Sequential presentation protects working memory from catastrophic interference

Neural network models of memory are notorious for catastrophic interference: old items are forgotten as new items are memorized (e.g., French, 1999; McCloskey & Cohen, 1989). While Working Memory (WM) in human adults shows severe capacity limitations, these capacity limitations do not reflect neural-network style catastrophic interference. However, our ability to quickly apprehend the numerosity of small sets of objects (i.e., subitizing) does show catastrophic capacity limitations, and this subitizing capacity and WM might reflect a common capacity. Accordingly, computational investigations (Knops, Piazza, Sengupta, Eger, & Melcher, 2014; Sengupta, Surampudi, & Melcher, 2014) suggest that mutual inhibition among neurons can explain both kinds of capacity limitations as well as why our ability to estimate the numerosity of larger sets is limited according to a Weber ratio signature. Based on simulations with a saliency map-like network and mathematical proofs, we provide three results. First, mutual inhibition among neurons leads to catastrophic interference when items are presented simultaneously. The network can remember a limited number of items, but when more items are presented, the network forgets all of them. Second, if memory items are presented sequentially rather than simultaneously, the network remembers the most recent items rather than forgetting all of them. Hence, the tendency in WM tasks to sequentially attend even to simultaneously presented items might not only reflect attentional limitations, but an adaptive strategy to avoid catastrophic interference. Third, the mean activation level in the network can be used to estimate the number of items in small sets, but does not accurately reflect the number of items in larger sets. Rather, we suggest that the Weber ratio signature of large number discrimination emerges naturally from the interaction between the limited precision of a numeric estimation system and a multiplicative gain control mechanism

Landauer's principle in multipartite open quantum system dynamics

We investigate the link between information and thermodynamics embodied by Landauer's principle in the open dynamics of a multipartite quantum system. Such irreversible dynamics is described in terms of a collisional model with a finite temperature reservoir. We demonstrate that Landauer's principle holds, for such a configuration, in a form that involves the flow of heat dissipated into the environment and the rate of change of the entropy of the system. Quite remarkably, such a principle for {\it heat and entropy power} can be explicitly linked to the rate of creation of correlations among the elements of the multipartite system and, in turn, the non-Markovian nature of their reduced evolution. Such features are illustrated in two exemplary cases.Comment: 5 pages, 3 figures, RevTeX4-1; Accepted for publication in Phys. Rev. Let

Disruptive Effects of the Coronavirus – Errors of Commission and of Omission?

It is increasingly evident that the coronavirus disease, COVID-19, is more than a health problem; it is and will continue to adversely affect work and workplaces, education, families and social engagements, political and environmental dimensions, and financial indicators. Apart from its health ramifications, the crisis is revealing serious challenges in the global supply chain. Those difficulties are, at least in part, consequences of unwise, short-sighted business decisions made over the course of decades to outsource and downsize

Algorithmic Bayesian Persuasion

Persuasion, defined as the act of exploiting an informational advantage in order to effect the decisions of others, is ubiquitous. Indeed, persuasive communication has been estimated to account for almost a third of all economic activity in the US. This paper examines persuasion through a computational lens, focusing on what is perhaps the most basic and fundamental model in this space: the celebrated Bayesian persuasion model of Kamenica and Gentzkow. Here there are two players, a sender and a receiver. The receiver must take one of a number of actions with a-priori unknown payoff, and the sender has access to additional information regarding the payoffs. The sender can commit to revealing a noisy signal regarding the realization of the payoffs of various actions, and would like to do so as to maximize her own payoff assuming a perfectly rational receiver. We examine the sender's optimization task in three of the most natural input models for this problem, and essentially pin down its computational complexity in each. When the payoff distributions of the different actions are i.i.d. and given explicitly, we exhibit a polynomial-time (exact) algorithm, and a "simple" $(1-1/e)$-approximation algorithm. Our optimal scheme for the i.i.d. setting involves an analogy to auction theory, and makes use of Border's characterization of the space of reduced-forms for single-item auctions. When action payoffs are independent but non-identical with marginal distributions given explicitly, we show that it is #P-hard to compute the optimal expected sender utility. Finally, we consider a general (possibly correlated) joint distribution of action payoffs presented by a black box sampling oracle, and exhibit a fully polynomial-time approximation scheme (FPTAS) with a bi-criteria guarantee. We show that this result is the best possible in the black-box model for information-theoretic reasons

Our Wicked Problem

The Coronavirus is more than a health problem. It is a “wicked” problem disrupting work, education, travel, politics, financial indicators, and more. This label came about in 1973 to help describe a special class of situations that are volatile, uncertain and ambiguous, often difficult to recognize, and difficult or impossible to solve because of incomplete, contradictory, and changing requirements. There is no clear problem definition due to interdependencies so the problem cannot be fully understood until after the solution comes about

Can we improve the prediction of hip fracture by assessing bone structure using shape and appearance modelling?

Copyright 2013 Elsevier B.V., All rights reserved.Peer reviewedPreprin

DeeSIL: Deep-Shallow Incremental Learning

Incremental Learning (IL) is an interesting AI problem when the algorithm is assumed to work on a budget. This is especially true when IL is modeled using a deep learning approach, where two com- plex challenges arise due to limited memory, which induces catastrophic forgetting and delays related to the retraining needed in order to incorpo- rate new classes. Here we introduce DeeSIL, an adaptation of a known transfer learning scheme that combines a fixed deep representation used as feature extractor and learning independent shallow classifiers to in- crease recognition capacity. This scheme tackles the two aforementioned challenges since it works well with a limited memory budget and each new concept can be added within a minute. Moreover, since no deep re- training is needed when the model is incremented, DeeSIL can integrate larger amounts of initial data that provide more transferable features. Performance is evaluated on ImageNet LSVRC 2012 against three state of the art algorithms. Results show that, at scale, DeeSIL performance is 23 and 33 points higher than the best baseline when using the same and more initial data respectively

Enhancing electrochemical intermediate solvation through electrolyte anion selection to increase nonaqueous Li-O2_2 battery capacity

Among the 'beyond Li-ion' battery chemistries, nonaqueous Li-O$_2$ batteries have the highest theoretical specific energy and as a result have attracted significant research attention over the past decade. A critical scientific challenge facing nonaqueous Li-O$_2$ batteries is the electronically insulating nature of the primary discharge product, lithium peroxide, which passivates the battery cathode as it is formed, leading to low ultimate cell capacities. Recently, strategies to enhance solubility to circumvent this issue have been reported, but rely upon electrolyte formulations that further decrease the overall electrochemical stability of the system, thereby deleteriously affecting battery rechargeability. In this study, we report that a significant enhancement (greater than four-fold) in Li-O$_2$ cell capacity is possible by appropriately selecting the salt anion in the electrolyte solution. Using $^7$Li nuclear magnetic resonance and modeling, we confirm that this improvement is a result of enhanced Li$^+$ stability in solution, which in turn induces solubility of the intermediate to Li$_2$O$_2$ formation. Using this strategy, the challenging task of identifying an electrolyte solvent that possesses the anti-correlated properties of high intermediate solubility and solvent stability is alleviated, potentially providing a pathway to develop an electrolyte that affords both high capacity and rechargeability. We believe the model and strategy presented here will be generally useful to enhance Coulombic efficiency in many electrochemical systems (e.g. Li-S batteries) where improving intermediate stability in solution could induce desired mechanisms of product formation.Comment: 22 pages, 5 figures and Supporting Informatio

Towards the production of radiotherapy treatment shells on 3D printers using data derived from DICOM CT and MRI: preclinical feasibility studies

Background: Immobilisation for patients undergoing brain or head and neck radiotherapy is achieved using perspex or thermoplastic devices that require direct moulding to patient anatomy. The mould room visit can be distressing for patients and the shells do not always fit perfectly. In addition the mould room process can be time consuming. With recent developments in three-dimensional (3D) printing technologies comes the potential to generate a treatment shell directly from a computer model of a patient. Typically, a patient requiring radiotherapy treatment will have had a computed tomography (CT) scan and if a computer model of a shell could be obtained directly from the CT data it would reduce patient distress, reduce visits, obtain a close fitting shell and possibly enable the patient to start their radiotherapy treatment more quickly. Purpose: This paper focuses on the first stage of generating the front part of the shell and investigates the dosimetric properties of the materials to show the feasibility of 3D printer materials for the production of a radiotherapy treatment shell. Materials and methods: Computer algorithms are used to segment the surface of the patient’s head from CT and MRI datasets. After segmentation approaches are used to construct a 3D model suitable for printing on a 3D printer. To ensure that 3D printing is feasible the properties of a set of 3D printing materials are tested. Conclusions: The majority of the possible candidate 3D printing materials tested result in very similar attenuation of a therapeutic radiotherapy beam as the Orfit soft-drape masks currently in use in many UK radiotherapy centres. The costs involved in 3D printing are reducing and the applications to medicine are becoming more widely adopted. In this paper we show that 3D printing of bespoke radiotherapy masks is feasible and warrants further investigation