Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Body Temperature in Adult Hospitalized Patients Using Axillary and Tympanic Measurement Methods

Thermoregulation is a vital body function that provides physiological hemostasis in individuals. Therefore, accurate measurement of body temperature is an important intervention, indicating level of physiological hemostasis [1]. Fever is defined as the increase of body temperature resulting from physiological response of the organism as an action-reaction response created against the inflammatory immune (defense) system of body temperature. Normal values of body temperature relative to measurement zones are considered as rectal 38 °C, oral 37.8 °C, and axillary 37.2 °C. The body temperature values above these values are called fever [2]. It is generally accepted that the normal measurement of the body temperature is between 36 °C and 38 °C. Body temperature below 36 °C is called hypothermia and above 38 °C is called hyperthermia (Fever) [3]. The measurement of body temperature in humans should be safer, easily applicable, non-invasive, and affordable method providing the most accurate measurement of the internal body temperature, and yielding to results in a short time. In addition, it should be unaffected by the peripheral conditions and facilitate measurement procedure for physicians and nurses. Results of the measurements should be reliable and not altered with the person performing the measurement. In the present study, we aimed to establish potential correlations through comparing body temperature measurements obtained with axillary contact digital thermometer (ACDT) and tympanic non-contact infrared thermometer (TNCIT). For the present study, body temperature of 111 adult female/male patients at age of 18 or older was obtained concurrently. A total of 1889 times of measurements were obtained noninvasively from 111 patients with an average of 17.02 times of measurements for per patient. The measurements obtained using TNCIT were 0.3 to 0.5 °C higher than the measurement average values acquired using ACDT. The results showed that there was a significant difference between the measurements of the TNCIT and the ACDT (p <0.001). If any doubt exits about the values of the body temperature measurement, the body temperature should be confirmed with other methods. Abstract
Flight patterns for clouds exploration with a fleet of UAVs

Modeling the cloud microphysics processes is essential to improve our understanding in climate changes and reduce the uncertainties in weather predictions. Aircrafts, remote sensing and ground-based infrastructures provide either sparse or coarse spatial measurements that are not sufficient to develop fine cloud models. UAVs have shown their ability to collect relevant cloud in-situ measures, and can be even more efficient when deployed in fleets. However, collecting relevant cloud data call for specific trajectories: this paper introduces a series of flight patterns dedicated to cloud exploration by a fleet of UAVs. The patterns definition comprise both a priori geometric information and real-time reactions to collected data. Results in simulated clouds assess their relevance for cloud in situ data collection.
Guiding MCTS with Generalized Policies for Probabilistic Planning

We examine techniques for combining generalized policies with search algorithms to exploit the strengths and overcome the weaknesses of each when solving probabilistic planning problems. The Action Schema Network (ASNet) is a recent contribution to planning that uses deep learning and neural networks to learn generalized policies for probabilistic planning problems. ASNets are well suited to problems where lo-cal knowledge of the environment can be exploited to improve performance, but may fail to generalize to problems they were not trained on. Monte-Carlo Tree Search (MCTS) is a forward-chaining state space search algorithm for optimal decision making which performs simulations to incrementally build a search tree and estimate the values of each state. Although MCTS can achieve state-of-the-art results when paired with domain-speciﬁc knowledge, without this knowledge, MCTS requires a large number of simulations in order to obtain reliable estimates in the search tree. By combining ASNets with MCTS, we are able to improve the capability of an ASNet to generalize beyond the distribution of problems it was trained on, as well as enhance the navigation of the search space by MCTS.
Generative Architectural Design and Build Strategies based on the Mapping of Human Behaviour

The introduction of robotics in construction offers the possibility of implementing design processes that incorporate unforeseen levels of complexity. Not only does this allow for the construction of high degrees of formal differentiation, but for the conception of projects with a highresolution integration of performative qualities. These performance aspects can include environmental and economical properties but also social and cultural mechanisms, which can be monitored through the tracing of human activities within architectural and urban spaces. This research presented here focuses on technologies and work flows that enable on-site data collection and construction implementation, in separated design and build phases or in an integrated process with continuous feedback. It discusses a series of small projects that explore new scenarios for the creation of architectural structures, experimenting with mobile and low-cost fabrication devices, connected to generative design algorithms driven by sensory technologies. These projects show that instead of working directly with the geometry of the final outcome, architects can begin to script individual and unique processes for generating structures, using rule-sets that capture the intelligence and underlying logic of materiality, organisation and spatial performance. We advocate a new paradigm where the architect is a 'process designer', aiming to generate emergent outcomes where the inherent complexity of the project is generated towards specific performance criteria related to human activities and inhabitation.
Less is More: Quantifying the Security Benefits of Debloating Web Applications

As software becomes increasingly complex, its attack surface expands enabling the exploitation of a wide range of vulnerabilities. Web applications are no exception since modern HTML5 standards and the ever-increasing capabilities of JavaScript are utilized to build rich web applications, often subsuming the need for traditional desktop applications. One possible way of handling this increased complexity is through the process of software debloating, i.e., the removal not only of dead code but also of code corresponding to features that a specific set of users do not require. Even though debloating has been successfully applied on operating systems, libraries, and compiled programs, its applicability on web applications has not yet been investigated. In this paper, we present the first analysis of the security benefits of debloating web applications. We focus on four popular PHP applications and we dynamically exercise them to obtain information about the server-side code that executes as a result of client-side requests. We evaluate two different debloating strategies (file-level debloating and function-level debloating) and we show that we can produce functional web applications that are 46% smaller than their original versions and exhibit half their original cyclomatic complexity. Moreover, our results show that the process of debloating removes code associated with tens of historical vulnerabilities and further shrinks a web application’s attack surface by removing unnecessary external packages and abusable PHP gadgets.
SVRG + + with Non-uniform Sampling

SVRG++ is a recent randomized optimization algorithm designed to solve nonstrongly convex smooth composite optimization problems in the large data regime. In this paper we combine SVRG++ with non-uniform sampling of the data points (already present in the original SVRG algorithm), leading to an algorithm with the best sample complexity to date and state-of-the art empirical performance. While the combination and the analysis of the algorithm is admittedly straightforward, our experimental results show significant improvement over the original SVRG++ method with the new method outperforming all competitors on datasets where the smoothness of the components varies. This demonstrates that, despite its simplicity and limited novelty, this extension is important in practice.
Dynamic Primitives in Human Manipulation of Non-Rigid Objects

This study examined strategies humans chose to manipulate an object with complex (nonlinear, underactuated) dynamics, such as liquid sloshing in a cup of coffee. The problem was simplified to the well-known cart-and-pendulum system moving on a horizontal line. This model was implemented in a virtual environment and human subjects manipulated the object via a robotic manipulandum. The task was to maneuver the system from rest to arrive at a target position such that no residual oscillations of the pendulum bob remained. Our goal was to test whether humans simplified control by employing dynamic primitives, specifically submovements. Experimental velocity profiles of the human movements were compared to those predicted by three different control models. Two models used continuous optimization-based control, the third control model was based on Input Shaping. Input Shaping is a method for controlling flexible objects by convolving a motion profile with impulses of appropriate amplitude and timing. To evaluate whether humans used Input Shaping, we decomposed the velocity profiles recorded from humans into submovements, as proxies for the convolved impulses. Comparing the motion profiles from the 3 models with the experimentally measured human profiles showed superior performance of the Input Shaping model. These initial results are consistent with our hypothesis that combining dynamic primitives, submovements, is a competent description of human performance and may provide a simpler alternative to computationally complex optimization-based methods of robot control.
Performance Comparison of ADTree and Naive Bayes Algorithms for Spam Filtering

Classification is an important data mining technique and could be used as data filtering in artificial intelligence. The broad application of classification for all kind of data leads to be used in nearly every field of our modern life. Classification helps us to put together different items according to the feature items decided as interesting and useful. In this paper, we compare two classification methods Naive Bayes and ADTree use to detect spam e-mail. This choice is motivated by the fact that Naive Bayes algorithm is based on probability calculus while ADTree algorithm is based on decision tree. The parameter settings of the above classifiers use the maximization of true positive rate and minimization of false positive rate. The experiment results present classification accuracy and cost analysis in view of optimal classifier choice for Spam Detection. It is point out the number of attributes to obtain a tradeoff between number of them and the classificationaccuracy.
On the Importance of Full Rank Initializations in Deep Neural Networks

Several methods have been proposed over the last few years for initializing the weights of neural networks in order to converge to better solutions or to reduce the time taken for convergence. On the other hand, there have been recent efforts connecting the full rank nature of weight matrices with the optimality of the final converged solution. In this work, we study the connection between popular initialization methods and the conditions necessary at optimal solution using deep linear networks with the squared loss. Through this connection, we attempt to provide a new explanation as to why these different initialization methods work well in practice.
Oil Price Uncertainty and the Macroeconomy

This paper examines the impact of oil price uncertainty shocks on economic activity. To do so, we define the uncertainty shock as the unanticipated component of oil price fluctuations. We find that this unanticipated component has a significantly negative and long-lasting impact on economic activity, with its cumulative effect on the US macroeconomy being much larger compared to that of popular uncertainty proxies such as stock market volatility and Economic Policy Uncertainty. Unlike our preferred measure of oil price uncertainty, volatility and the price spikes in oil futures prices present only a small and transitory effect on the real economy. Overall, our findings show that the US economy is significantly impaired when the degree of oil price unpredictability rises, while it is relatively immune to predictable fluctuations in the oil market.
C OMPUTER MODELING OF VIRTUAL E LECTRODES IN HUMAN VENTRICULAR MYOCARDIUM

Defibrillation of the heart is used widely to resuscitate patients with fibrillating heart, being the most effective therapy for this otherwise lethal disturbance of cardiac rhythm. The basic electrophysilogical mechanisms of this procedure are not well understood. The aim of this work is to investigate the conditions that influence the so called virtual electrodes that appear in human ventricular myocardium and also their effects. A two-dimensional and a threedimensional computer model of cardiac tissue is used. For this the temporal evolution of the transmembrane voltage is studied until the entire tissue is repolarized, the needed time interval being around 400 ms.
On definitions

Originating from a concern about the use of terms in hydroinformatics, this paper takes up the question of the nature of a definition and its consequences in a technical, and increasingly sociotechnical field like hydroinformatics. It is explained that the way things are defined is not so simple and so obvious as might at first appear, but is in fact an essentially ideological matter, having consequences not only within an individual discipline, but also for the applications of that discipline within a wider context. A mode of definition that satisfies the needs and expectations of hydroinformatics is then advanced. The notion of a defining self is introduced in order to explain how the influences of the mode of definition attains to a greater influence in social and ecological applications. This influence is exerted through communication, which is necessarily realised through the use of tokens. The four classes of tokens necessary for all processes of communication are introduced, delineated and defined. The way in which such broad concepts as ‘reality’ and ‘truth’ are defined is then seen to influence the definition of a model in the widest sense. Further definitions that are of central concern in hydroinformatics are developed accordingly. The paper concludes with a description of a phenomenon known only by its German name, as Gerede, that is at the root of much current confusion of terms in hydroinformatics, as in so many other places, and how its malign influences can be opposed.
NVIDIA DGX-1 With Tesla V 100 System Architecture The Fastest Platform for Deep Learning

The NVIDIA® DGX-1TM with Tesla V100 ( Figure 1) is an integrated system for deep learning. DGX-1 features 8 NVIDIA® Tesla® V100 GPU accelerators connect through NVIDIA® NVLinkTM, the NVIDIA highperformance GPU interconnect, in a hybrid cube-mesh network. Together with dual-socket Intel Xeon CPUs and four 100 Gb InfiniBand network interface cards, DGX-1 provides unprecedented performance for deep learning training. Moreover, the DGX-1 system software, powerful libraries, and NVLink network are tuned for scaling up deep learning across all eight Tesla V100 GPUs to provide a flexible, maximum performance platform for the development and deployment of deep learning applications in both production and research settings. DGX-1 with V100 GPUs achieves dramatically higher throughput than DGX-1 with previous-generation NVIDIA Tesla P100 GPUs, achieving up to 3.1x faster deep learning training for convolutional neural networks. High-performance NVLink GPU interconnect improves scalability of deep learning training, improving recurrent neural network training performance by up to 1.5x compared to slower PCIe interconnect. More productivity and performance benefits come from the fact that DGX-1 is an integrated system, with a complete optimized software platform aimed at deep learning that ensures DGX-1 outperforms similar off-the-shelf systems. Figure 1 NVIDIA DGX-1 NVIDIA DGX-1 With Tesla V100 System Architecture WP-08437-002_v01 | 1
With Similar Visual Angles, Larger Displays Improve Performance on Spatial Tasks

Large wall-sized displays are becoming prevalent. Although researchers have articulated qualitative benefits of group work on large displays, little work has been done to quantify the benefits for individual users. We ran two studies comparing the performance of users working on a large projected wall display to that of users working on a standard desktop monitor. In these studies, we held the visual angle constant by adjusting the viewing distance to each of the displays. Results from the first study indicate that although there was no significant difference in performance on a reading comprehension task, users performed about 26% better on a spatial orientation task done on the large display. Results from the second study suggest that the large display affords a greater sense of presence, allowing users to treat the spatial task as an egocentric rather than an exocentric rotation. We discuss future work to extend our findings and formulate design principles for computer interfaces and physical workspaces.
Algorithm for detecting vertical and horizontal lines in real-time image processing

Hough Transform is a common method used to extract lines in binary image,but it cannot meet the requirement of realtime processing because of its high complexity in computation. If the lines in the given directions are to be detected, the predigestion algorithm of the Hough Transform can be used. In this paper,based on the basic principles of Hough Transform,a new algorithm is suggested to detect with high speed the vertical and horizontal lines in binary image. The new algorithm is applied to detect the rectangle within the semiconductor chip,and a favorable effect is achieved.
Multiproduct Batch Plant Scheduling

Batch operations (e.g. batch drying, batch distillation, batch reactors etc.) are used preferentially in many chemical industries such as food, pharmaceuticals, cosmetics etc. Because of their unsteady state nature, scheduling of operations (when to produce what and in which order) is crucial in such plants. A principal feature of batch plants (Ku and Karimi et al., 1987) is production of multiple products using the same set of equipment. When products are similar in nature (e.g. different paints, milks etc.), they all need the same processing steps and hence pass through the same series of processing units. Plants producing such products are called multiproduct plants. Batches of same or different products are produced sequentially in such plants. Because of the different processing time requirements, the total time required to produce a set of batches (also called the makespan) depends on the sequence in which they are produced. To maximize the plant productivity, one may want to produce the batches in a sequence that minimizes the makespan. The plant schedule corresponding to such a sequence is then represented in the form of a Gantt chart (See an example in Fig. 1). Such a chart gives us a detailed time table of plant operation indicating exactly which products are produced by which units and at what times.
Evaluation of Human Action: Foucault’s Power/Knowledge Corollary

In today’s knowledge economy, innovating organizations are challenged to maximize a critically important asset -their human capital. The interaction of knowledge workers with information and communication technologies (ICTs) has presented a particularly interesting dynamic to researchers who study changes in related behavioral phenomena. One such phenomenon is self-monitoring. The following serves to outline the economics of self-monitoring and the integral role of ICTs in enabling knowledge workers to self-monitor. By merging Foucault’s power/knowledge ideas ABSTrACT
Effects of Social Contexts and Behaviors on Sex Steroids in Humans

In this paper we provide a critical review of research concerned with social/environmental mechanisms that modulate human neuroendocrine function.We survey research in four behavioral systems that have been shaped through evolution: competition, partnering, sex, and pregnancy/parenting. Generally, behavioral neuroendocrine research examines how hormones affect behavior. Instead, we focus on approaches that emphasize the effects of behavioral states on hormones (i.e., the “reverse relationship”), and their functional significance. We focus on androgens and estrogens because of their relevance to sexually selected traits. We conclude that the body of research employing a reversed or bidirectional perspective has an incomplete foundation: participants are mainly heterosexual men, and the functionality of induced shifts in neuroendocrine factors is generally unknown. This area of research is in its infancy, and opportunities abound for developing and testing intriguing research questions.
McNair Scholars: Identifying and Overcoming the Obstacles in Academia Faced by Underrepresented Populations

Using qualitative methodology, this study identifies obstacles faced by students underrepresented in academia (i.e., first-generation, low income, race/ethnic and gender minorities) participating in a high impact program, and how self-efficacy is maintained while combating obstacles. This study adds a new dimension to understanding the relationship between underserved populations in academia, self-efficacy and student success. Findings, based on interviews with nine McNair Scholars, identified multiple themes as both obstacles and benefits to their academic education e.g., minority status, support, knowledge, time/ organization, stability and community. A better understanding of the complex relationship between success and hardship for non-dominant identities in academia is needed before adequate changes can be implemented, promoting success for all students.