Returning to the root : radical feminist thought and feminist theories of International Relations

Feminist International Relations (IR) theory is haunted by a radical feminist ghost. From Enloe's suggestion that the personal is both political and international, often seen as the foundation of feminist IR, feminist IR scholarship has been built on the intellectual contributions of a body of theory it has long left for dead. Though Enloe's sentiment directly references the Hanisch's radical feminist rallying call, there is little direct engagement with the radical feminist thinkers who popularised the sentiment in IR. Rather, since its inception, the field has been built on radical feminist thought it has left for dead. This has left feminist IR troubled by its radical feminist roots and the conceptual baggage that feminist IR has unreflectively carried from second-wave feminism into its contemporary scholarship. By returning to the roots of radical feminism we believe IR can gain valuable insights regarding the system of sex-class oppression, the central role of heterosexuality in maintaining this system, and the feminist case for revolutionary political action in order to dismantle it

Etching with Electron Beam Generated Plasmas

A modulated electron beam generated plasma has been used to dry etch standard photoresist materials and silicon. Oxygen–argon mixtures were used to etch organic resist material and sulfur hexafluoride mixed with argon or oxygen was used for the silicon etching. Etch rates and anisotropy were determined with respect to gas compositions, incident ion energy (from an applied rf bias) and plasma duty factor. For 1818 negative resist and i-line resists the removal rate increased nearly linearly with ion energy (up to 220 nm/min at 100 eV), with reasonable anisotropic pattern transfer above 50 eV. Little change in etch rate was seen as gas composition went from pure oxygen to 70% argon, implying the resist removal mechanism in this system required the additional energy supplied by the ions. With silicon substrates at room temperature, mixtures of argon and sulfur hexafluoride etched approximately seven times faster (1375 nm/min) than mixtures of oxygen and sulfur hexafluoride (,200 nm/min) with 200 eV ions, the difference is attributed to the passivation of the silicon by involatile silicon oxyfluoride sSiOxFyd compounds. At low incident ion energies, the Ar–SF6 mixtures showed a strong chemical (lateral) etch component before an ion-assisted regime, which started at ,75 eV. Etch rates were independent of the 0.5%–50% duty factors studied in this work

Etching with Electron Beam Generated Plasmas

A modulated electron beam generated plasma has been used to dry etch standard photoresist materials and silicon. Oxygen–argon mixtures were used to etch organic resist material and sulfur hexafluoride mixed with argon or oxygen was used for the silicon etching. Etch rates and anisotropy were determined with respect to gas compositions, incident ion energy (from an applied rf bias) and plasma duty factor. For 1818 negative resist and i-line resists the removal rate increased nearly linearly with ion energy (up to 220 nm/min at 100 eV), with reasonable anisotropic pattern transfer above 50 eV. Little change in etch rate was seen as gas composition went from pure oxygen to 70% argon, implying the resist removal mechanism in this system required the additional energy supplied by the ions. With silicon substrates at room temperature, mixtures of argon and sulfur hexafluoride etched approximately seven times faster (1375 nm/min) than mixtures of oxygen and sulfur hexafluoride (,200 nm/min) with 200 eV ions, the difference is attributed to the passivation of the silicon by involatile silicon oxyfluoride sSiOxFyd compounds. At low incident ion energies, the Ar–SF6 mixtures showed a strong chemical (lateral) etch component before an ion-assisted regime, which started at ,75 eV. Etch rates were independent of the 0.5%–50% duty factors studied in this work

Effect of Plasma Flux Composition on the Nitriding Rate of Stainless Steel

The total ion flux and nitriding rate for stainless steel specimens exposed to a modulated electron beam generated argon-nitrogen plasma were measured as a function of distance from the electron beam axis. The total ion flux decreased linearly with distance, but the nitriding rate increased under certain conditions, contrary to other ion flux/nitriding rate comparisons published in the literature. Variation in ion flux composition with distance was explored with a mass spectrometer and energy analyzer as a possible explanation for the anomalous nitriding rate response to ion flux magnitude. A transition in ion flux composition from mostly N2 1 to predominantly N1 ions with increasing distance was observed. Significant differences in molecular and atomic nitrogen ion energy distributions at a negatively biased electrode were also measured. An explanation for nitriding rate dependence based on flux composition and magnitude is proposed

B–N/B–H Transborylation: borane-catalysed nitrile hydroboration

The reduction of nitriles to primary amines is a useful transformation in organic synthesis, however, it often relies upon stoichiometric reagents or transition-metal catalysis. Herein, a borane-catalysed hydroboration of nitriles to give primary amines is reported. Good yields (48–95%) and chemoselectivity (e.g., ester, nitro, sulfone) were observed. DFT calculations and mechanistic studies support the proposal of a double B–N/B–H transborylation mechanism

Addressing function approximation error in actor-critic methods

In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies. We show that this problem persists in an actor-critic setting and propose novel mechanisms to minimize its effects on both the actor and the critic. Our algorithm builds on Double Q-learning, by taking the minimum value between a pair of critics to limit overestimation. We draw the connection between target networks and overestimation bias, and suggest delaying policy updates to reduce per-update error and further improve performance. We evaluate our method on the suite of OpenAI gym tasks, outperforming the state of the art in every environment tested