Faulty Behavior of Storage Elements and Its Effects on Sequential Circuits

It is often assumed that the faults in storage elements (SEs) can be modeled as output/input stuck-at faults of the element. They are implicitly considered equivalent to the stuck-at faults in the combinational logic surrounding the SE cells. Transistor-level faults in common SEs are examined here. A more accurate higher level fault model for elementary SEs that better represents the physical failures is presented. It is shown that a minimal (stuck-at) model may be adequate if only modest fault coverage is desired. The enhanced model includes some common fault behaviors of SEs that are not covered by the minimal fault model. These include data-feedthrough and clock-feedthrough behaviors, as well as problems with logic level retention. Fault models for complex SE cells can be obtained without a significant loss of information about the structure of the circuit. The detectability of feedthrough faults is considered

On Fault Modeling and Testing of Content-addressable Memories

Associative or content addressable memories can be used for many computing applications. This paper discusses fault modeling for the content addressable memory (CAM) chips. Detailed examination of a single CAM cell is presented. A functional fault model for a CAM architecture executing exact match derived from the single cell model is presented. An efficient testing strategy can be derived using the proposed fault mode

The Minimum Shared Edges Problem on Grid-like Graphs

We study the NP-hard Minimum Shared Edges (MSE) problem on graphs: decide whether it is possible to route $p$ paths from a start vertex to a target vertex in a given graph while using at most $k$ edges more than once. We show that MSE can be decided on bounded (i.e. finite) grids in linear time when both dimensions are either small or large compared to the number $p$ of paths. On the contrary, we show that MSE remains NP-hard on subgraphs of bounded grids. Finally, we study MSE from a parametrised complexity point of view. It is known that MSE is fixed-parameter tractable with respect to the number $p$ of paths. We show that, under standard complexity-theoretical assumptions, the problem parametrised by the combined parameter $k$, $p$, maximum degree, diameter, and treewidth does not admit a polynomial-size problem kernel, even when restricted to planar graphs

The Complexity of Routing with Few Collisions

We study the computational complexity of routing multiple objects through a network in such a way that only few collisions occur: Given a graph $G$ with two distinct terminal vertices and two positive integers $p$ and $k$, the question is whether one can connect the terminals by at least $p$ routes (e.g. paths) such that at most $k$ edges are time-wise shared among them. We study three types of routes: traverse each vertex at most once (paths), each edge at most once (trails), or no such restrictions (walks). We prove that for paths and trails the problem is NP-complete on undirected and directed graphs even if $k$ is constant or the maximum vertex degree in the input graph is constant. For walks, however, it is solvable in polynomial time on undirected graphs for arbitrary $k$ and on directed graphs if $k$ is constant. We additionally study for all route types a variant of the problem where the maximum length of a route is restricted by some given upper bound. We prove that this length-restricted variant has the same complexity classification with respect to paths and trails, but for walks it becomes NP-complete on undirected graphs

Data-Feedthrough Faults in Circuits using Unclocked Storage Elements

Some faults in storage elements (SEs) do not manifest as stuck-at-0/1 faults. These include data-feedthrough faults that cause the SE cell to exhibit combinational behaviour. The authors investigate the implications of such faults on the behaviour of circuits using unclocked SEs. It is shown that effects of data-feedthrough faults at the behavioural level are different from those due to stuck-at faults, and therefore tests generated for the latter may be inadequat

Oxygen and Carbon Dioxide Rhythms Are Circadian Clock Controlled and Differentially Directed by Behavioral Signals

This is the author accepted manuscript. The final version is available from Elsevier (Cell Press).Daily rhythms in animal physiology are driven by endogenous circadian clocks in part through rest-activity and feeding-fasting cycles. Here, we examined principles that govern daily respiration. We monitored oxygen consumption and carbon dioxide release, as well as tissue oxygenation in freely moving animals to specifically dissect the role of circadian clocks and feeding time on daily respiration. We found that daily rhythms in oxygen and carbon dioxide are clock controlled and that time-restricted feeding restores their rhythmicity in clock-deficient mice. Remarkably, day-time feeding dissociated oxygen rhythms from carbon dioxide oscillations, whereby oxygen followed activity, and carbon dioxide was shifted and aligned with food intake. In addition, changes in carbon dioxide levels altered clock gene expression and phase shifted the clock. Collectively, our findings indicate that oxygen and carbon dioxide rhythms are clock controlled and feeding regulated and support a potential role for carbon dioxide in phase resetting peripheral clocks upon feeding.British Heart FoundationEuropean Research CouncilEuropean Union, Seventh Framework Program, Marie Curie Action

Artificial intelligence-based analysis of whole-body bone scintigraphy: The quest for the optimal deep learning algorithm and comparison with human observer performance

Purpose: Whole-body bone scintigraphy (WBS) is one of the most widely used modalities in diagnosing malignant bone diseases during the early stages. However, the procedure is time-consuming and requires vigour and experience. Moreover, interpretation of WBS scans in the early stages of the disorders might be challenging because the patterns often reflect normal appearance that is prone to subjective interpretation. To simplify the gruelling, subjective, and prone-to-error task of interpreting WBS scans, we developed deep learning (DL) models to automate two major analyses, namely (i) classification of scans into normal and abnormal and (ii) discrimination between malignant and non-neoplastic bone diseases, and compared their performance with human observers. Materials and Methods: After applying our exclusion criteria on 7188 patients from three different centers, 3772 and 2248 patients were enrolled for the first and second analyses, respectively. Data were split into two parts, including training and testing, while a fraction of training data were considered for validation. Ten different CNN models were applied to single- and dual-view input (posterior and anterior views) modes to find the optimal model for each analysis. In addition, three different methods, including squeeze-and-excitation (SE), spatial pyramid pooling (SPP), and attention-augmented (AA), were used to aggregate the features for dual-view input models. Model performance was reported through area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity and was compared with the DeLong test applied to ROC curves. The test dataset was evaluated by three nuclear medicine physicians (NMPs) with different levels of experience to compare the performance of AI and human observers. Results: DenseNet121_AA (DensNet121, with dual-view input aggregated by AA) and InceptionResNetV2_SPP achieved the highest performance (AUC = 0.72) for the first and second analyses, respectively. Moreover, on average, in the first analysis, Inception V3 and InceptionResNetV2 CNN models and dual-view input with AA aggregating method had superior performance. In addition, in the second analysis, DenseNet121 and InceptionResNetV2 as CNN methods and dual-view input with AA aggregating method achieved the best results. Conversely, the performance of AI models was significantly higher than human observers for the first analysis, whereas their performance was comparable in the second analysis, although the AI model assessed the scans in a drastically lower time. Conclusion: Using the models designed in this study, a positive step can be taken toward improving and optimizing WBS interpretation. By training DL models with larger and more diverse cohorts, AI could potentially be used to assist physicians in the assessment of WBS images. © 2023 The Author(s

Prevalence Rates of Mental Disorders in Chilean Prisons

PMCID: PMC371883