PennyLane is a Python 3 software framework for optimization and machine learning of quantum and hybrid quantumclassical computations. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware.We provide plugins for Strawberry Fields, Rigetti Forest, Qiskit, and ProjectQ, allowing PennyLane optimizations to be run on publicly accessible quantum devices provided by Rigetti and IBM Q. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, and autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications.
Spinifex grass derived hard carbon is used as anodes for sodium-ion batteries. Extraordinary stability and capacity retention of ∼300 mA h g−1 on prolonged cycling against sodium was observed. The eco-friendly and low-cost synthesis procedure make the biomass derived carbon material promising for energy storage applications.
Deep image classification models trained on large amounts of webscraped data are vulnerable to data poisoning, a mechanism for backdooring models. Even a few poisoned samples seen during training can entirely undermine the model's integrity during inference. While it is known that poisoning more samples enhances an attack's effectiveness and robustness, it is unknown whether poisoning too many samples weakens an attack by making it more detectable. We observe a fundamental detectability/robustness tradeoff in data poisoning attacks: Poisoning too few samples renders an attack ineffective and not robust, but poisoning too many samples makes it detectable. This raises the bar for data poisoning attackers who have to balance this trade-off to remain robust and undetectable. Our work proposes two defenses designed to (i) detect and (ii) repair poisoned models as a post-processing step after training using a limited amount of trusted image-label pairs. We show that our defenses mitigate all surveyed attacks and outperform existing defenses using less trusted data to repair a model. Our defense scales to joint vision-language models, such as CLIP, and interestingly, we find that attacks on larger models are more easily detectable but also more robust than those on smaller models. Lastly, we propose two adaptive attacks demonstrating that while our work raises the bar for data poisoning attacks, it cannot mitigate all forms of backdooring.
CCS CONCEPTS• Security and privacy → Software and application security; • Computing methodologies → Machine learning.
The
energy requirements for the production of high quality carbon
fiber and other carbon-based materials made by carbonization is a
key factor limiting the commercial application of these materials.
With the aim of enhancing the carbonization efficiency, we have prepared
polyacrylonitrile (PAN) based precursor materials doped with high
aspect-ratio cellulose nanofibers (CNF) derived from Australian spinifex
grass (T. pungens). This was achieved
by systematically investigating the rheology and electrospinning properties
of composite fibers of PAN and CNF prepared at various CNF concentration
levels and subsequently stabilized and carbonized. The carbon properties
were characterized by X-ray diffraction and Raman spectroscopy. Upon
carbonization, the incorporation of CNF into the PAN precursor led
to changes in the crystallite and graphitic structure of the carbon
materials, and these changes found to be closely related to the CNF
concentration. CNF loadings of 0.5–2 wt % resulted in spinnable
solutions with well-ordered carbon structures exhibiting a reduced
Raman D/G ratio and an increased [002] band intensity by XRD. These
spinifex CNF additives highlight a new approach for enhancing the
energy efficiency of the carbonization process for PAN-based precursors.
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