When and where do feed-forward neural networks learn localist representations?

According to parallel distributed processing (PDP) theory in psychology, neural networks (NN) learn distributed rather than interpretable localist representations. This view has been held so strongly that few researchers have analysed single units to determine if this assumption is correct. However, recent results from psychology, neuroscience and computer science have shown the occasional existence of local codes emerging in artificial and biological neural networks. In this paper, we undertake the first systematic survey of when local codes emerge in a feed-forward neural network, using generated input and output data with known qualities. We find that the number of local codes that emerge from a NN follows a well-defined distribution across the number of hidden layer neurons, with a peak determined by the size of input data, number of examples presented and the sparsity of input data. Using a 1-hot output code drastically decreases the number of local codes on the hidden layer. The number of emergent local codes increases with the percentage of dropout applied to the hidden layer, suggesting that the localist encoding may offer a resilience to noisy networks. This data suggests that localist coding can emerge from feed-forward PDP networks and suggests some of the conditions that may lead to interpretable localist representations in the cortex. The findings highlight how local codes should not be dismissed out of hand

International Investment Governance and Achieving a Just Zero-Carbon Future

As developing countries continue to be the most negatively affected by climate change and the energy transition, it is increasingly critical that they receive foreign direct investment and financial support to build climate resilience, adapt to climate impacts, avoid carbon lock-in and fossil fuel dependence, and leverage their rich endowments of renewable and extractive resources to prepare for the zero-carbon future. There is a disconnect and fundamental misalignment between international investment law and the international climate change regime, comprising the United Nations Framework Convention on Climate Change (UNFCCC) and the Paris Agreement. Existing investment treaties—including their centerpiece, investor–state dispute settlement (ISDS)—are hostile to states’ ability to address the climate crisis and build a zero-carbon future. Investment treaties and ISDS will deter, delay or water down states’ climate-related measures, and increase their costs for states. This briefing details how attempts to “re-balance” the international investment regime by refining investment protection and arbitration provisions do not address the fundamental misalignment of investment treaties with both climate goals and the broader sustainable development agenda. States can design treaties that support their national and global goals, reinforcing investment governance in treaties that can: Promote specific climate-aligned investment, by identifying the barriers to such investment and fostering international support; Strengthen governance of investment to minimize harms and leverage potential benefits; and Encourage and facilitate cooperation

Electrically tunable multi-terminal SQUID-on-tip

We present a new nanoscale superconducting quantum interference device (SQUID) whose interference pattern can be shifted electrically in-situ. The device consists of a nanoscale four-terminal/four-junction SQUID fabricated at the apex of a sharp pipette using a self-aligned three-step deposition of Pb. In contrast to conventional two-terminal/two-junction SQUIDs that display optimal sensitivity when flux biased to about a quarter of the flux quantum, the additional terminals and junctions allow optimal sensitivity at arbitrary applied flux, thus eliminating the magnetic field "blind spots". We demonstrate spin sensitivity of 5 to 8 $\mu_B/\text{Hz}^{1/2}$ over a continuous field range of 0 to 0.5 T, with promising applications for nanoscale scanning magnetic imaging

Are there any ‘object detectors’ in the hidden layers of CNNs trained to identify objects or scenes?

Various methods of measuring unit selectivity have been developed with the aim of better understanding how neural networks work. But the different measures provide divergent estimates of selectivity, and this has led to different conclusions regarding the conditions in which selective object representations are learned and the functional relevance of these representations. In an attempt to better characterize object selectivity, we undertake a comparison of various selectivity measures on a large set of units in AlexNet, including localist selectivity, precision, class-conditional mean activity selectivity (CCMAS), network dissection,the human interpretation of activation maximization (AM) images, and standard signal-detection measures. We find that the different measures provide different estimates of object selectivity, with precision and CCMAS measures providing misleadingly high estimates. Indeed, the most selective units had a poor hit-rate or a high false-alarm rate (or both) in object classification, making them poor object detectors. We fail to find any units that are even remotely as selective as the 'grandmother cell' units reported in recurrent neural networks. In order to generalize these results, we compared selectivity measures on units in VGG-16 and GoogLeNet trained on the ImageNet or Places-365 datasets that have been described as 'object detectors'. Again, we find poor hit-rates and high false-alarm rates for object classification. We conclude that signal-detection measures provide a better assessment of single-unit selectivity compared to common alternative approaches, and that deep convolutional networks of image classification do not learn object detectors in their hidden layers.Comment: Published in Vision Research 2020, 19 pages, 8 figure

Cost Effectiveness of Molecular Diagnostic Testing Algorithms for the Treatment Selection of Frontline Ibrutinib for Patients with Chronic Lymphocytic Leukemia in Australia

BACKGROUND: Clinical indications for ibrutinib reimbursement in Australia should consider the inclusion of patients with chronic lymphocytic leukemia (CLL) harboring prognostically unfavorable TP53/IGHV genomic aberrations. This study assessed the cost effectiveness of five first-line treatment strategies in CLL for young (aged ≤ 65 years), fit patients without significant comorbidities: (1) no testing (fludarabine, cyclophosphamide and rituximab [FCR] for all), (2) test for del(17p) only, (3) test for TP53 gene mutation status, (4) test for TP53 and IGHV gene mutation status and (5) no testing (ibrutinib for all).METHOD: A decision analytic model (decision tree and partitioned survival model) was developed from the Australian healthcare system perspective with a lifetime horizon. Comparative treatment effects were estimated from indirect treatment comparisons and survival analysis using several studies. Costs, utility and adverse events were derived from public literature sources. Deterministic and probabilistic sensitivity analyses explored the impact of modeling uncertainties on outcomes.RESULTS: Strategy 1 was associated with 5.69 quality-adjusted life-years (QALYs) and cost 458,836 Australian dollars (AUD). All other strategies had greater effectiveness but were more expensive than Strategy 1. At the willingness-to-pay (WTP) threshold of 100,000 AUD per QALY gained, Strategy 1 was most cost effective with an estimated probability of 68.8%. Strategy 4 was cost effective between thresholds 155,000-432,300 AUD per QALY gained, and Strategy 5 &gt;432,300 AUD per QALY gained.CONCLUSION: Population targeting using mutation testing for TP53 and IGHV when performed with del(17p) testing specifically in the context of frontline ibrutinib choice does not make a cost-ineffective treatment into a cost-effective treatment.</p

Use of the Research Readiness Self-Assessment (RRSA) to Evaluate Medical Students’ Competencies in Finding and Evaluating Online Health Information (Poster Presented at MHSLA Annual Conference)

To maintain awareness of current medical evidence and clinical practice guidelines in order to provide the best possible patient care, physicians must be able to locate, critically evaluate, synthesize, and make clinical decisions based on health information from multiple digital sources. Concerningly, however, medical students and junior doctors have been found to exhibit poor information literacy skills, including an inability to conduct efficient literature searches, find randomized controlled trials and systematic reviews, or evaluate study quality. Here, we describe our medical library’s experience in administering the Health version of the Research Readiness Self-Assessment (RRSA) tool to two cohorts of first-year medical students to evaluate their perceived and actual competencies in finding and critically evaluating online health information. We also describe our creation of online training modules that aim to improve medical students’ health information literacy skills, including one on using Bloom’s taxonomy to ask great research questions and another on deciding when to use various types of information sources (e.g., textbooks, journals, point-of-care clinical decision tools) to answer different types of health-related questions. Our preliminary findings suggest that first-year medical students are better at evaluating health information than they are at finding health information, although both skills show room for improvement. These findings will help guide our development of additional educational interventions to improve medical students’ research readiness

Finding the Gap:Neuromorphic Motion Vision in Cluttered Environments

Many animals meander in environments and avoid collisions. How the underlying neuronal machinery can yield robust behaviour in a variety of environments remains unclear. In the fly brain, motion-sensitive neurons indicate the presence of nearby objects and directional cues are integrated within an area known as the central complex. Such neuronal machinery, in contrast with the traditional stream-based approach to signal processing, uses an event-based approach, with events occurring when changes are sensed by the animal. Contrary to von Neumann computing architectures, event-based neuromorphic hardware is designed to process information in an asynchronous and distributed manner. Inspired by the fly brain, we model, for the first time, a neuromorphic closed-loop system mimicking essential behaviours observed in flying insects, such as meandering in clutter and gap crossing, which are highly relevant for autonomous vehicles. We implemented our system both in software and on neuromorphic hardware. While moving through an environment, our agent perceives changes in its surroundings and uses this information for collision avoidance. The agent's manoeuvres result from a closed action-perception loop implementing probabilistic decision-making processes. This loop-closure is thought to have driven the development of neural circuitry in biological agents since the Cambrian explosion. In the fundamental quest to understand neural computation in artificial agents, we come closer to understanding and modelling biological intelligence by closing the loop also in neuromorphic systems. As a closed-loop system, our system deepens our understanding of processing in neural networks and computations in biological and artificial systems. With these investigations, we aim to set the foundations for neuromorphic intelligence in the future, moving towards leveraging the full potential of neuromorphic systems.Comment: 7 main pages with two figures, including appendix 26 pages with 14 figure

International Investment Governance and Achieving a Just Zero-Carbon Future

As developing countries continue to be the most negatively affected by climate change and the energy transition, it is increasingly critical that they receive foreign direct investment and financial support to build climate resilience, adapt to climate impacts, avoid carbon lock-in and fossil fuel dependence, and leverage their rich endowments of renewable and extractive resources to prepare for the zero-carbon future. There is a disconnect and fundamental misalignment between international investment law and the international climate change regime, comprising the United Nations Framework Convention on Climate Change (UNFCCC) and the Paris Agreement. Existing investment treaties—including their centerpiece, investor-state dispute settlement (ISDS)—are hostile to states’ ability to address the climate crisis and build a zero-carbon future. Investment treaties and ISDS will deter, delay or water down states’ climate-related measures, and increase their costs for states. CCSI’s briefing "International Investment Governance and Achieving a Just Zero-Carbon Future" details how attempts to “re-balance” the international investment regime by refining investment protection and arbitration provisions do not address the fundamental misalignment of investment treaties with both climate goals and the broader sustainable development agenda

Sugary Logistics Gone Wrong:Membrane Trafficking and Congenital Disorders of Glycosylation

Contains fulltext : 225799.pdf (publisher's version ) (Open Access)Glycosylation is an important post-translational modification for both intracellular and secreted proteins. For glycosylation to occur, cargo must be transported after synthesis through the different compartments of the Golgi apparatus where distinct monosaccharides are sequentially bound and trimmed, resulting in increasingly complex branched glycan structures. Of utmost importance for this process is the intraorganellar environment of the Golgi. Each Golgi compartment has a distinct pH, which is maintained by the vacuolar H(+)-ATPase (V-ATPase). Moreover, tethering factors such as Golgins and the conserved oligomeric Golgi (COG) complex, in concert with coatomer (COPI) and soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE)-mediated membrane fusion, efficiently deliver glycosylation enzymes to the right Golgi compartment. Together, these factors maintain intra-Golgi trafficking of proteins involved in glycosylation and thereby enable proper glycosylation. However, pathogenic mutations in these factors can cause defective glycosylation and lead to diseases with a wide variety of symptoms such as liver dysfunction and skin and bone disorders. Collectively, this group of disorders is known as congenital disorders of glycosylation (CDG). Recent technological advances have enabled the robust identification of novel CDGs related to membrane trafficking components. In this review, we highlight differences and similarities between membrane trafficking-related CDGs

Direct interpretation of the X-ray and neutron three-dimensional difference pair distribution functions (3D-ΔPDFs) of yttria-stabilized zirconia

Three-dimensional difference pair distribution functions (3D-ΔPDFs) from X-ray and neutron diffraction experiments are reported for yttria-stabilized zirconia (Zr0.82Y0.18O1.91). A quantitative analysis of the signatures in the three-dimensional difference pair distribution functions is used to establish that oxygen ions neighbouring a vacancy shift by 0.525 (5) Å along ⟨1, 0, 0⟩ towards the vacancy while metal ions neighbouring a vacancy shift by 0.465 (2) Å along ⟨1, 1, 1⟩ away from the vacancy. The neutron 3D-ΔPDF shows a tendency for vacancies to cluster along ⟨½, ½, ½⟩, which results in sixfold coordinated metal ions