Background Sleep disorders are experienced by up to 40% of the population but their diagnosis is often delayed by the availability of specialists. Objective We propose the use of search engine activity in conjunction with a validated web-based sleep questionnaire to facilitate wide-scale screening of prevalent sleep disorders. Methods Search advertisements offering a web-based sleep disorder screening questionnaire were shown on the Bing search engine to individuals who indicated an interest in sleep disorders. People who clicked on the advertisements and completed the sleep questionnaire were identified as being at risk for 1 of 4 common sleep disorders. A machine learning algorithm was applied to previous search engine queries to predict their suspected sleep disorder, as identified by the questionnaire. Results A total of 397 users consented to participate in the study and completed the questionnaire. Of them, 132 had sufficient past query data for analysis. Our findings show that diurnal patterns of people with sleep disorders were shifted by 2-3 hours compared to those of the controls. Past query activity was predictive of sleep disorders, approaching an area under the receiver operating characteristic curve of 0.62-0.69, depending on the sleep disorder. Conclusions Targeted advertisements can be used as an initial screening tool for people with sleep disorders. However, search engine data are seemingly insufficient as a sole method for screening. Nevertheless, we believe that evaluable web-based information, easily collected and processed with little effort on part of the physician and with low burden on the individual, can assist in the diagnostic process and possibly drive people to seek sleep assessment and diagnosis earlier than they currently do.

We continuously interact with computerized systems to achieve goals and perform tasks in our personal and professional lives. Therefore, the ability to program such systems is a skill needed by everyone. Consequently, computational thinking skills are essential for everyone, which creates a challenge for the educational system to teach these skills at scale and allow students to practice these skills. To address this challenge, we present a novel approach to providing formative feedback to students on programming assignments. Our approach uses dynamic evaluation to trace intermediate results generated by student’s code and compares them to the reference implementation provided by their teachers. We have implemented this method as a Python library and demonstrate its use to give students relevant feedback on their work while allowing teachers to challenge their students’ computational thinking skills.

Empirical studies show that epidemiological models based on an epidemic’s initial spread rate often fail to predict the true scale of that epidemic. Most epidemics with a rapid early rise die out before affecting a significant fraction of the population, whereas the early pace of some pandemics is rather modest. Recent models suggest that this could be due to the heterogeneity of the target population’s susceptibility. We study a computer malware ecosystem exhibiting spread mechanisms resembling those of biological systems while offering details unavailable for human epidemics. Rather than comparing models, we directly estimate reach from a new and vastly more complete data from a parallel domain, that offers superior details and insight as concerns biological outbreaks. We find a highly heterogeneous distribution of computer susceptibilities, with nearly all outbreaks initially over-affecting the tail of the distribution, then collapsing quickly once this tail is depleted. This mechanism restricts the correlation between an epidemic’s initial growth rate and its total reach, thus preventing the majority of epidemics, including initially fast-growing outbreaks, from reaching a macroscopic fraction of the population. The few pervasive malwares distinguish themselves early on via the following key trait: they avoid infecting the tail, while preferentially targeting computers unaffected by typical malware.

Understanding the dynamics of infectious disease spread in a heterogeneous population is an important factor in designing control strategies. Here, we present a tensor-driven multi-compartment version of the classic Susceptible–Infected–Recovered (SIR) model and apply it to Internet data to reveal information about the complex spatial structure of disease spread. We develop an algorithm to estimate the model’s parameters in a high dimensional setting. The model and the algorithm are used to analyze state-level Google search data from the US pertaining to two viruses, Respiratory Syncytial Virus (RSV), and West Nile Virus (WNV), independently. We fit the data with correlations of R2=0.70, and 0.52 for RSV and WNV, respectively. Although no prior assumptions on spatial structure are made, human movement patterns in the US explain 27%–30% of the estimated inter-state transmission rates. The transmission rates within states are correlated with known demographic indicators, such as population density and average age. Finally, we show that the patterns of disease load for subsequent season can be predicted using the model parameters estimated for previous seasons and as few as 7 weeks of data from the current season. Our results are applicable to other countries and similar viruses, allowing the identification of disease spread parameters and prediction of disease load for seasonal viruses earlier in season.

Computer malware and biological pathogens often use similar mechanisms of infections. For this reason, it has been suggested to model malware spread using epidemiological models developed to characterize the spread of biological pathogens. However, most work examining the similarities between malware and pathogens using such methods was based on theoretical analysis and simulation. Here we extend the classical Susceptible-Infected-Recovered (SIR) epidemiological model to describe two of the most common infection methods used by malware. We fit the proposed model to malware collected over a period of one year from a major anti-malware vendor. We show that by fitting the proposed model it is possible to identify the method of transmission used by the malware, its rate of infection, and the number of machines which will be infected unless blocked by anti-virus software. The Spearman correlation between the number of actual and predicted infected machines is 0.84. Examining cases where an anti-malware signature was transmitted to susceptible computers by the anti-virus provider, we show that the time to remove the malware will be short and independent of the number of infected computers if fewer than approximately 60% of susceptible computers have been infected. If more computers were infected, the time to removal will be approximately 3.2 greater, and will depend on the fraction of infected computers. Our results show that the application of epidemiological models of infection to malware can provide anti-virus providers with information on malware spread and its potential damage. We further propose that similarities between computer malware and biological pathogens, the availability of data on the former and the dearth of data on the latter, make malware an extremely useful model for testing interventions which could later be applied to improve medicine.

We develop a multiple compartment Susceptible-Infected-Recovered (SIR) model to analyze the spread of several infectious diseases through different geographic areas. Additionally, we propose a data-quality sensitive optimization framework for fitting this model to observed data. We fit the model to the temporal profile of the number of people infected by one of six influenza strains in Europe over $7$ influenza seasons. In addition to describing the temporal and spatial spread of influenza, the model provides an estimate of the inter-country and intra-country infection and recovery rates of each strain and in each season. We find that disease parameters remain relatively stable, with a correlation greater than $0.5$ over seasons and stains. Clustering of influenza strains by the inferred disease parameters is consistent with genome sub-types. Surprisingly, our analysis suggests that inter-country human mobility plays a negligible role in the spread of influenza in Europe. Finally, we show that the model allows the estimation of disease load in countries with poor or none existent data from the disease load in adjacent countries. Our findings reveal information on the spreading mechanism of influenza and on disease parameters. These can be used to assist in disease surveillance and in control of influenza as well as of other infectious pathogens in a heterogenic environment.

Epidemiological models for the spread of pathogens in a population are usually only able to describe a single pathogen. This makes their application unrealistic in cases where multiple pathogens with similar symptoms are spreading concurrently within the same population. Here we describe a method which makes possible the application of multiple single-strain models under minimal conditions. As such, our method provides a bridge between theoretical models of epidemiology and data-driven approaches for modeling of influenza and other similar viruses. Our model extends the Susceptible-Infected-Recovered model to higher dimensions, allowing the modeling of a population infected by multiple viruses. We further provide a method, based on an overcomplete dictionary of feasible realizations of SIR solutions, to blindly partition the time series representing the number of infected people in a population into individual components, each representing the effect of a single pathogen. We demonstrate the applicability of our proposed method on five years of seasonal influenza-like illness (ILI) rates, estimated from Twitter data. We demonstrate that our method describes, on average, 44% of the variance in the ILl time series. The individual infectious components derived from our model are matched to known viral profiles in the populations, which we demonstrate matches that of independently collected epidemiological data. We further show that the basic reproductive numbers (RO) of the matched components are in range known for these pathogens. Our results suggest that the proposed method can be applied to other pathogens and geographies, providing a simple method for estimating the parameters of epidemics in a population. (C) 2017 Elsevier B.V. All rights reserved.

We study the online problem of multiplexing packets with arbitrary deadlines in bounded multi-buffer switch. In this model, a switch consists of m input buffers each with bounded capacity B and one output port. Each arriving packet is associated with a value and a deadline that specifies the time limit till the packet can be transmitted. At each time step the switch can select any non-empty buffer and transmit one packet from that buffer. In the preemptive model, stored packets may be preempted from their buffers due to lack of buffer space or discarded due to the violation of the deadline constraints. If preemption is not allowed, every packet accepted and stored in the buffer must be transmitted before its deadline has expired. The goal is to maximize the benefit of the packets transmitted by their deadlines. To date, most models for packets with deadlines assumed a single buffer. To the best of our knowledge this is the first time a bounded multi-buffer switch is used with arbitrary deadline constraints.Our main result is a 9.82-competitive deterministic algorithm for packets with arbitrary values and deadlines. Note that the greedy algorithm is not competitive. For the non-preemptive model we present a 2-competitive deterministic algorithm for the unit value packets. For arbitrary values we present a randomized algorithm whose competitiveness is logarithmic in the ratio between the largest and the smallest value of the packets in the sequence.

A novel computational model of a pre-attentive system performing visual-search is presented. The model processes various types of displays, reproduced from three sources of visual-search experimental data: Duncan and Humphreys, Psychol. Rev. 96 (1989) 453–458, Treisman and Sato, J. Exp. Psuchol. 16 (1990) 459–478, and Wolfe, Friedman-Hill, Stewart, O'Connell, J. Exp. Psychol. 18 (1992) 34–49. The response–time-slopes measured in these experiments suggest that some of the displays are searched serially while others are scanned in parallel. Our model operates in two phases. First, the visual-search displays are compressed to overcome assumed biological capacity limitations. Compression is achieved by projecting the tasks' displays on a small set of feature maps. These features have been extracted from a large set of natural images by means of principal component analysis. Second, the compressed representations are further processed to identify a target in the display. The model succeeds in fast detection of targets in experimentally labeled parallel displays, but fails with serial ones. Analysis of the compressed representations reveals that compressed parallel displays contain global information that enables instantaneous target detection. However, in serial displays' representations, this global information is obscure and hence, a target detection system should resort to a serial, attentional scan of local features across the display. Our analysis provides a numerical criterion that is strongly correlated with the experimental response–time-slopes. It also provides new insight to the mechanisms of visual-attention, suggesting a self-organized representation of Treisman's feature maps, which may be implemented in other paradigms in the field.

Recently, it has been shown that there exists a temporal synaptic learning curve, composed of potentiation for short time delays between pre- and post-synaptic neuronal spiking, and depression for spiking events occurring in the opposite order. We investigate the effect that such a curve can have on the temporal realization of an attractor in an associative memory network of spiking neurons. We find that an interesting result of this learning paradigm is the formation of distributed synchrony, i.e. spontaneous division of the Hebbian cell assembly into groups of cells that fire in a cyclic manner.

We investigate the behavior of a Hebbian cell assembly of spiking neurons formed via a temporal synaptic learning curve. This learning function is based on recent experimental findings. It includes potentiation for short time delays between pre - and postsynaptic neuronal spiking, and depression for spiking events occuring in the reverse order. The coupling between the dynamics of the synaptic learning and of the neuronal activation leads to interesting results. We find that the cell assembly can fire asynchronously, but may also function in complete synchrony, or in distributed synchrony. The latter implies spontaneous division of the Hebbian cell assembly into groups of cells that fire in a cyclic manner. We invetigate the behavior of distributed synchrony both by simulations and by analytic calculations of the resulting synaptic distributions.

We present a multi-modular approach to neural modeling of associa-tive memory. By segregating between intra-modular and inter-modular synaptic transmission, and subjecting the latter to non-linear dendritic processing, we can successfully store memories encoded on diierent numbers of modules. This model has a striking capability of memory retrieval from partial inputs when the appropriate neurons in only few modules, or even a single module, are activated by aaerent connections. Hence, if modular aaerents are lesioned, memories that are encoded in more modules are more resilient to damage. Assuming that memories of concrete objects are encoded in a larger number of modules than abstract ones, our results provide a novel explanation of the phenomenon of category speciic semantic impairments in patients with deep acquired dyslexia.

We investigate the behavior of a Hebbian cell assembly of spiking neurons formed via a temporal synaptic learning curve. This learning function is based on recent experimental findings. It includes potentiation for short time delays between pre- and post-synaptic neuronal spiking, and depression for spiking events occuring in the reverse order. The coupling between the dynamics of the synaptic learning and of the neuronal activation leads to interesting results. We find that the cell assembly can fire asynchronously, but may also function in complete synchrony, or in distributed synchrony. The latter implies spontaneous division of the Hebbian cell assembly into groups of cells that fire in a cyclic manner. We invetigate the behavior of distributed synchrony both by simulations and by analytic calculations of the resulting synaptic distributions.

Recent imaging studies suggest that object knowledge is stored in the brain as a distributed network of many cortical areas. Motivated by these observations, we study a multimodular associative memory network, whose functional goal is to store patterns with different coding levels-patterns that vary in the number of modules in which they are encoded. We show that in order to accomplish this task, synaptic inputs should be segregated into intramodular projections and intermodular projections, with the latter undergoing additional nonlinear dendritic processing. This segregation makes sense anatomically if the intermodular projections represent distal synaptic connections on apical dendrites. It is then straightforward to show that memories encoded in more modules are more resilient to focal afferent damage. Further hierarchical segregation of intermodular connections on the dendritic tree improves this resilience, allowing memory retrieval from input to just one of the modules in which it is encoded.

Recent imaging studies testify that objects are stored in the brain in a distributed network of discrete cortical areas. Motivated by these observations, we study a multi-modular associative memory network, whose functional goal is to store patterns of different coding levels, i.e., patterns that vary in the number of modules in which they are encoded. We show that in order to accomplish this task, the synaptic inputs to each neuron should be segregated to intramodular projections whose activations are summed up linearly, and inter-modular projections whose activations are summed up in a nonlinear fashion. This may reflect dendritic processing of distal synaptic connections. Moreover, we find that further hierarchical segregation of inter-modular connections on the dendritic tree improves memory retrieval from partial input cues, and improves resilience of the network to afferent damage.

Motivated by the findings of modular structure in the association cortex, we study a multi-modular model of associative memory that can successfully store memory patterns with different levels of activity. We show that the segregation of synaptic conductances into intra-modular linear and inter-modular nonlinear ones considerably enhances the network's memory retrieval performance. Compared with the conventional, single-module associative memory network, the multi-modular network has two main advantages: It is less susceptible to damage to columnar input, and its response is consistent with the cognitive data pertaining to category specific impairment.

Hebbian learning, the paradigm of memory formation, needs further mechanisms to guarantee creation and maintenance of a viable memory system. One such proposed mechanism is Hebbian unlearning, a process hypothesized to occur during sleep. It can remove spurious states and eliminate global correlations in the memory system. However, the problem of spurious states is unimportant in the biologically interesting case of memories that are sparsely coded on excitatory neurons. Moreover, if some memories are anomalously strong and have to be weakened to guarantee proper functioning of the network, we show that it is advantageous to do that by neuronal regulation (NR) rather than synaptic unlearning. Neuronal regulation can account for dynamical maintenance of memory systems that undergo continuous synaptic turnover. This neuronal-based mechanism, regulating all excitatory synapses according to neuronal average activity, has recently gained strong experimental support. NR achieves synaptic maintenance over short time scales by preserving the average neuronal input field. On longer time scales it acts to maintain memories by letting the stronger synapses grow to their upper bounds. In ageing, these bounds are increased to allow stronger values of remaining synapses to overcome the loss of synapses that have perished.

Since their conception half a century ago, Hebbian cell assemblies have become a basic term in the neurosciences, and the idea that learning takes place through synaptic modifications has been accepted as a fundamental paradigm. As synapses undergo continuous metabolic turnover, adopting the stance that memories are engraved in the synaptic matrix raises a fundamental problem: How can memories be maintained for very long time periods? We present a novel solution to this long-standing question, based on biological evidence of neuronal regulation mechanisms that act to maintain neuronal activity. Our mechanism is developed within the framework of a neural model of associative memory. It is operative in conjunction with random activation of the memory system and is able to counterbalance degradation of synaptic weights and normalize the basins of attraction of all memories. Over long time periods, when the variance of the degradation process becomes important, the memory system stabilizes if its synapses are appropriately bounded. Thus, the remnant memory system is obtained by a dynamic process of synaptic selection and growth driven by neuronal regulatory mechanisms. Our model is a specific realization of dynamic stabilization of neural circuitry, which is often assumed to take place during sleep.