Parameterized Compilation Lower Bounds for Restricted CNF-formulas

We show unconditional parameterized lower bounds in the area of knowledge compilation, more specifically on the size of circuits in decomposable negation normal form (DNNF) that encode CNF-formulas restricted by several graph width measures. In particular, we show that - there are CNF formulas of size $n$ and modular incidence treewidth $k$ whose smallest DNNF-encoding has size $n^{\Omega(k)}$, and - there are CNF formulas of size $n$ and incidence neighborhood diversity $k$ whose smallest DNNF-encoding has size $n^{\Omega(\sqrt{k})}$. These results complement recent upper bounds for compiling CNF into DNNF and strengthen---quantitatively and qualitatively---known conditional low\-er bounds for cliquewidth. Moreover, they show that, unlike for many graph problems, the parameters considered here behave significantly differently from treewidth

Using Open Source Libraries in the Development of Control Systems Based on Machine Vision

The possibility of the boundaries detection in the images of crushed ore particles using a convolutional neural network is analyzed. The structure of the neural network is given. The construction of training and test datasets of ore particle images is described. Various modifications of the underlying neural network have been investigated. Experimental results are presented. © 2020, IFIP International Federation for Information Processing.Foundation for Assistance to Small Innovative Enterprises in Science and Technology, FASIEFunding. The work was performed under state contract 3170ΓC1/48564, grant from the FASIE

Bit-Vector Model Counting using Statistical Estimation

Approximate model counting for bit-vector SMT formulas (generalizing \#SAT) has many applications such as probabilistic inference and quantitative information-flow security, but it is computationally difficult. Adding random parity constraints (XOR streamlining) and then checking satisfiability is an effective approximation technique, but it requires a prior hypothesis about the model count to produce useful results. We propose an approach inspired by statistical estimation to continually refine a probabilistic estimate of the model count for a formula, so that each XOR-streamlined query yields as much information as possible. We implement this approach, with an approximate probability model, as a wrapper around an off-the-shelf SMT solver or SAT solver. Experimental results show that the implementation is faster than the most similar previous approaches which used simpler refinement strategies. The technique also lets us model count formulas over floating-point constraints, which we demonstrate with an application to a vulnerability in differential privacy mechanisms

Improving MCS Enumeration via Caching

Enumeration of minimal correction sets (MCSes) of conjunctive normal form formulas is a central and highly intractable problem in infeasibility analysis of constraint systems. Often complete enumeration of MCSes is impossible due to both high computational cost and worst-case exponential number of MCSes. In such cases partial enumeration is sought for, finding applications in various domains, including axiom pinpointing in description logics among others. In this work we propose caching as a means of further improving the practical efficiency of current MCS enumeration approaches, and show the potential of caching via an empirical evaluation.Peer reviewe

A Bayesian approach to modelling heterogeneous calcium responses in cell populations

Calcium responses have been observed as spikes of the whole-cell calcium concentration in numerous cell types and are essential for translating extracellular stimuli into cellular responses. While there are several suggestions for how this encoding is achieved, we still lack a comprehensive theory. To achieve this goal it is necessary to reliably predict the temporal evolution of calcium spike sequences for a given stimulus. Here, we propose a modelling framework that allows us to quantitatively describe the timing of calcium spikes. Using a Bayesian approach, we show that Gaussian processes model calcium spike rates with high fidelity and perform better than standard tools such as peri-stimulus time histograms and kernel smoothing. We employ our modelling concept to analyse calcium spike sequences from dynamically-stimulated HEK293T cells. Under these conditions, different cells often experience diverse stimuli time courses, which is a situation likely to occur in vivo. This single cell variability and the concomitant small number of calcium spikes per cell pose a significant modelling challenge, but we demonstrate that Gaussian processes can successfully describe calcium spike rates in these circumstances. Our results therefore pave the way towards a statistical description of heterogeneous calcium oscillations in a dynamic environmen

Evaluation of a nudge intervention providing simple feedback to clinicians of the consequence of radiation exposure on demand for computed tomography: a controlled study

Computerised tomography (CT) is readily available in developed countries. As one of the side-effects includes an increased risk of cancer, interventions that may encourage more judicious use of CT scans are important. Behavioural economics theory includes the use of nudges that aim to help more informed decisions to be made, although these have been rarely used in hospitals to date. We aimed to evaluate the impact of a simple educational message appended to the CT scan report on subsequent numbers of CT scans completed using a controlled interrupted time series design based in two teaching hospitals in X. The intervention was the addition of a non-directional educational message on the risk of ionising radiation to all CT scan reports. There was a statistically significant reduction in the number of CT scans requested in the intervention hospital compared to the control hospital (-4.6%, 95% confidence intervals -7.4 to -1.7, p = 0.002) in the 12 months after the intervention was implemented. We conclude that a simple, non-directional nudge intervention has the capacity to modify clinician use of CT scans. This approach is cheap, and has potential in helping support doctors make informed decisions

Disentangling astroglial physiology with a realistic cell model in silico

Electrically non-excitable astroglia take up neurotransmitters, buffer extracellular K+ and generate Ca2+ signals that release molecular regulators of neural circuitry. The underlying machinery remains enigmatic, mainly because the sponge-like astrocyte morphology has been difficult to access experimentally or explore theoretically. Here, we systematically incorporate multi-scale, tri-dimensional astroglial architecture into a realistic multi-compartmental cell model, which we constrain by empirical tests and integrate into the NEURON computational biophysical environment. This approach is implemented as a flexible astrocyte-model builder ASTRO. As a proof-of-concept, we explore an in silico astrocyte to evaluate basic cell physiology features inaccessible experimentally. Our simulations suggest that currents generated by glutamate transporters or K+ channels have negligible distant effects on membrane voltage and that individual astrocytes can successfully handle extracellular K+ hotspots. We show how intracellular Ca2+ buffers affect Ca2+ waves and why the classical Ca2+ sparks-and-puffs mechanism is theoretically compatible with common readouts of astroglial Ca2+ imaging

A Dopaminergic Gene Cluster in the Prefrontal Cortex Predicts Performance Indicative of General Intelligence in Genetically Heterogeneous Mice

Background: Genetically heterogeneous mice express a trait that is qualitatively and psychometrically analogous to general intelligence in humans, and as in humans, this trait co-varies with the processing efficacy of working memory (including its dependence on selective attention). Dopamine signaling in the prefrontal cortex (PFC) has been established to play a critical role in animals ’ performance in both working memory and selective attention tasks. Owing to this role of the PFC in the regulation of working memory, here we compared PFC gene expression profiles of 60 genetically diverse CD-1 mice that exhibited a wide range of general learning abilities (i.e., aggregate performance across five diverse learning tasks). Methodology/Principal Findings: Animals ’ general cognitive abilities were first determined based on their aggregate performance across a battery of five diverse learning tasks. With a procedure designed to minimize false positive identifications, analysis of gene expression microarrays (comprised of <25,000 genes) identified a small number (,20) of genes that were differentially expressed across animals that exhibited fast and slow aggregate learning abilities. Of these genes, one functional cluster was identified, and this cluster (Darpp-32, Drd1a, and Rgs9) is an established modulator of dopamine signaling. Subsequent quantitative PCR found that expression of these dopaminegic genes plus one vascular gene (Nudt6) were significantly correlated with individual animal’s general cognitive performance. Conclusions/Significance: These results indicate that D1-mediated dopamine signaling in the PFC, possibly through it

Ultrasound-guided diagnostic breast biopsy methodology: retrospective comparison of the 8-gauge vacuum-assisted biopsy approach versus the spring-loaded 14-gauge core biopsy approach

<p>Abstract</p> <p>Background</p> <p>Ultrasound-guided diagnostic breast biopsy technology represents the current standard of care for the evaluation of indeterminate and suspicious lesions seen on diagnostic breast ultrasound. Yet, there remains much debate as to which particular method of ultrasound-guided diagnostic breast biopsy provides the most accurate and optimal diagnostic information. The aim of the current study was to compare and contrast the 8-gauge vacuum-assisted biopsy approach and the spring-loaded 14-gauge core biopsy approach.</p> <p>Methods</p> <p>A retrospective analysis was done of all ultrasound-guided diagnostic breast biopsy procedures performed by either the 8-gauge vacuum-assisted biopsy approach or the spring-loaded 14-gauge core biopsy approach by a single surgeon from July 2001 through June 2009.</p> <p>Results</p> <p>Among 1443 ultrasound-guided diagnostic breast biopsy procedures performed, 724 (50.2%) were by the 8-gauge vacuum-assisted biopsy technique and 719 (49.8%) were by the spring-loaded 14-gauge core biopsy technique. The total number of false negative cases (i.e., benign findings instead of invasive breast carcinoma) was significantly greater (P = 0.008) in the spring-loaded 14-gauge core biopsy group (8/681, 1.2%) as compared to in the 8-gauge vacuum-assisted biopsy group (0/652, 0%), with an overall false negative rate of 2.1% (8/386) for the spring-loaded 14-gauge core biopsy group as compared to 0% (0/148) for the 8-gauge vacuum-assisted biopsy group. Significantly more (P < 0.001) patients in the spring-loaded 14-gauge core biopsy group (81/719, 11.3%) than in the 8-gauge vacuum-assisted biopsy group (18/724, 2.5%) were recommended for further diagnostic surgical removal of additional tissue from the same anatomical site of the affected breast in an immediate fashion for indeterminate/inconclusive findings seen on the original ultrasound-guided diagnostic breast biopsy procedure. Significantly more (P < 0.001) patients in the spring-loaded 14-gauge core biopsy group (54/719, 7.5%) than in the 8-gauge vacuum-assisted biopsy group (9/724, 1.2%) personally requested further diagnostic surgical removal of additional tissue from the same anatomical site of the affected breast in an immediate fashion for a benign finding seen on the original ultrasound-guided diagnostic breast biopsy procedure.</p> <p>Conclusions</p> <p>In appropriately selected cases, the 8-gauge vacuum-assisted biopsy approach appears to be advantageous to the spring-loaded 14-gauge core biopsy approach for providing the most accurate and optimal diagnostic information.</p

Characterization of K-Complexes and Slow Wave Activity in a Neural Mass Model

NREM sleep is characterized by two hallmarks, namely K-complexes (KCs) during sleep stage N2 and cortical slow oscillations (SOs) during sleep stage N3. While the underlying dynamics on the neuronal level is well known and can be easily measured, the resulting behavior on the macroscopic population level remains unclear. On the basis of an extended neural mass model of the cortex, we suggest a new interpretation of the mechanisms responsible for the generation of KCs and SOs. As the cortex transitions from wake to deep sleep, in our model it approaches an oscillatory regime via a Hopf bifurcation. Importantly, there is a canard phenomenon arising from a homoclinic bifurcation, whose orbit determines the shape of large amplitude SOs. A KC corresponds to a single excursion along the homoclinic orbit, while SOs are noise-driven oscillations around a stable focus. The model generates both time series and spectra that strikingly resemble real electroencephalogram data and points out possible differences between the different stages of natural sleep