Miracle-dz/UniFAR-FTUs huggingface dataset:
Size-dependent random-telegraph noise in phase-change memories

Current noise is among the major concerns for nonvolatile memories, as it affects the read speed and the capability to distinguish among different programmed levels in the memory cell. The memory size reduction usually enhances noise, due to current localization and variability effects in nanoscale electronic devices. Phase change memory (PCM) devices display noise, mostly due to fluctuations of the subthreshold current in the amorphous chalcogenide phase. Understanding the current noise and its physical nature may allow for physics-based predictions of its scaling dependence in future PCM generations. This work addresses random telegraph noise (RTN) in PCM devices. Experiments at variable temperature indicate that the fluctuation kinetic is controlled by an Arrhenius law, while the temperature-dependent amplitude reveals that the current fluctuation is driven by a change in activation energy for conduction. Based on these evidences, we develop a model for RTN based on bistable fluctuations of defects along the percolation path. Simulation results account for the temperature and size dependence of noise observed in PCM devices. The model allows to explain the noise spectrum in PCM devices as the convolution of many RTN component, each associated to bistable fluctuations with a different characteristic frequency. Scaling projections of noise are finally provided based on model simulations.
TESTING ASSUMPTIONS MADE IN THE CONSTRUCTION OF INPUT–OUTPUT TABLES

Product input–output (IO) tables are mainly constructed on the basis of product and/or industry technology assumptions. The choice is not trivial and deserves empirical analysis using input and output data at the level of establishments. This paper offers input–output compilers econometric tests to facilitate the construction of tailored hybrid technology-based product IO tables. We provide weighted likelihood ratios of the product and industry technology assumptions. Although the proposed econometric tests are aimed to be used ex ante, we construct four variants of hybrid technology-based product IO tables using establishment data from Andalusia (Spain) and contrast them to the official product IO table and the pure product and industry technology-based tables. Our econometric tests are not valid for industry IO tables.
Developing an IoT system for an agricultural enterprise on a single-board computer platform

The article discusses the main trends in the development of agriculture and the agro-industrial complex. Currently, for this industry, the priority tasks are the improvement, development and implementation of projects of intelligent agriculture, which includes the principles of automation and production robotization. The issues of developing a system for automated management of business processes for an agricultural enterprise are considered. The main stages of creating the hardware of the system, using microcontrollers, sensors and modules are defined. The organization of the server part of the developed system, as well as the connection of sensors and modules to the microcontroller, are investigated. The development of modules for monitoring and managing electricity, light, temperature and humidity using the C ++ programming language is presented. The analysis of test modules under the control of the microcontroller. Further development of this scientific direction can be directed to the development and creation of automated control systems based on single-bit processors. Keywords— IoT system, monitoring and control system, automated control systems, intelligent automation, system development, agriculture, agricultural enterprise.
Learning a Unified Embedding for Visual Search at Pinterest

At Pinterest, we utilize image embeddings throughout our search and recommendation systems to help our users navigate through visual content by powering experiences like browsing of related content and searching for exact products for shopping. In this work we describe a multi-task deep metric learning system to learn a single unified image embedding which can be used to power our multiple visual search products. The solution we present not only allows us to train for multiple application objectives in a single deep neural network architecture, but takes advantage of correlated information in the combination of all training data from each application to generate a unified embedding that outperforms all specialized embeddings previously deployed for each product. We discuss the challenges of handling images from different domains such as camera photos, high quality web images, and clean product catalog images. We also detail how to jointly train for multiple product objectives and how to leverage both engagement data and human labeled data. In addition, our trained embeddings can also be binarized for efficient storage and retrieval without compromising precision and recall. Through comprehensive evaluations on offline metrics, user studies, and online A/B experiments, we demonstrate that our proposed unified embedding improves both relevance and engagement of our visual search products for both browsing and searching purposes when compared to existing specialized embeddings. Finally, the deployment of the unified embedding at Pinterest has drastically reduced the operation and engineering cost of maintaining multiple embeddings while improving quality.
REVAMP: Challenges and innovation roadmap for variability management in round-trip engineering of software-intensive systems

The Software Intensive Systems (SIS) has become dominant in industry. The Product Lines (PL) approach offers significant cost reductions in customization and rapid development of products targeting various market segments. However, Product Lines Engineering often times requires a complex modeling and coevolution of multiple assets. That hinders the proliferation of the PL approach and constrains its accessibility for SMEs and larger community of system developers. The ITEA 3 project REVaMP² aims to conceive, develop and evaluate the first comprehensive automation tool-chain and associated executable process to support round-trip engineering of SIS Product Lines and thereby helping to profitably engineer mass customized products and services.
A Detailed Neuroscientific Framework for the Multiple Intelligences: Describing the Neural Components for Specific Skill Units within Each Intelligence

The concept of intelligence has been debated since introduction of IQ tests in the early 1900s. Numerous alternatives to unitary intelligence have achieved limited acceptance and IQ remains the predominant theoretical basis for schooling. Multiple intelligences theory (Gardner, 1983), despite criticism it lacks experimental validity, has had sustained interest by educators worldwide as a means of personalizing instruction and curriculum. The neuroscientific evidence for the intelligences has not been updated since 1983. This investigation reviewed 417 neuroscientific studies examining neural correlates for skill units within seven intelligences. Neural activation patterns demonstrate each skill unit has its own unique neural underpinnings as well as neural features shared with other skill units within its designated intelligence. These patterns of commonality and uniqueness provide richly detailed neural architectures in support of MI theory as a scientific model of human intelligence. This conclusion is supported by four previous studies revealing extensive neural evidence that MI theory distinguishes among ability groups and several cognitive qualities (Shearer and Karanian, 2017). The emerging field of educational cognitive neuroscience strives to bridge the gap between laboratory findings and classroom instruction. MI theory aligns with advances in understanding how the mind and brain interact providing a practical interface between the art of teaching and neuroscience. A neuroscientific model of the multiple intelligences brings us closer to the goal of personalizing education by understanding the unique neuro-cognitive profiles of all students. These findings, coupled with advanced technologies, point the way forward to bring MI-inspired education to all students.
An Open and Automated Android Behavior Monitor in Cloud

For security and privacy considerations, it is important for Android users to understand the behavior and the risk of an application. Although Google claims that new applications available on the official market have passed their security checks, the open design of the Android system still allows a user to install applications for third party vendors. Therefore, there is still a demand for users to know more about an unknown application. In this paper, we discussed our experiences on setting up a scalable automated Android behavior monitor using virtualization techniques. Our contribution is two-fold: (1) We design and implement a scalable behavior monitor using both dynamic analysis and static analysis techniques; and (2) Based on parts of the analyzed results, we develop a preliminary filter to distinguish benign and malicious applications. The system is open to the public and we expect that the analyzed results can be fed back to the research community and further stimulate more studies on analyzing malicious Android applications.
Efficient simulation of Lévy-driven point processes

Abstract In this paper, we introduce a new large family of Lévy-driven point processes with (and without) contagion, by generalising the classical self-exciting Hawkes process and doubly stochastic Poisson processes with non-Gaussian Lévy-driven Ornstein–Uhlenbeck-type intensities. The resulting framework may possess many desirable features such as skewness, leptokurtosis, mean-reverting dynamics, and more importantly, the ‘contagion’ or feedback effects, which could be very useful for modelling event arrivals in finance, economics, insurance, and many other fields. We characterise the distributional properties of this new class of point processes and develop an efficient sampling method for generating sample paths exactly. Our simulation scheme is mainly based on the distributional decomposition of the point process and its intensity process. Extensive numerical implementations and tests are reported to demonstrate the accuracy and effectiveness of our scheme. Moreover, we use portfolio risk management as an example to show the applicability and flexibility of our algorithms.
Effective and Efficient Reuse of Past Travel Behavior for Route Recommendation

With the increasing availability of moving-object tracking data, use of this data for route search and recommendation is increasingly important. To this end, we propose a novel parallel split-and-combine approach to enable route search by locations (RSL-Psc). Given a set of routes, a set of places to visit O, and a threshold θ, we retrieve the route composed of sub-routes that (i) has similarity to O no less than θ and (ii) contains the minimum number of sub-route combinations. The resulting functionality targets a broad range of applications, including route planning and recommendation, ridesharing, and location-based services in general. To enable efficient and effective RSL-Psc computation on massive route data, we develop novel search space pruning techniques and enable use of the parallel processing capabilities of modern processors. Specifically, we develop two parallel algorithms, Fully-Split Parallel Search (FSPS) and Group-Split Parallel Search (GSPS). We divide the route split-and-combine task into ∑k=0 M S(|O|,k+1) sub-tasks, where M is the maximum number of combinations and S(⋅) is the Stirling number of the second kind. In each sub-task, we use network expansion and exploit spatial similarity bounds for pruning. The algorithms split candidate routes into sub-routes and combine them to construct new routes. The sub-tasks are independent and are performed in parallel. Extensive experiments with real data offer insight into the performance of the algorithms, indicating that our RSL-Psc problem can generate high-quality results and that the two algorithms are capable of achieving high efficiency and scalability.
A Topology Based Spatio-Temporal Map Algebra for Big Data Analysis

Continental and global datasets based on earth observations or computational models challenge the existing map algebra approaches. The available datasets differ in their spatio-temporal extents and their spatio-temporal granularity, which makes it difficult to process them as time series data in map algebra expressions. To address this issue we introduce a new map algebra approach that is topology based. This topology based map algebra uses spatio-temporal topological operators (STTOP and STTCOP) to specify spatio-temporal operations between topological related map layers of different time-series data. We have implemented several topology based map algebra tools in the open source geoinformation system GRASS GIS and its open source cloud processing engine actinia. We demonstrate the application of our topology based map algebra by solving real world big data problems using a single algebraic expression. This included the massively parallel computation of the NDVI from a series of 100 Sentinel2A scenes organized as earth observation data cubes. The processing was performed and benchmarked on a many core computer setup and in a distributed container environment. The design of our topology based map algebra allows us to deploy it as a standardized service in the EU Horizon 2020 project openEO.
Bioinformatics Workflows for Genomic Variant Discovery, Interpretation and Prioritization

Next-generation sequencing (NGS) techniques allow high-throughput detection of a vast amount of variations in a cost-efficient manner. However, there still are inconsistencies and debates about how to process and analyse this ‘ big data ’ . To accurately extract clinically relevant information from genomics data, choosing appropriate tools, knowing how to best utilize them and interpreting the results correctly is crucial. This chapter reviews state-of-the-art bioinformatics approaches in clinically relevant genomic variant detection. Best practices of reads-to-variant discovery workflows for germline and somatic short genomic variants are presented along with the most commonly utilized tools for each step. Additionally, methods for detecting structural variations are overviewed. Finally, approaches and current guidelines for clinical interpretation of genomic variants are discussed. As emphasized in this chapter, data processing and variant discovery steps are relatively well-understood. The differences in prioritization algorithms on the other hand can be perplexing, thus creating a bottleneck during interpretation. This review aims to shed light on the pros and cons of these differences to help experts give more informed decisions.
Fuel-Optimal Power Split and Gear Selection Strategies for a Hybrid Electric Vehicle

This paper presents a computationally inexpensive optimization algorithm to jointly compute the fuel-optimal state and input trajectories for the energy management of a hybrid electric vehicle (HEV) on a known driving mission. Specifically, we first introduce a model of the HEV that we leverage to formulate the fuel-optimal control problem as a mixed-integer convex program. Second, we use Pontryagin’s minimum principle and non-smooth convex analysis to derive the fuel-optimal control policy for the power split, the engine on/off decision, and the gear selection. Third, we combine the optimal control policy with single shooting and a bisection method to compute the optimal strategy for reaching a predefined terminal state of charge in the absence of path constraints. Fourth, we introduce a multi-point boundary value approach to deal with path constraints, thus obtaining the optimal control strategies for a complete driving cycle. Finally, we test the proposed algorithm on representative driving cycles and compare its performance to the globally optimal solution computed using dynamic programming (DP). Our results show that our algorithm can compute the fuel-optimal control strategy with the same precision as DP, in less than one second, forming the basis for a real-time-implementable predictive energy management system for HEVs.
Alteration Method of Schedule Information on Public Cloud

We are currently experiencing an explosion of cloud technologies. However, a cloud service administrator may be an untrustworthy third party. Therefore, companies dealing with confidential information cannot use a public cloud. In this paper, we propose a method for preventing the leakage of private information on a cloud schedule service. In this method, even a cloud administrator or a hacker who steals a cloud service login key cannot read the true schedule because the schedule date is altered and schedule content is encrypted. Consequently we can safely use a public cloud schedule service with this method. We also evaluated the method’s performance using an actual alteration program on Google server. We implement the proposed method and show the performance is practical by evaluating actually. Keywords-cloud computing; Internet security; privacy; Google Calendar; schedule service; date alteration.
A Hybrid Fuzzy TOPSIS – Best Worst Method for Risk Prioritization in Megaprojects

Megaprojects are usually complex and in many cases encounter failure in terms of finish late or overspent. This study aims to investigate the critical risk factors behind these projects as well as their priority. Project risk management is a mature research stream. But when focus on megaprojects the amount of research decreases significantly. This research provides a hierarchy of risk structure in Tehran-Rasht railway megaproject and prioritizes the risk factors through a two-phase methodology. This method is a new hybrid MCDM technique consist of group fuzzy TOPSIS and fuzzy Best-Worst Method. BWM is the latest MCDM technique which in this paper, its fuzzy version combined with fuzzy TOPSIS is employed. This research also considers all the project success criteria including time, cost and quality simultaneously and calculates the risk priority Index (RPI) accordingly. The results imply that quality is the most important project success factor and the risk elements with greater impact on project quality, get higher PRI. The identified and ranked risk factors help practitioners and academics to follow the subsequent steps of the risk management process of Iranian transportation megaprojects.
Embedding Strategies for Specialized Domains: Application to Clinical Entity Recognition

Using pre-trained word embeddings in conjunction with Deep Learning models has become the “de facto” approach in Natural Language Processing (NLP). While this usually yields satisfactory results, off-the-shelf word embeddings tend to perform poorly on texts from specialized domains such as clinical reports. Moreover, training specialized word representations from scratch is often either impossible or ineffective due to the lack of large enough in-domain data. In this work, we focus on the clinical domain for which we study embedding strategies that rely on general-domain resources only. We show that by combining off-the-shelf contextual embeddings (ELMo) with static word2vec embeddings trained on a small in-domain corpus built from the task data, we manage to reach and sometimes outperform representations learned from a large corpus in the medical domain.
Does Collaborative Learning Improve Student Learning Outcomes? Evidence from a New Experiment

This project creates a collaborative learning framework that promotes a deep approach to learning. In spring 2002, we applied our framework to one section of intermediate macroeconomics, while we taught another section using a traditional lecture format. Unlike previous research, we incorporated the collaborative-learning framework into the regular class period. We identified and then tracked measures of student learning outcomes. Using multivariate regression analysis, we find that students taught by collaborative learning attended class more often, which resulted in higher academic achievement.
Minimal characterization of linguistic phenomena for robust ternary expression construction

This thesis introduces Astroparse, a system that uses the output of a third-party neural network based dependency parser (spaCy) to construct semantic parses of sentences in the form of ternary expressions as pioneered by the Start Natural Language system. Ternary expressions are a powerful representation for efficiently indexing, matching, and retrieving natural language. Because Start is a purely symbolic system, extending Start's parser, which produces ternary expressions from sentences, requires significant effort. Astroparse makes it far easier to extend Start's coverage. Learning from examples (pairs of sentences and ternary expressions), Astroparse automatically learns to associate the linguistic phenomenon corresponding to an example's ternary expression with a subtree of the example sentence's dependency tree and the token-level features (e.g., lemma, part-of-speech tags) of the subtree's nodes. Given unseen sentences, Astroparse recognizes the learned minimal characterizations of linguistic phenomena to construct ternary expressions from spaCy's parse of the sentence. By leveraging the output of a neural network based dependency parser with high efficiency and state-of-the-art accuracy, Astroparse offers a fast, high-recall, easy-to-train system to augment Start's current parser for constructing ternary expressions.
Binary Fields and Economics through Fuzzy Logic Approach and Boolean Algebra Using Multidimensional Processing with Respect to Artificial Neural Networks and Machine Learning

The fuzzy logic represents the relationship between precision and uncertainty. As the uncertainty in a theme is high, then less precise we can be in our conception. A binary logic admits only the opposites of true and false, a logic which does not admit digress of truth and there are no variations in magnitudes, but only two possible results. As more complex a system is, then more imprecise or inexact is the information that we have to the system. Aristotle mentioned that “It is the mark of an instructed mind to rest satisfied with that degree of precision which the nature of the subject admits, and not to seek exactness where only an approximation of the truth is possible”. So, Aristotelian logic does not admit imprecision in truth. However, Aristotle’s quote is so relevant with the approach that admits uncertainty. The theme is the balance between the precision with the uncertainty in a concept. The case of imprecision comes up from physical processes upon on imprecise human reasoning. Requiring precision in engineering models and economics means high cost and long lead times in production and development. So, considering the use of fuzzy logic then ponder the need for exploiting the tolerance for imprecision. According to the traditional view of science, uncertainty represents an undesirable situation, and must be excluded at any cost. Max Black referred to vagueness, where the possible states are not clearly. According to his essay in 1937 known as “Vagueness: An exercise in logical analysis” presented some remarks by Plato about Uncertainty in geometry. Bertand Russell in 1923 pointed out that “all traditional logic habitually assumes that precise symbols are being employed”. So, follow some proposals.