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In recent years, static warhead arena tests have used stereoscopic cameras to count fragments in real time and correlate their individual velocity and mass. This new technique provides the ability ...

Applications based on the compute-in-the-network (COIN) paradigm require flexible network telemetry data to drive effective allocation decisions. Telemetry systems collect such data based on queries specifying the precise traffic metrics or features required. Recent advances in programmable switch hardware have led to highly efficient methods to compute query results using in-network resources. However, current approaches fail to meet the simultaneous requirements of dynamic traffic loads, diverse query types, and query dynamics. In this work, we argue that telemetry systems should be cast as active runtime schedulers rather than static data sources. COIN-based applications can then dynamically submit telemetry queries on the fly and react to their results in a closed-loop fashion – enabling a new generation of telemetry-driven reactive applications. As a first step toward this vision, we present a single-switch telemetry operation scheduling method. Our empirical evaluation demonstrates that such a method can reduce overhead by an order of magnitude compared to worst case allocations. This initial exploration opens the door to a multi-tier scheduling framework combining switch hardware with software-based compute platforms to meet the telemetry demands of future COIN-based applications.

Current biometric applications exhibit numerous connections to artificial intelligence (AI). They use AI to boost their performance, can fall prey to attacks targeting AI components or based on AI, and can use AI toward off attacks. Biometric applications serve to identify individuals based on their biological characteristics or behaviors. Currently, fingerprints and faces are the predominantly used biometric modalities, but systems based on hand veins, irises, or voices are also available. Biometric applications can use single modalities or combine multiple ones, and they can process static data (for example, for facial recognition) as well as (for example, for video identification or speaker verification). Data can be acquired using single or multiple sensors of different types (for ex-ample, optical or acoustic).

Projections aim to convey the relationships and similarity of high-dimensional data in a low-dimensional representation. Most such techniques are designed for static data. When used for time-dependent data, they usually fail to create a stable and suitable low dimensional representation. We propose two dynamic projection methods (PCD-tSNE and LD-tSNE) that use global guides to steer projection points. This avoids unstable movement that does not encode data dynamics while keeping t-SNE's neighborhood preservation ability. PCD-tSNE scores a good balance between stability, neighborhood preservation, and distance preservation, while LD-tSNE allows creating stable and customizable projections. We compare our methods to 11 other techniques using quality metrics and datasets provided by a recent benchmark for dynamic projections.

In this research work, a crack diagnosis method for beam-column structures is proposed considering axial load effects through experimental data. The proposed method is employed for the detection of...

espanolEl objetivo de este trabajo es dar cuenta de los conceptos de sujeto, subjetivacion y ontologia historica de nosotros mismos. Estas categorias son esenciales en las pesquisas de Foucault de los anos 80-84. De igual manera, desde estos conceptos, Foucault pudo pensar al sujeto de forma alterna al modo como este fue configurado en Occidente. En efecto, en sus intensas busquedas encuentra en la tradicion moderna, Baudelaire y Kant, pistas para considerar al sujeto no como un dato estatico, sino como alguien que se gestiona a traves de procesos historicos que lo van delineando y que lo habilitan en la empresa de construirse, de elaborarse a si mismo, esto es, mediante procesos de subjetivacion. Finalmente, la tradicion antigua, es decir, la tradicion greco-latina, le ofrece elementos teoricos sustanciales para reflexionar sobre las condiciones en las que se pueden configurar modos de vida, estilos de existencia que operen como ejes de resistencia a las formas que se nos han impuesto. EnglishThe purpose of this paper is to analyse the concepts of subject, subjectivation and historical ontology of ourselves. These categories are essential in Foucault’s research of the 80-84 years. In the same way, from these concepts Foucault interprets the subject in an alternating way to the way it was configured in the West. Indeed, in his intense researches he finds in the modern tradition, Baudelaire and Kant, clues to consider the subject not as a static data, but as someone who is managed through historical processes that delineate it and that enable it to be built, to be selfelaborated, that is, through processes of subjectivation. Finally, the ancient tradition, that is to say, the Greco-Latin tradition, offers substantial theoretical elements to reflect on the conditions under which lifestyles can be configured, styles of existence that operate as axes of resistance to the forms that have been given to us.

The mining of frequent weighted patterns (FWPs) that considers the different semantic significance (weight) of items is more suitable for practice than the mining of frequent patterns. Therefore, it plays a vital role in real-world scenarios. However, there exist several limitations when applying methods for mining FWPs designed for static data on growth datasets, especially data streams. Hence, this study proposes an algorithm for mining FWPs over data streams. First, we introduce the concept of mining FWPs over data streams via a sliding window model. Then, we introduce a modification of the weighted node tree (WN-tree) named SWN-tree that has the ability to maintain the information over data streams. Next, this study develops a method for mining FWPs over data streams employing a sliding window model based on SWN-tree. This method is called FWPODS (Frequent Weighted Patterns Over Data Stream) algorithm. Finally, we conduct empirical experiments to compare the performances of our approach and the state-of-the-art algorithm (NFWI) for mining FWPs over data streams. The results of experiment indicate that our approach outperforms the NFWI algorithm when running in batch mode in a sliding window.

Currently, neuroendocrine neoplasms (NENs) of the gastroenteropancreat-ic and bronchopulmonary systems are well studied and the most common, the share of which is 73 and 25 % of cases, respectively. The remaining 2 % of cases are attributed to NEN of more rare localizations, the static registration of which is difficult due to their rarity, and clinical cases are presented by episodic descriptions of single observations. Such tumors include NEN of the prostate gland from 0.5 to 1.0 %, NEN of ovaries 0.5 %, NEN of the mammary gland from 0.3 to 0.5 %, NEN of the kidney (practically devoid of static data), bladder 0.48-1.00 %. The retrospective study included 92 patients with NEN of rare localizations who were examined and treated of the N. N. Blochin National Medical Research Center of Oncology from 1990 to 2019. Currently, there are no generally accepted methods of treatment for this group of patients due to the rarity of the pathology, and it is carried out according to the NEN of other localizations. NEN of rare localizations have high survival rates (the observation period was from a month to 14 years), both general and without progression and require long-term monitoring and observation. At the time of assessment of long-term results, 20 (21.8 %) patients died of progression, 12 (13.1 %) patients dropped out of observation. The rest were alive at the date of the last control.

O presente estudo tem como objetivo analisar a expansão e o financiamento da Pós-Graduação Stricto Sensu, no período 2002-2018, considerando o contexto de desigualdade regional brasileira. A pesquisa, com abordagem quali-quantitativa, tem como base empírica os dados estatísticos do Sistema de Informações Georreferenciadas da CAPES (GEOCAPES) e da Execução Orçamentária da União. Os resultados apontam expressivo movimento de expansão da Pós-Graduação no país, com índices mais acentuados nas regiões Norte e Nordeste, mas também revelam ainda haver elevada concentração no eixo sul-sudeste. Em relação ao financiamento, destaca-se a elevada redução orçamentária da CAPES, a partir de 2014, com fortes implicações na manutenção dos Programas, particularmente no que diz respeito aos auxílios financeiros a pesquisadores e estudantes. Em síntese, conclui-se que sem o necessário financiamento público serão aprofundadas as disparidades regionais na Pós-Graduação brasileira.

The state-of-the-art Credit Card Fraud Detection (CCFD) systems utilize supervised rule-based schemes to identify fraudulent behaviors in static data. Designing an efficient framework for CCFD is p...

All the examples since Chapter 11 have relied on static data that has been hardwired into the application. In this chapter, I demonstrate how to use asynchronous HTTP requests, often called Ajax requests, to interact with a web service to get real data into an application. Table 24-1 puts HTTP requests in context.

Prevention and management of epidemic is a protracted war. As large community in city, universities are key regions in the anti-epidemic period. However, the current epidemic prevention and management measures in many universities do not compatible with the spatial form and the characteristics of the population, likely to lead to waste of resources and cause conflicts. The research simulates campus environment by constructing GIS model, and simulates the behavior of campus crowd by ABM. Under the coupling effect of the two, the real-time calculation of the spread of epidemic in universities can be calculated in real-time, making up for the deficiency of GIS model which can only do static data analysis. On this basis, research takes South China University of Technology as an example and assumes three epidemic prevention management measures, i.e. closed-off management, zoning management and self prevention, respectively to simulate the spread of the epidemic, sum up the results of different management measures and provide certain suggestions.

Connected and automated vehicles (CAVs) are already part of the surface transportation system. In order for a CAV to operate safely, it needs information such as static data (high-resolution naviga...

DOI: 10.1590/2179-8966/2019/45695 Resumo O presente artigo visa apresentar discussoes sobre a politica de morte da juventude negra no Brasil, tendo como principal chave de leitura a raca, e fundamentada nas teses de dois filosofos, o italiano Roberto Esposito (biopolitica) e o camarones Achille Mbembe (necropolitica). Para desenvolvimento das ideias sera utilizado o metodo de revisao de literatura entrelacado com dados estaticos apresentados no Mapa da Violencia 2018 e Atlas da Violencia 2019 sobre a morte da juventude negra brasileira. Palavras-chave: Juventude negra; Biopolitica; Necropolitica. Abstract This article aims to present discussions about the politics of death of black youth in Brazil, having as main reading key the race, and based on the theses of two philosophers, the Italian Roberto Esposito (biopolitics) and the Cameroonian Achille Mbembe (necropolitics). To develop the ideas will be used the literature review method interlaced with static data presented in the Map of Violence 2018 and Atlas of Violence 2019 on the death of Brazilian black youth. Keywords: Black youth; Biopolitics; Necropolitics.

This study conducted a comparative analysis on working capital management and performance ofindustrial and consumer goods firms in Nigeria. Precisely, the study compared effect of average collectionperiod and average payment period on return on capital employed of selected industrial goods and consumergoods firms. 20 firms were randomly selected over a period of 10 years data were collected from annualreport of the firms. This study used static data analyses to analyze data. Result showed average collectionperiod and average payment period exert insignificant positive effect on return on capital employed ofindustrial goods firms, while both average collection period and average payment period exert insignificantnegative effect on return on capital employed of consumer goods firms. Independent t-test result showedsignificant mean difference between coefficient estimate corresponding to industrial and consumer goodsfirms. This study concluded that there exists significant difference between the effect of working capitalmanagement on performance of industrial goods firms and consumer goods firms when performance ismeasured in terms of return on capital employed. Hence firms in both sub-sectors should be strategic whenmanaging working capital, by setting higher average payment period in a manner that will not crowd-outtheir credit worthiness.

The Android operating system dominates the smartphone market. Thus, to service the market, the number of Android applications has risen dramatically. These applications are processing a great amount of sensitive data, which could result in various concerns including data leakage and privacy violations. For example, applications may misuse the sensitive data stored on Android devices and violate the privacy of the user. Therefore, it is essential to maintain user privacy and protect sensitive data from leakage. Static data flow analysis approaches are used for analyzing Android applications to uncover security and privacy issues. However, these approaches frequently generate false alarms, given the different challenges created by Android applications, such as inter-component communication (ICC), reflection, and implicit flow. This work presents the DroidRista approach for conducting static data flow analysis on Android applications to detect sensitive data leakage. DroidRista analyzes ICC, reflection, and implicit flow in Android applications. To evaluate the performance of DroidRista, it was tested on three data sets. The results demonstrate improved performance in terms of detecting data leakage compared to existing static data flow analysis approaches.

Parameters controlling continuous chemical processes as well as all instruments found in such processes generate a huge amount of data. Calculating models to predict critical quality attributes (in y ) from process data (in X ) can be fastidious. Partial-Least-Squares (PLS) regression is now widely present in the industry for process analysis and modelling purposes. However, this methodology requires static data while the majority of chemical processes operate continuously. Some dynamic PLS variants have been developed, but they cannot associate specific critical process parameters with meaningful process dynamics. This work presents a novel Dynamic-PLS variant developed to resolve these issues. Using one real dataset and artificial cases, the performances of this novel methodology is compared to standard regression methodologies. It is demonstrated that the proposed methodology calculates the most robust models in validation. With the real dataset, it is also demonstrated that the predictive model calculated from the proposed methodology can easily be interpreted, leading to better process understanding.

This study assessed working capital management and performance of listed manufacturing firms in Nigeria 20 firms were sampled, over 10 years. The study employed static data analyses and panel Granger causality test. Result showed that average collection period exerts insignificant negative effect on return on capital employed of the sampled firms, while average collection period also exerts insignificant negative effect on earnings per share of the sampled firms. The result further showed that, average payment period exerts insignificant positive effect on return on capital employed of the sampled firms, but average payment period exerts insignificant negative effect on earnings per share of the sampled firms. The study concluded that, average collection period and average payment exert insignificant effect on return on capital employed of listed manufacturing firms in Nigeria, also; average collection period and average payment period exert insignificant effect on earnings per share of listed manufacturing firms in Nigeria. Hence manufacturing firms in Nigeria should objectively manage average collection period and also maintain a consistent improvement in return on capital employed and earnings per share of listed manufacturing firms in Nigeria.

Background. The study of oncological morbidity in children, as well as the characteristics of its structure and the analysis of temporal trends within the subject of the Russian Federation is aimed at improving the organization of medical care: increasing early diagnosis, reliability of static data, and optimizing patient routing. Objective. Our aim was to analyze the incidence and its structure in children (0–17 years old) who lived in the Arkhangelsk Region and the Nenets Autonomous District in 2007–2016. Methods. The analysis of the incidence of malignant neoplasms of the child population in the Arkhangelsk region and the Nenets Autonomous District was carried out. Results. During the study period, the diagnosis of cancer was first established in 329 (including 6 patients from other subjects of the Russian Federation children (boys — 177, girls — 146) at the age of 0–17 years, of which 260 children at the age of 0–14 years and 63 — at the age of 15–17 years. The average annual incidence of children over 10 years was 14.3: for the age group 0–14 years old — 13.8, for the age group 15–17 years old — 16.6. During the analyzed period, there were significant fluctuations in annual incidence rates (minimum — 9.8 in 2010; maximum — 16.8 in 2009). The average annual incidence in the age group 15–17 years (16.6) was 18% higher than in the age group 0–14 years (14). The morbidity structure was characterized by some predominance of solid tumors (51.7%) over hemoblastosis (48.3%), and in the group of 0–14 years this ratio was 54.6/45.4%, in the group of 15–17 years — 50, 9/49.1%. Conclusion. In the analyzed subjects in recent years there has been practically no increase in the incidence in children. The incidence in the age group 15–17 years is slightly higher than in the age group 0–14 years. Changes in the morbidity structure are noted, mainly due to a decrease in the proportion of lymphomas.

International audience; In this article, we propose a new framework based on dataflow graphs to abstract and analyze designs for hardware architectures. It is called Actors with Stretchable Access Patterns (ASAP). It can overcome some limitations of all Static Data Flow (SDF) based models like mandatory buffering between actors. This article details the fundamental contributions of ASAP. Firstly, it gives the definition of actors and their different patterns. It also illustrates the link between these notions and components written in VHDL through several examples. Secondly, it presents the main algorithms to check if a graph processes an input data stream correctly, which is called compatibility checking. Thirdly, it summarizes the principles of graph modification to enforce this correctness in case of some blocks are declared incompatible. Finally, it briefly describes our EDA tool called BlAsT which integrates the above principles, before presenting an application on a realistic FPGA design. It shows that ASAP overwhelms other models in terms of resources saving without any impact on the global latency. It also points out the ability of BlAsT to compute and to propose graph modifications and to generate the VHDL code of the whole design.

Background COVID-19 was first identified in December 2019 in the city of Wuhan, China. The virus quickly spread and was declared a pandemic on March 11, 2020. After infection, symptoms such as fever, a (dry) cough, nasal congestion, and fatigue can develop. In some cases, the virus causes severe complications such as pneumonia and dyspnea and could result in death. The virus also spread rapidly in the Netherlands, a small and densely populated country with an aging population. Health care in the Netherlands is of a high standard, but there were nevertheless problems with hospital capacity, such as the number of available beds and staff. There were also regions and municipalities that were hit harder than others. In the Netherlands, there are important data sources available for daily COVID-19 numbers and information about municipalities. Objective We aimed to predict the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands, using a data set with the properties of 355 municipalities in the Netherlands and advanced modeling techniques. Methods We collected relevant static data per municipality from data sources that were available in the Dutch public domain and merged these data with the dynamic daily number of infections from January 1, 2020, to May 9, 2021, resulting in a data set with 355 municipalities in the Netherlands and variables grouped into 20 topics. The modeling techniques random forest and multiple fractional polynomials were used to construct a prediction model for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands. Results The final prediction model had an R2 of 0.63. Important properties for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality in the Netherlands were exposure to particulate matter with diameters Conclusions Data about municipality properties in relation to the cumulative number of confirmed infections in a municipality in the Netherlands can give insight into the most important properties of a municipality for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality. This insight can provide policy makers with tools to cope with COVID-19 and may also be of value in the event of a future pandemic, so that municipalities are better prepared.

Persistent identification of dynamic and static data, using DOIs and provenance (inabstract).

Intrusion detection system is a primary defense mechanism in aspect of protecting network security. Anomaly detection, as one of the most commonly used intrusion detection methods, plays a significant part in detecting the network traffic data. However, network traffic data is typical data stream pattern that is in the natures of infiniteness, correlation and concept drift, which bring some challenges to traditional anomaly detection algorithms for static data. In this paper, a novel method (called DLSHiForest*) based on LSHiForest, sliding window, concept drift detection and model update is proposed, which can cope with above issues to improve detection accuracy while guaranteeing anomaly detection efficiency. Extensive experiments are conducted using SMTP dataset to verify the feasibility of our proposed method. Experimental results demonstrate that our proposal can achieve accurate and efficient anomaly detection for data stream.

Discovery of Attribute Based Access Control policies through mining has been studied extensively in the literature. However, current solutions assume that the rules are to be mined from a static data set of access permissions and that this process only needs to be done once. However, in real life, access policies are dynamic in nature and may change based on the situation. Simply utilizing the current approaches would necessitate that the mining algorithm be re-executed for every update in the permissions or user/object attributes, which would be significantly inefficient. In this paper, we propose to incrementally maintain ABAC policies by only updating the rules that may be affected due to any change in the underlying access permissions or attributes. A comprehensive experimental evaluation demonstrates that the proposed incremental approach is significantly more efficient than the conventional ABAC mining.

SummaryAlthough knowing where a protein functions in a cell is important to characterize biological processes, this information remains unavailable for most known proteins. Machine learning narrows the gap through predictions from expert-designed input features leveraging information from multiple sequence alignments (MSAs) that is resource expensive to generate. Here, we showcased using embeddings from protein language models (pLMs) for competitive localization prediction without MSAs. Our lightweight deep neural network architecture used a softmax weighted aggregation mechanism with linear complexity in sequence length referred to as light attention (LA). The method significantly outperformed the state-of-the-art (SOTA) for ten localization classes by about eight percentage points (Q10). So far, this might be the highest improvement of just embeddings over MSAs. Our new test set highlighted the limits of standard static data sets: while inviting new models, they might not suffice to claim improvements over the SOTA.AvailabilityOnline predictions are available at http://embed.protein.properties. Predictions for the human proteome are available at https://zenodo.org/record/5047020. Code is provided at https://github.com/HannesStark/protein-localization.

Knowledge graph representation learning models are mostly used for static data. When the data changes, the models cannot be adjusted dynamically as the data changes. The data is constantly changing in actual usage scenarios. However, most representation learning models focus on prediction accuracy and ignore the dynamic change of knowledge graph. A trained representation learning model is closed, but the facts in the real world are constantly changing. The knowledge graph of the real world should be open to realize the description of reality. This paper proposes a dynamic adaptive chain transfer model aim to deal with changing knowledge graph data. Our model realizes the dynamic increase and decrease of triples without retraining. We designed an experimental method to verify the validity of the model. Experimental results show that our model can achieve dynamic data changes and keep the performance of the original model.