Miracle-dz/UniFAR-FTUs huggingface dataset:
Embedding Learning

Abstract Numerical embedding has become one standard technique for processing and analyzing unstructured data that cannot be expressed in a predefined fashion. It stores the main characteristics of data by mapping it onto a numerical vector. An embedding is often unsupervised and constructed by transfer learning from large-scale unannotated data. Given an embedding, a downstream learning method, referred to as a two-stage method, is applicable to unstructured data. In this article, we introduce a novel framework of embedding learning to deliver a higher learning accuracy than the two-stage method while identifying an optimal learning-adaptive embedding. In particular, we propose a concept of U-minimal sufficient learning-adaptive embeddings, based on which we seek an optimal one to maximize the learning accuracy subject to an embedding constraint. Moreover, when specializing the general framework to classification, we derive a graph embedding classifier based on a hyperlink tensor representing multiple hypergraphs, directed or undirected, characterizing multi-way relations of unstructured data. Numerically, we design algorithms based on blockwise coordinate descent and projected gradient descent to implement linear and feed-forward neural network classifiers, respectively. Theoretically, we establish a learning theory to quantify the generalization error of the proposed method. Moreover, we show, in linear regression, that the one-hot encoder is more preferable among two-stage methods, yet its dimension restriction hinders its predictive performance. For a graph embedding classifier, the generalization error matches up to the standard fast rate or the parametric rate for linear or nonlinear classification. Finally, we demonstrate the utility of the classifiers on two benchmarks in grammatical classification and sentiment analysis. Supplementary materials for this article are available online.
UIR-Net: Object Detection in Infrared Imaging of Thermomechanical Processes in Automotive Manufacturing

Thermomechanical processes (TMPs) such as resistance spot welding (RSW) and hot stamping are widely used in automotive manufacturing. Recent advancement in sensing technology has led to an increasing adoption of thermographic cameras to capture the infrared (IR) radiation of a metal part (or component of a part) during its thermomechanical processing or immediately after the process when the part is still hot. Detecting the object(s) of interest from raw IR images is an essential step in analyzing these data. Deep learning (DL) has been a recent success for object detection (OD), but the application of DL-based OD for industrial IR images in manufacturing is largely lagging behind. The major contribution of this work, which is also the distinction from previous OD studies, is the capability of building the OD model with unlabeled IR images, i.e., imaging data without accurate information indicating the object position. The architecture of Unsupervised IR Image Net (UIR-Net) is designed to accommodate the unique characteristics of IR images from TMPs in manufacturing. This study presents a novel method for OD in unlabeled IR images from TMPs. The proposed method, called UIR-Net, consists of two components: label generation and DL model construction. Two case studies from automotive manufacturing, RSW and hot stamping, are reported to demonstrate the feasibility and effectiveness of the proposed method. Note to Practitioners—This article was motivated by the problem of detecting objects such as weld nugget or metal piece in infrared (IR) imaging of thermomechanical processes (TMPs) in automotive manufacturing. The method is applicable to in situ IR images or videos that contain one or more objects to be detected. It only requires that the data are in image form and come from TMPs. Currently, there is no existing deep learning (DL)-based method for generic object detection (OD) in unlabeled IR images from TMPs. The proposed method takes advantages of the recent advancement in DL. This article suggests a systematic approach to build a DL-based OD model, named Unsupervised IR Image Net (UIR-Net), to extract objects from raw IR images collected for TMPs. A step-by-step procedure is given in this article to guide users through label generation, data quality evaluation, and model training to establish the proposed UIR-Net model. Results from resistance spot welding and hot stamping suggest that this approach is feasible and effective. It is one of the few generic OD works designed for manufacturing applications. Simple implementation, feasibility, and effectiveness make this method a suitable candidate for online data analytics and process monitoring in a wide range of manufacturing applications.
Implementación y configuración de un receptor de radio definido por software (SDR) para estudios de propagación

The constant development of digital systems in radio communications demands the adaptation of the current receiving equipment to the new technologies. In this context, a new Software Defined Radio based receiver is being implemented with the aim of carrying out different experiments to analyze the propagation of signals through the atmosphere from a satellite beacon. The receiver selected for this task is the PERSEUS SDR from the Italian company Microtelecom s.r. l. It is a software defined VLF-LF-MF-HF receiver based on an outstanding direct sampling digital architecture which features a 14 bit 80 MSamples/s analog-to-digital converter, a high-performance FPGA-based digital down-converter and a high-speed 480 Mbit/s USB2.0 PC interface. The main goal is to implement the related software and adapt the new receiver to the current working environment. In this paper, SDR technology guidelines are given and PERSEUS receiver digital signal processing is presented with the most remarkable results.
Tägliche HNO-Überweisung: Einseitige Nasenatmungsbehinderung und Cephalgien

Eine 67-jährige Patientin stellte sich mit einseitiger Nasenatmungsbehinderung links und rezidivierenden Cephalgien sowie einem Druckgefühl links frontal in der Ambulanz vor. Seit 2 Monaten bestand zudem eine persistierende Hyposmie. Eine konservative Behandlung erbrachte bisher keine zufriedenstellende Besserung. Eine B-Symptomatik wurde in der initialen Vorstellung nicht beklagt. Nebenbefundlich lag bei der Patientin ein arterieller Hypertonus vor, der medikamentös gut eingestellt war.
Near-Optimum Detection Performance of Power-Law Processors for Random Signals of Unknown Locations, Structure, Extent, and Arbitrary Strengths,

Abstract : A signal (if present) is located somewhere in a band of frequencies characterized by a total of N search bins with uniform noise normalized to unit power. The signal occupies an arbitrary set of M of these bins, where not only is extent M unknown, but in addition, the locations of the particular M occupied bins are unknown. Also, the average signal strengths per bin, ? S sub m! for 1 <or= m <or= M, are arbitrary and unknown. The optimum (likelihood ratio) processor can be derived but it cannot be constructed or realized, due to all the unknowns and the voluminous amount of searching required for this scenario. These deleterious conditions force adoption of an approximation to the optimum processor, which leads to a suboptimum processing technique, namely, the v-th power law device, which utilizes absolutely no knowledge of extent M, or locations, structure, or signal strengths ? S sub m! of any sort. The performance of this technique has been accurately evaluated quantitatively, both by analytic means and simulation, in terms of its false alarm and detection probabilities, as functions of N, M, the average signal powers per bin, ? S sub m! , and power law v. Additionally, the absolute limiting detection capability in this environment has been determined quantitatively by means of a new bounding procedure employing signal pairing, for any N and arbitrary signal powers ? S sub m! The performance levels attained by various actual power law processors lie within 0.1 dB of the ultimate level, for any value of M. The best single compromise power law processor, covering all values of N, is the v = 2.4 device, which loses less than 1.2 dB relative to the unrealistic optimum processor, which must know and use both M and ? S sub m!
Cuff-less Blood Pressure Measurement Using a Smart Phone

Blood pressure is vital sign information that physicians often need as preliminary data for immediate intervention during emergency situations or for regular monitoring of people with cardiovascular diseases. Despite the availability of portable blood pressure meters in the market, they are not regularly carried by people, creating a need for an ultra-portable measurement platform or device that can be easily carried and used at all times. One such device is the smartphone which, according to comScore survey is used by 26.2% of the US adult population. the mass production of these phones with built-in sensors and high computation power has created numerous possibilities for application development in different domains including biomedical. Motivated by this capability and their extensive usage, this thesis focuses on developing a blood pressure measurement platform on smartphones. Specifically, I developed a blood pressure measurement system on a smart phone using the built-in camera and a customized external microphone. the system consists of first obtaining heart beats using the microphone and finger pulse with the camera, and finally calculating the blood pressure using the recorded data. I developed techniques for finding the best location for obtaining the data, making the system usable by all categories of people. the proposed system resulted in accuracies between 90-100%, when compared to traditional blood pressure meters. the second part of this thesis presents a new system for remote heart beat monitoring using the smart phone. with the proposed system, heart beats can be transferred live by patients and monitored by physicians remotely for diagnosis. the proposed blood pressure measurement and remote monitoring systems will be able to facilitate information acquisition and decision making by the 9-1-1 operators.
A single, 30 minutes pretreatment with Isoflurane or Sevoflurane is not protective against high tidal volume induced lung injury in rats

Background: Volatile anesthetics (VA) may exert organ-protective effects in various experimental and clinical settings. Mechanical ventilation (MV) induces an inflammatory response and, depending on the ventilator settings chosen, injury in the lungs. It is unclear if prophylactic inhaled VA applied on healthy lungs prior to MV are protective regarding these effects. Methods: Healthy, spontaneously breathing rats were exposed for 30 minutes to either isoflurane (1.8 Vol %), sevoflurane (3.0 Vol %) or no VA. Animals were allowed to recover, intraperitoneally anesthetized and then mechanically ventilated for 4 hours with either high (21 ml/kg body weight) or low (9 ml/kg body weight) tidal volume (n = 12 per group). Cardiorespiratory parameters and systemic inflammation were assessed at the beginning and during mechanical ventilation. Cellular, non-cellular and histologic markers of pulmonary injury and inflammation were determined. Results: Irrespective of VA pretreatment, MV with high VT negatively affected markers of lung integrity such as arterial oxygenation and lung wet-to-dry ratio. Following VA pretreatment we found no protective effects on lung function but there were changes in some markers of inflammation such as a decrease in blood lymphocyte counts and an increase in interleukin 6 concentration in plasma and lung lavage fluid. These effects were heterogeneous regarding group allocation and time points. Conclusions: In this in in vivo animal model, prophylactic administration of inhaled VA was not beneficial or protective regarding ventilation induced lung injury. However, there were effects suggestive of a modulation of inflammatory markers associated with VA prophylaxis. The clinical or biological relevance of these findings so far remain unclear and should be subject to further studies.
A Drosophila P5B ‐Type ATPase is Required for Polyamine Transport

In eukaryotes P‐type ATPases are represented by a superfamily of membrane ion and lipid transporters divided into five subclasses. Substrates for subclasses 1–4 have been identified, however little is known about the substrate specificity of the P‐type 5A and 5B transporters. Here we show that the Drosophila P5B‐type ATPase is required for polyamine transport. The gene encoding the Drosophila P5B‐type ATPase produces multiple protein isoforms. Using a combination of genetic mutational analysis and CRISPR‐Cas9 gene editing we show that some of these isoforms are required for polyamine transport and others are not. In addition, we show that the Drosophila P5A‐type ATPase is not required for polyamine transport indicating a different substrate specificity for this transporter subfamily. In summary, we provide evidence for the first time of substrate specificity for the P5B‐type ATPase subfamily of transporters.
Real-Time Case-Based Reasoning in a Complex World

MAYOR is a real-time player of the popular simulation game SimCity. Since SimCity is a complex, dynamic world, players must make do with a partial understanding of how the world works. MAYOR contends with an incomplete world model by using a case-based planner which repairs plans on expectation failures, and with experience can improve its case library. SimCity players must also be able to quickly prioritize many tasks, which range from short to long-term. MAYOR uses utility estimates based on historical observation of the success of past tasks. This paper describes these approaches, and provides an overview of the MAYOR architecture.
A Reliable Agent System for Cloud Service Discovery using MFA Technique

Reliable Agent system for discovering suitable cloud services are very interesting and challenging process. In this research work, the proposed reliable agents’ authentication is enforced to provide security for cloud data’s. In this proposed method the agent client requires service with specific functional and technical requirements. For privacy concern, we provide authentication for each agent in cloud by using Multifactor authentication process. By using authentication id agents discover the cloud service based on service engine. In our system, we enhance the privacy of the cloud search engine by introducing new authentication technique. The authentication process is done by the use of multi factors such the secret key which is generated based on AES algorithm, biometric of user and the user’s password credentials. Focused Selection Contract Net Protocol is performed to find the relevant cloud service and it plays multiple roles in cloud service discovery. The similarity between the agent’s request and cloud service is computed based on service specifications and consumer service using Cloud ontology. Each legitimate agent provides reliable cloud services for agent clients (users). Service capability Table records the list of cloud service and their corresponding ID and services for reliable cloud discovery
IMPROVING SOFTWARE PRODUCTIVITY USING LEAN SIX SIGMA METHODS AND TOOLS

Although equations and models have existed for some time, the means to easily apply this knowledge to software product development to improve productivity have been long in coming. Macro tools such as Crystal Ball® software provide an excellent way to achieve software productivity aims. This was accomplished by modeling software development effort, effects due to software size and complexity as well as quality issues against a schedule constraint. As a Six Sigma project, a software spiral build cycle was modeled as the baseline. After doing a diagnostic/root cause analysis, various methods were identified that could effect improvement in software productivity such as more testing, improved peer reviews (PR), and reducing software complexity. The first improvement for the next build cycle focused on improving the peer review process. Crystal Ball was used to predict the impact of this change on productivity and in reducing the Cost of Poor Quality. Results on the Weather Data Analysis Capability (WDAC) program as of this posting were improvement in productivity by 25%, reduction in rework by 57% and reduction in cost due to poor quality by over $250K / build or $1M over two years for this particular contract. Recent results from the Joint Environmental Toolkit (JET) program have been over $336K per build so far.
Case Survey Method in Entrepreneurship Research

The Case Method and the Survey Method are frequently being used in social science research, even though each of them is known to have its own limitations. While the case method can offer richness of data and generate insightful understanding of the phenomenon, it is often criticized about the non-venerability of its findings beyond the case(s). The survey method, on the other hand, claims to produce generalizable findings but is criticized for the high probability of inaccuracies due to inappropriate and non-representative sampling, inadequate sample sizes, hurried and careless responses, and non-uniform understanding of the questions by the respondents as well as for the abstract nature of its findings that may not provide in-depth understanding of the phenomenon. The Case-Survey Method is an attempt to combine the richness of the case data with the generalization of the survey data by generating quantitative data from published cases and statistically processing them for generalizable findings. Based on a pioneering effort in using this method in entrepreneurship research for a study of ‘Entrepreneurial Heuristics’, the author explains the process of generating and analyzing data through content analysis of cases in order to develop generalizable propositions, besides discussing the various related issues of ensuring homogeneity and sufficiency of data, sample adequacy, scaling adequacy, reliability and validity. The paper concludes by listing the research situations in which the case-survey method is more appropriate and convenient than other methods.
Voltage-Controlled Magnetic Dynamics in Nanoscale Magnetic Tunnel Junctions

Spintronic devices, i.e., those utilizing the interaction of magnetic moments with electric voltages and currents in magnetic nanostructures, offer an exceptionally promising set of candidates for future electronic memory needs. Recently, the possibility of reversing the magnetization of nanoscale magnetic tunnel junction (MTJ) devices using the spin transfer torque (STT) effect has attracted the attention of industry and academia, since STT-MRAM has been demonstrated to be a strong candidate for a high speed, high density, and high endurance nonvolatile memory. Further, by replacing the current-driven (e.g., STT) switching mechanism for a voltage-controlled effect, a novel magnetoelectric RAM (MeRAM) architecture could result in considerable improvements in terms of density and dissipated energy during switching, both factors which are limited in STT-MRAM by the large currents required.In this dissertation, the possibility of exploiting the voltage-controlled magnetic anisotropy (VCMA) effect on nanoscale MTJ devices as the driving mechanism for MeRAM will be introduced. Experimental results on the demonstration of current-assisted and purely voltage-controlled switching in the thermally-activated and precessional regimes are presented. The write energies in VCMA-driven switching of nanoscale MTJs are shown to be at least one order of magnitude smaller compared to STT-based schemes. The advantages and challenges in terms of scalability and energy-efficiency of this voltage-driven approach are discussed. Further, a compact model for co-simulation of VCMA-driven MTJs with CMOS is established and validated against experimental data. The compact model is refined by the parameters extracted from a detailed characterization of the voltage-driven dynamics in these devices. This includes experimental results on the accurate characterization of the temperature dependence of the perpendicular anisotropy and the VCMA effect in MTJs, as well as on the influence of higher order anisotropies over MTJ dynamics measured via ferromagnetic resonance (FMR).
STUDY OF BIOFILM RESPONSE OF OLEA EUROPAEA TO UV RADIATIONS: AN IN VITRO APPROACH

A variety of bacteria, yeasts, and fungi colonize the aerial parts of plants termed as phyllosphere. Study of phylloshpere has gained considerable attention focusing on new non-invasive methods and bio-control agents of plant-associated microorganisms. The present study was designed to isolate microbes from phyllosphere of Olea europaea and assess their biofilm forming ability. Further, correlation between biofilm formation and ultraviolet (UV) radiation was observed. In total, four strains L1, L2, L3 and L4 were screened and purified from phyllosphere of O. europaea. Biochemical characterization and 16srRNA sequencing identified these strains as Erwinia herbicola (MN905123) , Micrococcus luteus (MN905159), Pseudomonas syringae (MN905161) and Bacillus subtilis (MN905164) . Physiological characterization revealed that strains exhibited optimum growth at pH 7 and temperature 37 ° C. Biofilm formation showed B. subtilis as the strongest biofilm former. Afterwards, UV radiation at various time intervals i.e., 3, 4, and 15 minutes indicated that bacterial biofilm was resistant upto 10 minutes UV exposure. A significant decrease (p≤0.01) in biofilm formation of mutant strains was observed after 15 minutes UV exposure thus pointing towards significantly reduced activity of the DNA repair gene. This study provided evidence that environments exposed to desiccation and/ ambient UV radiations could principally select for highly resistant microbial biofilms organisms against ionizing radiations, enabling these to withstand extreme conditions.
AN ENGLISH PLAY IS HOSTED BY FACEBOOK: STUDENTS' ACHIEVEMENTS AND ATTITUDES TOWARDS STUDYING AN ENGLISH PLAY USING THE FACEBOOK ENVIRONMENT

The aim of the present study is to present and study an innovative model for the collaborative and asynchronic learning of English Literature in high school within a "closed group" in Facebook. The importance of a closed group in the Facebook is identified in the literature concerning communities of knowledge which share a common goal, common knowledge and access to resources and communications for creating collaborative products [1] [2] [3] [4] [5]. The current research examines the attitudes and achievements of thirty Israeli twelfth grade students towards studying an English play in the Facebook environment. The intervention using Facebook took place over a period of one semester in a closed group created exclusively for the English literature class. The students' attitudes were analyzed in a quantitative and a qualitative method. Likert scale was applied for the quantitative measures along with metacognitive measures which allowed a richer expression of the learners’ perceptions and attitudes towards their learning experience. The findings revealed positive attitudes and satisfaction of the students concerning the learning experience, as well as a significant increase in positive attitudes and in metacognitive parameters after the end of the sessions. Yet the students’ attitudes were not found to correlate with their grades in the final paper and pencil test. This may indicate the contribution of additional factors to achievement besides positive attitudes and satisfaction, such as pre-disposition towards collaborative learning, quality and immediacy of teacher’s feedback, as well as the different characteristics and demands of the final test as compared to the Facebook learning environment.