Divide-and-Conquer Distributed Learning: Privacy-Preserving Offloading of Neural Network Computations

Machine learning has become a highly utilized technology to perform decision making on high dimensional data. As dataset sizes have become increasingly large so too have the neural networks to learn the complex patterns hidden within. This expansion has continued to the degree that it may be infeasible to train a model from a singular device due to computational or memory limitations of underlying hardware. Purpose built computing clusters for training large models are commonplace while access to networks of heterogeneous devices is still typically more accessible. In addition, with the rise of 5G networks, computation at the edge becoming more commonplace, and inspired by the successes of the folding@home project utilizing crowdsourced computation, we consider the scenario of the crowdsourcing the computation required for training of a neural network particularly appealing. Distributed learning promises to bridge the widening gap between singular device performance and large-scale model computational requirements, but unfortunately, current distributed learning techniques do not maintain privacy of both the model and input with- out an accuracy or computational tradeoff. In response, we present Divide and Conquer Learning (DCL), an innovative approach that enables quantifiable privacy guarantees while offloading the computational burden of training to a network of devices. A user can divide the training computation of its neural network into neuron-sized computation tasks and dis- tribute them to devices based on their available resources. The results will be returned to the user and aggregated in an iterative process to obtain the final neural network model. To protect the privacy of the user’s data and model, shuffling is done to both the data and the neural network model before the computation task is distributed to devices. Our strict adherence to the order of operations allows a user to verify the correctness of performed computations through assigning a task to multiple devices and cross-validating their results. This can protect against network churns and detect faulty or misbehaving devices

Pathways to Drug Liberalization: Racial Justice, Public Health, and Human Rights

In our recent article, together with more than 60 of our colleagues, we outlined a proposal for drug policy reform consisting of four specific yet interrelated strategies: (1) de jure decriminalization of all psychoactive substances currently deemed illicit for personal use or possession (so-called “recreational” drugs), accompanied by harm reduction policies and initiatives akin to the Portugal model; (2) expunging criminal convictions for nonviolent offenses pertaining to the use or possession of small quantities of such drugs (and releasing those serving time for these offenses), while delivering retroactive ameliorative relief; (3) the ultimate legalization and careful regulation of currently illicit drugs; and (4) the delivery of a new “Marshall Plan” focused on community-building initiatives, expanded harm reduction programs, and social and health care support efforts (Earp et al. 2021). We were gratified to see so many thoughtful commentaries on our proposal, and we respond to them in part in this reply

Synthesis and characterisation of cationic quaternary ammonium-modified polyvinyl alcohol hydrogel beads as a drug delivery embolisation system

To extend the platform of clinically utilised chemoembolic agents based on ion-exchange systems to support the delivery of anionic drugs, a series of PVA-based beads was produced with different levels of (3-acrylamidopropyl)trimethylammonium chloride (APTA) in their formulation. The beads were characterised to confirm composition and the effect of formulation variation on physical properties was assessed. Suspension polymerisation was shown to successfully produce uniformly spherical copolymer beads with APTA content up to 60 wt%. Equilibrium water content and resistance to compression both increased with increasing APTA content in the formulation. Confocal laser scanning microscopy was used with model drugs to demonstrate that by increasing APTA content, compounds between the molecular weight range 70–250 kDa could permeate the microsphere structures. Interaction with anionic drugs was modelled using multivalent dyes. Dyes with multi-binding sites had increased interaction with the polymer, slowing the release and also demonstrating a reduced rate of elution from beads with higher charge density. The model drug release studies demonstrate that these systems can be engineered for different potential anionic drugs for local therapeutic delivery in the clinic

ANISOTROPY DETERMINATIONS IN EXCHANGE SPRING MAGNETS.

Ferromagnetic nanocomposites, or ''exchange spring'' magnets, possess a nanoscaled microstructure that allows intergrain magnetic exchange forces to couple the constituent grains and alter the system's effective magnetic anisotropies. While the effects of the anisotropy alterations are clearly seen in macroscopic magnetic measurement, it is extremely difficult to determine the detailed effects of the system's exchange coupling, such as the interphase exchange length, the inherent domain wall widths or the effective anisotropies of the system. Clarification of these materials parameters may be obtained from the ''micromagnetic'' phenomenological model, where the assumption of magnetic reversal initiating in the magnetically-soft regions of the exchange-spring maqet is explicitly included. This approach differs from that typically applied by other researchers and allows a quantitative estimate of the effective anisotropies of an exchange spring system. Hysteresis loops measured on well-characterized nanocomposite alloys based on the composition Nd{sub 2}Fe{sub 14}B + {alpha}-Fe at temperatures above the spin reorientation temperature were analyzed within the framework of the micromagnetic phenomenological model. Preliminary results indicate that the effective anisotropy constant in the material is intermediate to that of bulk {alpha}-Fe and bulk Nd{sub 2}Fe{sub 14}B and increases with decreasing temperature. These results strongly support the idea that magnetic reversal in nanocomposite systems initiates in the lower-anisotropy regions of the system, and that the soft-phase regions become exchange-hardened by virtue of their proximity to the magnetically-hard regions

Multi-locus sequence typing of Escherichia coli isolates with acquired ampC genes and ampC promoter mutations

© 2016 Elsevier Inc. Multi-locus sequence typing was used to reveal a high degree of diversity amongst the E. coli isolates with AmpC plasmid genes, and a high prevalence of the −32 mutation present

Constraining slow-roll inflation with WMAP and 2dF

We constrain slow-roll inflationary models using the recent WMAP data combined with data from the VSA, CBI, ACBAR and 2dF experiments. We find the slow-roll parameters to be $0 < \epsilon_1 < 0.032$ and $\epsilon_2 + 5.0 \epsilon_1 = 0.036 \pm 0.025$. For inflation models $V \propto \phi^{\alpha}$ we find that $\alpha< 3.9, 4.3$ at the 2$\sigma$ and $3\sigma$ levels, indicating that the $\lambda\phi^4$ model is under very strong pressure from observations. We define a convergence criterion to judge the necessity of introducing further power spectrum parameters such as the spectral index and running of the spectral index. This criterion is typically violated by models with large negative running that fit the data, indicating that the running cannot be reliably measured with present data.Comment: 8 pages RevTeX4 file with six figures incorporate

Gravitational lensing as a contaminant of the gravity wave signal in CMB

Gravity waves (GW) in the early universe generate B-type polarization in the cosmic microwave background (CMB), which can be used as a direct way to measure the energy scale of inflation. Gravitational lensing contaminates the GW signal by converting the dominant E polarization into B polarization. By reconstructing the lensing potential from CMB itself one can decontaminate the B mode induced by lensing. We present results of numerical simulations of B mode delensing using quadratic and iterative maximum-likelihood lensing reconstruction methods as a function of detector noise and beam. In our simulations we find the quadratic method can reduce the lensing B noise power by up to a factor of 7, close to the no noise limit. In contrast, the iterative method shows significant improvements even at the lowest noise levels we tested. We demonstrate explicitly that with this method at least a factor of 40 noise power reduction in lensing induced B power is possible, suggesting that T/S=10^-6 may be achievable in the absence of sky cuts, foregrounds, and instrumental systematics. While we do not find any fundamental lower limit due to lensing, we find that for high-sensitivity detectors residual lensing noise dominates over the detector noise.Comment: 6 pages, 2 figures, submitted to PR