Miracle-dz/UniFAR-FTUs huggingface dataset:
On role extraction for digraphs via neighbourhood pattern similarity

We analyze the recovery of different roles in a network modeled by a directed graph, based on the so-called Neighborhood Pattern Similarity approach. Our analysis uses results from random matrix theory to show that, when assuming that the graph is generated as a particular stochastic block model with Bernoulli probability distributions for the different blocks, then the recovery is asymptotically correct when the graph has a sufficiently large dimension. Under these assumptions there is a sufficient gap between the dominant and dominated eigenvalues of the similarity matrix, which guarantees the asymptotic correct identification of the number of different roles. We also comment on the connections with the literature on stochastic block models, including the case of probabilities of order log(n)/n where n is the graph size. We provide numerical experiments to assess the effectiveness of the method when applied to practical networks of finite size.
Can We Achieve Fairness Using Semi-Supervised Learning?

Ethical bias in machine learning models has become a matter of concern in the software engineering community. Most of the prior software engineering works concentrated on finding ethical bias in models rather than fixing it. After finding bias, the next step is mitigation. Prior researchers mainly tried to use supervised approaches to achieve fairness. However, in the real world, getting data with trustworthy ground truth is challenging and also ground truth can contain human bias. Semi-supervised learning is a machine learning technique where, incrementally, labelled data is used to generate pseudo-labels for the rest of data (and then all that data is used for model training). In this work, we apply four popular semi-supervised techniques as pseudo-labelers to create fair classification models. Our framework, Fair-SSL , takes a very small amount (10%) of labeled data as input and generates pseudo-labels for the unlabeled data. We then synthetically generate new data points to balance the training data based on class and protected attribute as proposed by Chakraborty et al. in FSE 2021. Finally, classification model is trained on the balanced pseudo-labeled data and validated on test data. After experimenting on ten datasets and three learners, we find that Fair-SSL achieves similar performance as three state-of-the-art bias mitigation algorithms. That said, the clear advantage of Fair-SSL is that it requires only 10% of the labeled training data. To the best of our knowledge, this is the first SE work where semi-supervised techniques are used to fight against ethical bias in SE ML models. To facilitate open science and replication, all our source code and datasets are publicly available at https://github.
New physics searches with the International Large Detector at the ILC

Although the LHC experiments have searched for and excluded many proposed new particles up to masses close to 1 TeV, there are many scenarios that are difficult to address at a hadron collider. This talk will review a number of these scenarios and present the expectations for searches at an electron-positron collider such as the International Linear Collider. The cases discussed include the light Higgsino, the τ̃ slepton in the coannihilation region relevant to dark matter, as well as other BSM signatures. The studies are based on the ILD concept at the ILC. *** Particles and Nuclei International Conference PANIC2021 *** *** 5 10 September, 2021 *** *** Online *** ar X iv :2 11 1. 09 92 8v 1 [ he pex ] 1 8 N ov 2 02 1
Closing the loop: control and robot navigation in wireless sensor networks

Wireless sensor networks have received considerable attention for their potential as a cheap, easily deployed, distributed monitoring tool. Recently, researchers have begun to investigate the use of wireless sensor networks to drive closed-loop control systems. However, such composite systems are nontrivial to design due to the system interface dichotomy: control systems typically assume periodic, high frequency sensor updates whereas sensor networks provide aperiodic, low frequency, and laggy sensor updates. Utilizing robot navigation and pursuit-evasion games as benchmarks, our research focuses on improving control system performance by exploiting the properties of wireless sensor networks. We developed and deployed a real-world, medium-scale wireless sensor network for playing pursuit-evasion games. Using our experience from this deployment, we highlight the difficulties in using sensor network data to accurately localize robots. Several techniques designed to compensate for such difficulties are developed and incorporated into an unified system architecture. To test our architecture, an application-level simulator, accounting for many of the sensor network characteristics that frustrate control design, is developed. This simulator allows us to identify components of our system architecture that can improve the performance of control systems operating in networks of sensors. Amongst the components, intelligent path planning is identified as uniquely important in improving robot localization accuracy during navigation. Path planning techniques that use information maps, exploiting the knowledge of node topology and sensor models, are developed. Information is a metric for measuring the ability of a region in the environment to aid in robot localization. In particular, for each region in the environment, an information map computes the change in entropy expected by a robot in this area using Markov localization. We adapt sensor network models for use with information maps and verify the ability of such maps to improve robot localization. Additionally, automatic path planning techniques based on information maps are developed that minimize localization error. We compare the performance of these planners with other path planners, and demonstrate that this technique is effective for generating paths that increase the accuracy of localization while typically requiring fewer detections.
SVD-Embedded Deep Autoencoder for MIMO Communications

Using a deep autoencoder (DAE) for end-to-end communication in multiple-input multiple-output (MIMO) systems is a novel concept with significant potential. DAE-aided MIMO has been shown to outperform singular-value decomposition (SVD)-based precoded MIMO in terms of bit error rate (BER). This paper proposes embedding left- and right-singular vectors of the channel matrix into DAE encoder and decoder to further improve the performance of the MIMO DAE. SVD-embedded DAE largely outperforms theoretic linear precoding in terms of BER. This is remarkable since it demonstrates that DAEs have significant potential to exceed the limits of current system design by treating the communication system as a single, end-to-end optimization block. Based on the simulation results, at SNR=10dB, the proposed SVD-embedded design can achieve a BER of about 10−5 and reduce the BER at least 10 times compared with existing DAE without SVD, and up to 18 times compared with theoretical linear precoding. We attribute this to the fact that the proposed DAE can match the input and output as an adaptive modulation structure with finite alphabet input. We also observe that adding residual connections to the DAE further improves the performance.
Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-Aware Dual-Agent Framework

Ultrasound (US) imaging is commonly used to assist in the diagnosis and interventions of spine diseases, while the standardized US acquisitions performed by manually operating the probe require substantial experience and training of sonographers. In this work, we propose a novel dual-agent framework that integrates a reinforcement learning (RL) agent and a deep learning (DL) agent to jointly determine the movement of the US probe based on the real-time US images, in order to mimic the decision-making process of an expert sonographer to achieve autonomous standard view acquisitions in spinal sonography. Moreover, inspired by the nature of US propagation and the characteristics of the spinal anatomy, we introduce a view-specific acoustic shadow reward to utilize the shadow information to implicitly guide the navigation of the probe toward different standard views of the spine. Our method is validated in both quantitative and qualitative experiments in a simulation environment built with US data acquired from 17 volunteers. The average navigation accuracy toward different standard views achieves $5.18mm/5.25^{\circ }$ and $12.87mm/17.49^{\circ }$ in the intra- and inter-subject settings, respectively. The results demonstrate that our method can effectively interpret the US images and navigate the probe to acquire multiple standard views of the spine.
Bayesian geostatistical modeling for discrete‐valued processes

We introduce a flexible and scalable class of Bayesian geostatistical models for discrete data, based on nearest‐neighbor mixture processes (NNMP), referred to as discrete NNMP. To define the joint probability mass function (pmf) over a set of spatial locations, we build from local mixtures of conditional pmfs using a directed graphical model, with a directed acyclic graph that summarizes the nearest neighbor structure. The approach supports direct, flexible modeling for multivariate dependence through specification of general bivariate discrete distributions that define the conditional pmfs. In particular, we develop a modeling and inferential framework for copula‐based NNMPs that can attain flexible dependence structures, motivating the use of bivariate copula families for spatial processes. Moreover, the framework allows for construction of models given a pre‐specified family of marginal distributions that can vary in space, facilitating covariate inclusion. Compared to the traditional class of spatial generalized linear mixed models, where spatial dependence is introduced through a transformation of response means, our process‐based modeling approach provides both computational and inferential advantages. We illustrate the methodology with synthetic data examples and an analysis of North American Breeding Bird Survey data.
The defining moment : FDR's hundred days and the triumph of hope

This is the story of a political miracle -- the perfect match of man and moment. Franklin Delano Roosevelt took office in March of 1933 as America touched bottom. Banks were closing everywhere. Millions of people lost everything. The Great Depression had caused a national breakdown. With the craft of a master storyteller, Jonathan Alter brings us closer than ever before to the Roosevelt magic. Facing the gravest crisis since the Civil War, FDR used his cagey political instincts and ebullient temperament in the storied first Hundred Days of his presidency to pull off an astonishing conjuring act that lifted the country and saved both democracy and capitalism. Who was this man? To revive the nation when it felt so hopeless took an extraordinary display of optimism and self-confidence. Alter shows us how a snobbish and apparently lightweight young aristocrat was forged into an incandescent leader by his domineering mother; his independent wife; his eccentric top adviser, Louis Howe; and his ally-turned-bitter-rival, Al Smith, the Tammany Hall street fighter FDR had to vanquish to complete his preparation for the presidency. "Old Doc Roosevelt" had learned at Warm Springs, Georgia, how to lift others who suffered from polio, even if he could not cure their paralysis, or his own. He brought the same talents to a larger stage. Derided as weak and unprincipled by pundits, Governor Roosevelt was barely nominated for president in 1932. As president-elect, he escaped assassination in Miami by inches, then stiffed President Herbert Hoover's efforts to pull him into cooperating with him to deal with a terrifying crisis. In the most tumultuous and dramatic presidential transition in history, the entire banking structure came tumbling down just hours before FDR's legendary "only thing we have to fear is fear itself" Inaugural Address. In a major historical find, Alter unearths the draft of a radio speech in which Roosevelt considered enlisting a private army of American Legion veterans on his first day in office. He did not. Instead of circumventing Congress and becoming the dictator so many thought they needed, FDR used his stunning debut to experiment. He rescued banks, put men to work immediately, and revolutionized mass communications with pioneering press conferences and the first Fireside Chat. As he moved both right and left, Roosevelt's insistence on "action now" did little to cure the Depression, but he began to rewrite the nation's social contract and lay the groundwork for his most ambitious achievements, including Social Security. From one of America's most respected journalists, rich in insights and with fresh documentation and colorful detail, this thrilling story of presidential leadership -- of what government is for -- resonates through the events of today. It deepens our understanding of how Franklin Delano Roosevelt restored hope and transformed America. "The Defining Moment" will take its place among our most compelling works of political history.
Density delusion? Urban form and sustainable transport in Australian, Canadian and US cities

The relationship between population density and the share of travel by different modes has been a mainstay of the transport and urban planning literature, with mode split generally regarded as an outcome of density. As a result, the most popular recipe for mode shift away from the automobile is the 'compact city', or 'smart growth' as it is called in the United States. This paper questions the assumptions behind the compact city recipe for mode shift, by re-examining the historic and contemporary evidentiary basis for it. Interestingly, increasing city densities was originally advocated as a way to facilitate, rather than reduce, the dominance of the car.
An Intelligent System for Facility Management

A software system has been developed that monitors and interprets temporally changing (internal) building environments and generates related knowledge that can assist in facility management (FM) decision making. The use of the multi agent paradigm renders a system that delivers demonstrable rationality and is robust within the dynamic environment that it operates. Agent behaviour directed at working toward goals is rendered intelligent with semantic web technologies. The capture of semantics though formal expression to model the environment, adds a richness that the agents exploit to intelligently determine behaviours to satisfy goals that are flexible and adaptable. The agent goals are to generate knowledge about building space usage as well as environmental conditions by elaborating and combining near real time sensor data and information from conventional building models. Additionally further inferences are facilitated including those about wasted resources such as unnecessary lighting and heating for example. In contrast, current FM tools, lacking automatic synchronisation with the domain and rich semantic modelling, are limited to the simpler querying of manually maintained models.
An evaluation of dexterity and cutaneous sensibility tests for use with medical gloves

The ability of selected dexterity and cutaneous sensibility tests to measure the effect of medical glove properties (material, fit, and number of layers) on manual performance was analyzed. Manual performance testing of gloves to-date has focused on thicker gloves where the effects are more obvious. However, clinicians have reported dissatisfaction with some medical gloves and a perceived detriment to performance of new materials compared to latex. Three tests (Purdue Pegboard Test, Crawford Small Parts Dexterity Test, and Semmes-Weinstein Monofilaments) were performed by 18 subjects in five hand conditions (ungloved; best-fitting, loose-fitting and a double layer of latex examination gloves; best-fitting vinyl gloves). Tests were performed in the ungloved condition first, and the order of the gloved tests was randomized. Learning behavior was also measured. The Purdue test showed a significant effect of hand condition, but no differences between latex and vinyl. No significant effect of hand condition was found in the Crawford “Pins and Collars” test, but the “Screws” test showed promising discrimination between glove types. The Monofilaments test showed a significant effect of hand condition on cutaneous sensibility, particularly a reduction when “double-gloving,” but no significant differences between glove types. Existing tests show some ability to measure the effect of gloves and their properties on manual performance but are not comprehensive and require further validation. In order to fully describe the effects of medical gloves on manual performance, further tests should be designed with greater resolution and that better replicate clinical manual tasks.
Thermal pleasure in built environments: alliesthesia in different thermoregulatory zones

The principle of thermal alliesthesia indicates that the hedonic character of a thermal environment is determined as much by the general state of the subject as by the environment itself. An environmental stimulus that offsets or counters a thermoregulatory load error will be pleasantly perceived, and vice versa. Extant empirical evidence supporting thermal alliesthesia only exists for instances of core temperature deviation. Yet the reconciliation of alliesthesia with contemporary neurophysiological discourse (in the previous paper in this series) renders the concept directly relevant to everyday experiences in built environments where core temperature rarely deviates from neutral values. New experimental data are presented that explore alliesthesia in non-steady-state conditions across three different physiological states: thermoneutral; the upper and lower fringes of the thermoneutral zone; and mild excursions into the sweating and shivering regulatory zones. Thirteen human subjects evaluated the hedonic tone of a sequence of temperature step-changes and ramps. It was found that the psychophysiological principle of thermal alliesthesia operates within the thermoneutral zone, making it equally relevant to quotidian indoor environments as it is to the extremes found in traditional physiological research. Non-steady-state built environments can potentially offer spatial alliesthesia through carefully managed contrasts between local and mean skin temperature trends. Transitional zones are suggested as design solutions.
A study on the performance simulation of interior permanent magnet synchronous motor for electric vehicle considering nonlinear parameters

In this paper, we propose a simulation algorithm for driving characteristics of interior permanent magnet synchronous motor (IPMSM) for electric vehicle considering nonlinear parameters like d-axis and q-axis inductances as well as core loss. D-axis and q-axis inductances as well as core loss parameter are changed according to both current magnitude and current angle between q-axis and current axis because of nonlinearity of magnetic flux density in stator and rotor core. Therefor e, the calculation of d-axis and q-axis inductances with nonlinearity of magnetic flux density and core loss is performed by usi ng FEM. We can get precise driving performance of traction motor for electric vehicle adopting nonlinear d-axis and q-axis inductance table and core loss table.
The Relationship of Agricultural Mechanics Teachers’ Confidence in their Mathematic Skills and Confidence in their Ability to Teach Mathematic Skills

This study assessed Idaho agricultural science and technology teachers that taught at least one agricultural mechanics course during the 2006-2007 school year in terms of their confidence level in their own mathematics skills and their ability to teach mathematics skills. Data were collected using Dillman’s Tailored Design Method (2000) resulting in a response rate of 56.84% (N=95, n=54). Data were used to identify relationships between the level of confidence in mathematic abilities and confidence in ability to teach mathematics. A strong relationship was found between the confidence agricultural mechanics teachers had in their ability to perform mathematic tasks and their confidence to teach mathematic skills. The respondents were completely confident in their mathematics skills and their ability to teach mathematics skills. Respondents who were confident in their ability to teach mathematics skills are poised to implement mathematics instruction within the agricultural mechanics courses they teach. Introduction/Theoretical Framework High school students in the United States are consistently outperformed by those from Asian and some European countries on international assessments of mathematics and science, according to The Condition of Education 2006 report released by the U.S. Department of Education's National Center for Education Statistics (National Center for Educational Statistics, 2006). Previous research (Gliem & Elliot, 1988; Gliem, Lichtensteiger, & Hard, 1987; Hague & Phua, 1990; Miller & Gliem, 1994) has suggested that student scores on proficiency tests are not reflective of the number and level of mathematics courses completed by the students. To improve student performance in mathematics, Shinn, et al. (2003) shared that students will learn mathematics best when they can apply the concepts to real life. The context of agriculture can act as a unifying theme for mathematics and can add meaning to what students learn (Shinn, et al., 2003). Researchers (Melodia & Small, 2002; National Research Council, 1988; Shinn et al., 2003) agree that agricultural education provides opportunities for teaching mathematics. In 1988, the National Research Council recommended that all students needed a basic understanding of mathematics and science concepts and that teaching mathematics and science through agriculture was an effective approach to student learning. Melodia and Small (2002) believed that: It is important that a quality agriculture teacher recognize he/she already integrates science and mathematics in many ways. The next step is to be intentional about