Contact Moishezon threefolds with second Betti number one

We prove that the only contact Moishezon threefold having second Betti number equal to one is the projective space.Comment: 5 pages. v2: exposition improved as suggested by the referee. To appear in Archiv der Mat

Bound-state-in-continuum guided modes in a multilayer electro-optically active photonic integrated circuit platform

Bound states in the continuum (BICs) are localized states existing within a continuous spectrum of delocalized waves. Emerging multilayer photonic integrated circuit (PIC) platforms allow implementation of low index 1D guided modes within a high-index 2D slab mode continuum; however, conventional wisdom suggests that this always leads to large radiation losses. Here we demonstrate low-loss BIC guided modes for multiple mode polarizations and spatial orders in single- and multi-ridge low-index waveguides within a two-layer heterogeneously integrated electro-optically active photonic platform. The transverse electric (TE) polarized quasi-BIC guided mode with low, <1.4 dB/cm loss enables a Mach-Zehnder electro-optic amplitude modulator comprising a single straight Si3N4 ridge waveguide integrated with a continuous LiNbO3 slab layer. The abrupt optical transitions at the edges of the slab function as compact and efficient directional couplers eliminating the need for additional components. The modulator exhibits a low insertion loss of 2.3 dB and a high extinction ratio of 25 dB. The developed general theoretical model may enable innovative BIC-based approaches for important PIC functions, such as agile spectral filtering and switching, and may suggest new photonic architectures for quantum and neural network applications based on controlled interactions between multiple guided and delocalized modes

The Effects of a Modified Direct Instruction Flashcard System on a 14 Year-Old-Student with Learning Behavioral Issues Enrolled in a Behavior Intervention Classroom

The purpose of this study was to evaluate the effects of a Direct Instruction (DI) flashcard system on the mastery of the multiplication facts by a 14-year-old boy with learning and behavioral issues. The participant attended a low-income high school located in a large urban area in the Pacific Northwest. A changing criterion design was employed to evaluate the efficacy of DI flashcards. When DI flashcards were employed, the performance increased and the participant met or was close to criterion for each criterion ceiling. The DI flashcard procedure was easy to implement and evaluate, and the current paper includes suggestions for additional research with DI flashcards at the high school level.Faculty Sponsor: T. F. McLaughlin and Jennifer Neyma

Anion–π Enzymes

In this report, we introduce artificial enzymes that operate with anion-π interactions, an interaction that is essentially new to nature. The possibility to stabilize anionic intermediates and transition states on an π-acidic surface has been recently demonstrated, using the addition of malonate half thioesters to enolate acceptors as a biologically relevant example. The best chiral anion-π catalysts operate with an addition/decarboxylation ratio of 4:1, but without any stereoselectivity. To catalyze this important but intrinsically disfavored reaction stereoselectively, a series of anion-π catalysts was equipped with biotin and screened against a collection of streptavidin mutants. With the best hit, the S112Y mutant, the reaction occurred with 95% ee and complete suppression of the intrinsically favored side product from decarboxylation. This performance of anion-π enzymes rivals, if not exceeds, that of the best conventional organocatalysts. Inhibition of the S112Y mutant by nitrate but not by bulky anions supports that contributions from anion-π interactions exist and matter, also within proteins. In agreement with docking results, K121 is shown to be essential, presumably to lower the p K a of the tertiary amine catalyst to operate at the optimum pH around 3, that is below the p K a of the substrate. Most importantly, increasing enantioselectivity with different mutants always coincides with increasing rates and conversion, i.e., selective transition-state stabilization

Contact Detection and Constraints Enforcement for the Simulation of Pellet/Clad Thermo-Mechanical Contact in Nuclear Fuel Rods

As fission process heats up the fuel rods, UO2 pellets stacked on top of each other swell both radially and axially, while the surrounding Zircaloy cladding creeps down, so that the pellets eventually come into contact with the clad. This exacerbates chemical degradation of the protective cladding and high stress values may enable the formation and propagation of cracks, thus threatening the integrity of the clad. Along these lines, pellet-cladding interaction establishes itself as a major concern for fuel rod design and core operation in light water reactors. Accurately modeling fuel behavior is challenging because the mechanical contact problem strongly depends on temperature distribution and the pellet-clad coupled heat transfer problem is, in turn, affected by changes in geometry induced by body deformations and stresses generated at the contact interface. Our work focuses on active set strategies to determine the actual contact area in high-fidelity coupled physics fuel performance codes. The approach consists of two steps: in the first one, we determine the boundary region on standard finite element meshes where the contact conditions shall be enforced to prevent objects from occupying the same space. For this purpose, we developed and implemented an efficient parallel search algorithm for detecting mesh inter-penetration and vertex/mesh overlap. The second step deals with solving the mechanical equilibrium taking into account the contact conditions computed in the first step. To do so, we developed a modified version of the multi-point constraint strategy. While the original algorithm was restricted to the Jacobi preconditioned conjugate gradient method, our approach works with any Krylov solver and does not put any restriction on the type of preconditioner used. The multibody thermo-mechanical contact problem is tackled using modern numerics, with continuous finite elements and a Newton-based monolithic strategy to handle nonlinearities (the one stemming from the contact condition itself as well as the one due to the temperature-dependence of the fuel thermal conductivity, for instance) and coupling between the various physics components (gap conductance sensitive to the clad-pellet distance, thermal expansion coefficient or Young’s modulus affected by temperature changes, etc.). We will provide different numerical examples for contact problems using one and multiple bodies in order to demonstrate the performance of the method

GPU enabled real-time optical frequency comb spectroscopy and photonic readout

We describe a GPU-enabled approach for real-time optical frequency comb spectroscopy in which data is recorded, Fourier transformed, normalized, and fit at data rates up to 2.2 GB/s. As an initial demonstration we have applied this approach to rapidly interrogate the motion of an optomechanical accelerometer through the use of an electro-optic frequency comb. However, we note that this approach is readily amenable to both self-heterodyne and dual comb spectrometers for molecular spectroscopy as well as photonic readout where the approach's agility, speed, and simplicity are expected to enable future improvements and applications

MixNN: Protection of Federated Learning Against Inference Attacks by Mixing Neural Network Layers

International audienceMachine Learning (ML) has emerged as a core technology to provide learning models to perform complex tasks. Boosted by Machine Learning as a Service (MLaaS), the number of applications relying on ML capabilities is ever increasing. However, ML models are the source of different privacy violations through passive or active attacks from different entities. In this paper, we present MixNN a proxy-based privacy-preserving system for federated learning to protect the privacy of participants against a curious or malicious aggregation server trying to infer sensitive information (i.e., membership and attribute inferences). MixNN receives the model updates from participants and mixes layers between participants before sending the mixed updates to the aggregation server. This mixing strategy drastically reduces privacy leaks without any trade-off with utility. Indeed, mixing the updates of the model has no impact on the result of the aggregation of the updates computed by the server. We report on an extensive evaluation of MixNN using several datasets and neural networks architectures to quantify privacy leakage through membership and attribute inference attacks as well the robustness of the protection. We show that MixNN significantly limits both the membership and attribute inferences compared to a baseline using model compression and noisy gradient (well known to damage the utility) while keeping the same level of utility as classic federated learning

MixNN: Protection of Federated Learning Against Inference Attacks by Mixing Neural Network Layers

International audienceMachine Learning (ML) has emerged as a core technology to provide learning models to perform complex tasks. Boosted by Machine Learning as a Service (MLaaS), the number of applications relying on ML capabilities is ever increasing. However, ML models are the source of different privacy violations through passive or active attacks from different entities. In this paper, we present MixNN a proxy-based privacy-preserving system for federated learning to protect the privacy of participants against a curious or malicious aggregation server trying to infer sensitive information (i.e., membership and attribute inferences). MixNN receives the model updates from participants and mixes layers between participants before sending the mixed updates to the aggregation server. This mixing strategy drastically reduces privacy leaks without any trade-off with utility. Indeed, mixing the updates of the model has no impact on the result of the aggregation of the updates computed by the server. We report on an extensive evaluation of MixNN using several datasets and neural networks architectures to quantify privacy leakage through membership and attribute inference attacks as well the robustness of the protection. We show that MixNN significantly limits both the membership and attribute inferences compared to a baseline using model compression and noisy gradient (well known to damage the utility) while keeping the same level of utility as classic federated learning