DeepFT: Fault-tolerant edge computing using a self-supervised deep surrogate model

The emergence of latency-critical AI applications has been supported by the evolution of the edge computing paradigm. However, edge solutions are typically resource-constrained, posing reliability challenges due to heightened contention for compute capacities and faulty application behavior in the presence of overload conditions. Although a large amount of generated log data can be mined for fault prediction, labeling this data for training is a manual process and thus a limiting factor for automation. Due to this, many companies resort to unsupervised fault-tolerance models. Yet, failure models of this kind can incur a loss of accuracy when they need to adapt to non-stationary workloads and diverse host characteristics. Thus, we propose a novel modeling approach, DeepFT, to proactively avoid system overloads and their adverse effects by optimizing the task scheduling decisions. DeepFT uses a deep-surrogate model to accurately predict and diagnose faults in the system and co-simulation based self-supervised learning to dynamically adapt the model in volatile settings. Experimentation on an edge cluster shows that DeepFT can outperform state-of-the-art methods in fault-detection and QoS metrics. Specifically, DeepFT gives the highest F1 scores for fault-detection, reducing service deadline violations by up to 37% while also improving response time by up to 9%

Analisis Sumberdaya Ikan Cakalang (Katsuwonus Pelamis) Di Perairan Kabupaten Pohuwato, Provinsi Gorontalo (Resource Analysis of Skipjack (Katsuwonus Pelamis) in Pohuwato, Gorontalo Province)

Skipjack (Katsuwonus pelamis) is the one commodity that the waters of the Gulf of Tomini which potential and high economic value, and the fishermen used with various types of fishing technology. The aims of this study was to see whether the fish catches of skipjack influenced by extrinsic factors such as zones, depth, and season and knowing revenue per fishing effort. This study used primary data, especially on fish resources obtained through direct observation of research and interviews with actors fishery (fishing/crew, ship owners, collectors, officers TPI and other stakeholders), and the option to use a list of questions/questionnaire structured according to the research objectives. Secondary data were collected through the reporting of fisheries statistics include statistical data of domestic fisheries (RTP), statistics fleets and fishing gear, production data from the Department of Fisheries and Marine Gorontalo Province, Department of Fisheries and Marine Pohuwato, the Central Bureau of Statistics of Gorontalo Province Development Planning Agency Regional and Tomini Bay Sustainable Coastal Livelihoods and Management (SUSCLAM). In addition, a variety of literature supporting this research e.g. scientific publications, local publications and other documents. Data analysis was performed using resources through the stock abundance index data recording catches and the number of trips/boat/gear in the time series, to determine the allocation of fishing effort on the abundance of fish. Next will be the calculation of the catch per effort (CPUE, Catch per Unit of Effort). Analysis of the allocation of fishing effort done using monthly seasonal index (%), and forecasts an economic advantage by analysis Revenue per Unit Effort (RPUEj). The results of the study explained that the test results obtained by a factor wald gear types, depths and seasons significant effect on catches of tuna with a p-value <5%. The zone does not affect the catches of skipjack. Based on the Monthly Seasonal Index (Ij) that Ij the August high of 69, 05% then followed in July by 65, 94%. The results of the analysis Revenue per Unit Effort (RPUEj), the value of the highest RPUE for flying fish and tuna occur on in October of IDR 102,325,862

Are Convolutional Neural Networks or Transformers more like human vision?

Modern machine learning models for computer vision exceed humans in accuracy on specific visual recognition tasks, notably on datasets like ImageNet. However, high accuracy can be achieved in many ways. The particular decision function found by a machine learning system is determined not only by the data to which the system is exposed, but also the inductive biases of the model, which are typically harder to characterize. In this work, we follow a recent trend of in-depth behavioral analyses of neural network models that go beyond accuracy as an evaluation metric by looking at patterns of errors. Our focus is on comparing a suite of standard Convolutional Neural Networks (CNNs) and a recently-proposed attention-based network, the Vision Transformer (ViT), which relaxes the translation-invariance constraint of CNNs and therefore represents a model with a weaker set of inductive biases. Attention-based networks have previously been shown to achieve higher accuracy than CNNs on vision tasks, and we demonstrate, using new metrics for examining error consistency with more granularity, that their errors are also more consistent with those of humans. These results have implications both for building more human-like vision models, as well as for understanding visual object recognition in humans.Comment: Accepted at CogSci 2021. Source code and fine-tuned models are available at https://github.com/shikhartuli/cnn_txf_bia

Idiopathic granulomatous mastitis masquerading as carcinoma of the breast: a case report and review of the literature

BACKGROUND: Idiopathic granulomatous mastitis is an uncommon, benign entity with a diagnosis of exclusion. The typical clinical presentation of idiopathic granulomatous mastitis often mimics infection or malignancy. As a result, histopathological confirmation of idiopathic granulomatous mastitis combined with exclusion of infection, malignancy and other causes of granulomatous disease is absolutely necessary. CASE PRESENTATION: We present a case of a young woman with idiopathic granulomatous mastitis, initially mistaken for mastitis as well as breast carcinoma, and successfully treated with a course of corticosteroids. CONCLUSION: There is no clear clinical consensus regarding the ideal therapeutic management of idiopathic granulomatous mastitis. Treatment options include expectant management with spontaneous remission, corticosteroid therapy, immunosuppressive agents and extensive surgery for refractory cases

The Aims and Activities of the International Network of Nuclear Structure and Decay Data Evaluators.

International Network of Nuclear Structure and Decay Data (NSDD) Evaluators consists of a number of evaluation groups and data service centers in several countries that appreciate the merits of working together to maintain and ensure the quality and comprehensive content of the ENSDF database (Evaluated Nuclear Structure Data File). Biennial meetings of the network are held under the auspices of the International Atomic Energy Agency (IAEA) to assign evaluation responsibilities, monitor progress, discuss improvements and emerging difficulties, and agree on actions to be undertaken by individual members. The evaluated data and bibliographic details are made available to users via various media, such as the journals ''Nuclear Physics A'' and ''Nuclear Data Sheets'', the World Wide Web, on CD-ROM, wall charts of the nuclides and ''Nuclear Wallet Cards''. While the ENSDF master database is maintained by the US National Nuclear Data Center at the Brookhaven National Laboratory, these data are also available from other nuclear data centers including the IAEA Nuclear Data Section. The Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy, in cooperation with the IAEA, organizes workshops on NSDD at regular intervals. The primary aims of these particular workshops are to provide hands-on training in the data evaluation processes, and to encourage new evaluators to participate in NSDD activities. The technical contents of these NSDD workshops are described, along with the rationale for the inclusion of various topics

Population of isomers in decay of the giant dipole resonance

The value of an isomeric ratio (IR) in N=81 isotones ($^{137}$Ba, $^{139}$Ce, $^{141}$Nd and $^{143}$Sm) is studied by means of the ($\gamma, n)$ reaction. This quantity measures a probability to populate the isomeric state in respect to the ground state population. In ($\gamma, n)$ reactions, the giant dipole resonance (GDR) is excited and after its decay by a neutron emission, the nucleus has an excitation energy of a few MeV. The forthcoming $\gamma$ decay by direct or cascade transitions deexcites the nucleus into an isomeric or ground state. It has been observed experimentally that the IR for $^{137}$Ba and $^{139}$Ce equals about 0.13 while in two heavier isotones it is even less than half the size. To explain this effect, the structure of the excited states in the energy region up to 6.5 MeV has been calculated within the Quasiparticle Phonon Model. Many states are found connected to the ground and isomeric states by $E1$, $E2$ and $M1$ transitions. The single-particle component of the wave function is responsible for the large values of the transitions. The calculated value of the isomeric ratio is in very good agreement with the experimental data for all isotones. A slightly different value of maximum energy with which the nuclei rest after neutron decay of the GDR is responsible for the reported effect of the A-dependence of the IR.Comment: 16 pages, 4 Fig