time PCR was used to examine expression of ECRG4 in gliomas and their matched brain tissues. The effect of ECRG4 expression on cell proliferation, invasion, and migration was investigated in human U251 glioma cells. Finally, the regulation of transcription factor NF-kB by ECRG4 was evaluated by western blotting.</p> <p>Results</p> <p>Of the 10 paired samples analyzed, 9 glioma tissues displayed the decreased expression of ECRG4 compared to matched normal brain tissues. Cells transfected with ECRG4 showed significantly decreased cell proliferation as evaluated by MTT and colony formation assays. Furthermore, overexpression inhibited cell migration and invasion in transwell and Boyden chamber experiments and retarded the cell cycle progression from G1 to S phase by FACSCaliber cytometry. Protein levels of nuclear transcription factor NF-kB, which is involved in cell proliferation, inversely correlated with ECRG4 expression.</p> <p>Conclusion</p> <p>Our data suggest that ECRG4 serves as a tumor suppressor in glioma.</p
Focused Ultrasound-Induced Cavitation Sensitizes Cancer Cells to Radiation Therapy and Hyperthermia
Focused ultrasound (FUS) has become an important non-invasive therapy for solid tumor ablation via thermal effects. The cavitation effect induced by FUS is thereby avoided but applied for lithotripsy, support drug delivery and the induction of blood vessel destruction for cancer therapy. In this study, head and neck cancer (FaDu), glioblastoma (T98G), and prostate cancer (PC-3) cells were exposed to FUS by using an in vitro FUS system followed by single-dose X-ray radiation therapy (RT) or water bath hyperthermia (HT). Sensitization effects of short FUS shots with cavitation (FUS-Cav) or without cavitation (FUS) to RT or HT (45 °C, 30 min) were evaluated. FUS-Cav significantly increases the sensitivity of cancer cells to RT and HT by reducing long-term clonogenic survival, short-term cell metabolic activity, cell invasion, and induction of sonoporation. Our results demonstrated that short FUS treatment with cavitation has good potential to sensitize cancer cells to RT and HT non-invasively
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