Miracle-dz/UniFAR-FTUs huggingface dataset:
Induced drag ideal efficiency factor of arbitrary lateral-vertical wing forms

SUMMARY A relatively simple equation is presented for estimating the induced drag ideal efficiency factor e for arbitrary cross-sectional wing forms. This equation is based on eight basic but varied wing configurations which have exact solutions. The e function which relates the basic wings is developed statistically and is a continuous function of configuration geometry. The basic wing configurations include boxwings shaped as a rectangle, ellipse, and diamond; the V-wing; end-plate wing; 90 degree cruciform; circle dumbbell; and biplane. Example applications of the e equations are made to many wing forms such as wings with struts which form partial span rectangle, diamond, triangle, ellipse, and semi-ellipse wings; the ellipse and rectangle dumbbell wings; bow- tie, cruciform, winglet, and fan wings; and multi-wings. Derivations are presented in the appendices of exact closed form solutions found of e for the V-wing and 90 degree cruciform wing and for an asymptotic solution for multi- wings. INTRODUCTION With the objective of better economics through improved aerodynamic and structural shapes, many unconventional aircraft configurations are being investigated. Particularly desirable are wing forms for which both drag and structural stresses decrease. A measure of aerodynamics efficiency is the induced drag ideal efficiency factor denoted by e. Historically, exact solutions for induced drag have been found for several nonplanar wing configurations. These include the rectangle boxwing, end-plate wing, and biplane reported in reference 1; the ellipse boxwing and circle dumbbell wing from the methods of reference 2; and the diamond boxwing and V-wing in reference 3. These solutions form the basis for the present study. The objective of this study is to develop a generalized equation for esti- mating the ideal e for arbitrary yz-plane wing forms. For a given wing form the ideal e is the maximum that e can become, and at ideal e all the wing form surfaces have ideal loading distributions such that the total induced drag is minimized. A generalized equation for estimating e provides the designer with
Supporting Prosocial Behaviour in Online Communities through Social Media Affordances

Affordances are action possibilities that emerge from the relationship between the properties of an object and an interacting agent's capabilities. This poster examines how one type of affordance—anonymity—is enabled or constrained by six features of social media platforms. Our work is one step in a broader agenda to: (1) identify affordances that influence users' behaviour in online communities; (2) outline the social media features that enable or constrain those affordances; and, (3) experimentally determine whether certain affordances, or combinations of affordances, support prosocial behaviour. Prosocial behaviour in information and communication technologies (ICTs) is broadly viewed here as benefiting other individuals in online communities, which in turn could increase the subjective well‐being (SWB) of the community's members. SWB is characterized by high positive affect, low negative affect, and high life satisfaction.
Chaotic Fast Scrambling At Black Holes

Fast scramblers process information in characteristic times scaling logarithmically with the entropy, a behavior which has been conjectured for black hole horizons. In this note we use the AdS/CFT fold to argue that causality bounds on information flow only depend on the properties of a single thermal cell, and admit a geometrical interpretation in terms of the optical depth, i.e. the thickness of the Rindler region in the so-called optical metric. The spatial sections of the optical metric are well approximated by constant-curvature hyperboloids. We use this fact to propose an effective kinetic model of scrambling which can be assimilated to a compact hyperbolic billiard, furnishing a classic example of hard chaos. It is suggested that classical chaos at large N is a crucial ingredient in reconciling the notion of fast scrambling with the required saturation of causality.
Storied Past, Bright Future: A Provenance Jam Session

This panel will explore provenance of the past, present, and future in information science and technology, as both a concept and professional and intellectual value. The OED defines “provenance” as origin, source, ownership of an artwork, or guidance to determine authenticity. Provenance today is not limited to history domains. It can be used to describe what did happen (retrospective provenance), what will happen (prospective provenance), and what could happen (subjunctive provenance). Provenance transcends disciplines, and this panel is intended for anyone who is interested in the world of provenance. Perhaps echoing life in general, provenance is more about the journey than the destination. The goal of the panel is not to find an authoritative answer to why we choose one method or theory of provenance over another. Rather, this “jam session” will empower participants‐ panelists, moderators, and audiences alike‐ to frame and answer provenance questions for their own work.
Solutions to a Weak Gravity Quantum Equations and their Significance to the Dark Matter Problem.

A n ew t heory of dark matter is presented in which Schrodinger’s equation is applied to gravitational potentials on a macroscopic scale. Some of the stationary state solutions to this eq uation intrinsically possess all the physical properties required for dark matter, eliminating the need to introduce new particles or new physics. In particular the theory predicts the existence of certain states that have (1) negligible decay rates resulting in an inherent inability to gravitationally collapse or coalesce and (2) negligible elastic/ inelastic scattering rates with particles and electromagnetic radiation. Traditional baryonic particles occupying such eig enstates will be both invisible and weakly interacting. A vi able formation an d e volutionary scenario for t hese “eigenstructure” halos can be developed that allows the universe to be “all baryonic” but potentially stil l consistent with measured Big B ang Nucleosynthesis (BBN) ratios an d data from the W ilkinson Microwave Anisotropy Probe (WMAP). This paper outlines the properties, formation, evolution and possible detection of these new dark matter candidates.
Who Talked about What Regarding Derek Chauvin’s Trial: A Work‐In‐Progress Analysis

This work‐in‐progress study investigates the information sharing behavior of different levels of Twitter influencers within the context of the #BlackLivesMatter social movement and its related discussions #AlllivesMatter and #BlueLivesMatter during the 2‐week period around Derek Chauvin’s trial. Using qualitative content analysis and quantitative machine learning methods, we analyzed over one million retweets to test if different levels of influencers tend to spread different kinds of information in the discussions around #All/Black/BlueLivesMatter on Twitter. We found out that different levels of influencers tend to spread different information within and between the #All/Black/BlueLivesMatter, and we offer some explanations through the lens of curation logics. We suggest that different levels of influencers may be exposed to different incentives, and be facing different social norms, which leads to different information behaviors. This work‐in‐progress study contributes to updating the theory of curated logics, virality, and influencers, as well as provides empirical data for the discussions of the #BlackLivesMatter social movement and its related discussions of #AllLivesMatter and #BlueLivesMatter.
Characterizing Peer Review Comments of Academic Articles in Multiple Rounds

Peer review can evaluate the quality of academic articles involving the evaluation of some aspects, e.g., methodology, experiment, and aspects are usually the critical sections, substance and properties of the article concerned by reviewers. Previous research on content mining of peer review did not distinguish the round of reviews. Detecting differences peer review comments among different rounds can help us to understand the focus change of the reviewers in the different rounds. This paper takes the Nature Communications as an example to build the corpora of peer reviews in multiple rounds. We correlate review rounds and citations and extract aspects from the corpora to analyze the round characteristics of review comments. We find that there is no significant correlation between the review rounds and the citations frequency, but when the number of rounds is within 1–3, there is a weak positive correlation between the review rounds and citations. Additionally, the reviewers tend to pay more attention to the results analysis and the significance of the work firstly, and then they will focus on the details of the article, such as diagrams. This study can provide new ideas for peer review mining and the application of bibliometrics.
Quantifying Individual Research's Distance from the Trends based on Dynamic Topic Modeling

Research trends are the keys for researchers to decide their research agenda. However, only few works have tried to quantify how scholars follow the trends. This paper addresses this problem by proposing a novel measurement for quantifying how a scientific entity (paper or researcher) follows the hot topics in a research field. Specifically, the topic evolution and papers are vectorizing by dynamic topic modeling. Then the degree of hotness tracing is explored from three different perspectives: mainstream, short‐term direction, long‐term direction. Papers and researchers in the field of Computer Vision from 2006 to 2017 were selected to evaluate our method. Further study will show the results of topic evolution patterns and researchers' clusters.
Disrupting Design: A Multi‐level Technological Transition Study of Dribbble.com

As social media continues to integrate into people’s everyday lives, it provides a space for people to present their work and connect with others. This study seeks to understand how Dribbble.com, a site created in 2009 for visual designers to showcase their work, plays a role in the transformation of the visual design industry and design education. We use sociotechnical transitions theory to interpret 30 semi‐structured interviews with active Dribbble users. We find that the niche site Dribbble, along with the constellation of sites around it, are changing design regimes (the ways work gets done). Our Dribbble users report that the site changes how they develop and maintain their skills, find inspiration to solve design problems, keep up with trends, network with peers, produce and promote their portfolios and find jobs. However, the site also presents some challenges. For example, our interviewees indicate that they no longer receive constructive feedback on the platform. These emerging regimes are competing with, and coexisting with, existing design regimes. Our work contributes to social media studies by looking at under‐studied niche sites, like Dribbble, and how sites in the design space may be impacting the wider society.
A Longitudinal Study to Understand University Students' Searching as a Learning Process

Learning, or addressing a gap in one's knowledge, is an important motivator behind information‐seeking activities. The Search as Learning research community advocates that information search systems should be reconfigured to become educational platforms to foster learning and sensemaking. Modern search systems have yet to adapt to support this function. An important step to foster learning during search is to identify behavioral patterns that distinguish searchers gaining more vs. less knowledge during search. Previous efforts have primarily studied searchers in the short term, typically during a single lab session. Researchers are concerned over this ephemeral approach, as learning is not fleeting, and takes place over time. We propose an exploratory longitudinal study to analyze the long‐term searching behaviour of students enrolled in a university course, over the span of a semester. Our research aims are to identify if and how students’ searching behaviour changes over time, as they gain new knowledge on a subject; and how processes like motivation, metacognition, self‐regulation, and other individual differences moderate their “searching as learning” behaviour. Findings from this exploratory longitudinal study will help to build improved information search systems that foster human learning and sensemaking.
Predicting Search Task Difficulty through a Discrete‐Time Action Log Representation on Spectrum Kernel

Predicting perceived difficulty on a web search task is an open problem in the interactive information retrieval field. A common approach to tackle it, is through features obtained from full search sessions, which are then used to train classification models. In this poster we attempt to predict perceived task difficulty at different stages of the search process. To do so, we use the spectrum kernel for support vector machine (SVM) classification. Our preliminary results suggest that by using behavioral data from the first query segment, it is possible to provide timely classifications of whether a search task is perceived as hard or easy.
Cross‐Analysis of Researchers' Different Shared File Management Activities in Cloud Storage

Using cloud storage for collaborative projects has become common practice among researchers. However, despite its popularity, we lack an understanding of researchers' shared file management practices in cloud storage. More importantly, although researchers engage in various shared file management activities (i.e., create, update, organize, find/re‐find, keep, archive, and delete) in cloud storage, no studies have comparatively investigated these activities. Based on 475 responses from researchers collected through an online survey, this study examined researchers' different shared file management activities by focusing on the differences in the frequency, perceived ease of use, and satisfaction for each activity, and identified well‐supported activities as well as activities that require increased support. This study deepens our understanding of researchers' shared file management practice in cloud storage by cross‐examining an array of shared file management activities, and contributes to the development of tools and applications that better support researchers' shared file management in cloud storage.
Perturbing neutrino oscillations around the solar resonance

Atmospheric neutrinos at low energies, $E \lesssim 500$ MeV, are known to be a rich source of information on lepton mixing parameters. We formulate a simple perturbative framework to elucidate the characteristic features of neutrino oscillation at around the solar-scale enhancement due to the matter effect. The clearest message that we could extract from our perturbation theory is that CP violation in the appearance oscillation probability is large, a factor of $\sim 10$ times larger than CP violation at around the atmospheric-scale oscillation maximum. The underlying mechanism for this is that one of the suppression factors on the CP phase-dependent terms due to the smallness of $\Delta m^2_{21} / \Delta m^2_{31}$ is dynamically lifted by the solar-scale enhancement. Our framework has a unique feature as a perturbation theory in which a large $\Delta m^2_{31}$ term outside the key 1-2 sector for the solar-scale resonance does not yield sizable corrections. On the contrary, the larger the $\Delta m^2_{31}$, the smaller the higher-order corrections.
A Preliminary Study on App‐Switching during the Mobile Search

Users' mobile app switching behavior has recently received attention, particularly in the context of mobile search. This poster used Amazon MTurk to recruit 86 participants and used structured diaries and interviews to collect data on app switching behavior during the mobile search in daily scenes. We summarized the various behaviors after switching apps during the mobile search process. We also concluded why people switch apps based on information, personal characteristics, and mobile app features.
Large graphene-induced shift of surface-plasmon resonances of gold films: Effective-medium theory for atomically thin materials

Despite successful modeling of graphene as a 0.34-nm thick optical film synthesized by exfoliation or chemical vapor deposition (CVD), graphene induced shift of surface plasmon resonance (SPR) of gold films has remained controversial. Here we report the resolution of this controversy by developing a clean CVD graphene transfer method and extending Maxwell-Garnet effective medium theory (EMT) to 2D materials. A SPR shift of 0.24 is obtained and it agrees well with 2D EMT in which wrinkled graphene is treated as a 3-nm graphene/air layered composite, in agreement with the average roughness measured by atomic force microscope. Because the anisotropic built-in boundary condition of 2D EMT is compatible with graphene's optical anisotropy, graphene can be modelled as a film thicker than 0.34-nm without changing its optical property; however, its actual roughness, i.e., effective thickness will significantly alter its response to strong out-of-plane fields, leading to a larger SPR shift.
Which uber is mine?: identifying target in crowd of objects with RF analysis and AR visual tags

This paper presents ABC, the AR(augmented reality), and BLE combined target detector in a crowd of objects. The AR design of BLE analysis is a ready-to-use option for the ever-growing app-based ride market. The carefully designed AR tags and BLE beacons effectively collaborate to identify the target car. Such that a passenger can pinpoint the ride from a far distance. ABC consistently identify the target in a realistic environment through evaluations.
Radar Jamming Sensing Method Based on Vision Transformer

In the complex electromagnetic environment, it is difficult to perceive and recognize radar jamming signal under the condition of low jamming noise ratio, and the common deep learning method based on convolutional neural network is difficult to effectively deal with the problem. This paper first to Vision Transformer used in radar jamming task, perception of interference with the echo of the time-frequency domain to global context makes the modeling data, make up for the defect of convolution neural network focus on the local characteristics, extract the time-frequency domain data dependence characteristics before and after and then make perception judgment, more in line with the characteristics of radar jamming signal as a sequence of data. The proposed model has more accurate radar jamming perception ability at low jamming noise ratio than the typical ResNet-34 and AlexNet models, according to experimental results.
A deep learning urban traffic congestion forecast model blending the temporal continuity and periodicity

Traffic congestion has become an inevitable and difficult disease in the process of urban development, and it has also brought harm and hidden dangers to citizens' travel and urban development. The emergence of GCN solves the problem of capturing the spatial characteristics of urban road traffic. Based on this, we propose a new method that considers the periodicity of traffic patterns and builds a neural network model with multiple time scales to capture more detailed features. And the experiment proves that our model is better in predicting traffic congestion.
Crop Recognition Method Based on Gradient Features and Multilayer Perceptron with Application to Maize Recognition

At present, using time-series remote sensing data to identify large-scale crop planting types is of great significance for the government to formulate macro policies and guide agricultural production. As one of the main food crops in Inner Mongolia, it is very necessary to identify maize from large regional crops. Based on MODIS (Moderate-resolution Imaging Spectroradiometer) remote sensing images and artificial field sampling data, this paper constructs a unique crop temporal vegetation index data set along the Yellow Plain in Inner Mongolia; A multi-layer perceptron algorithm integrating NDVI (Normalized Difference Vegetation Index) gradient features is proposed for the first time to realize the intelligent recognition of corn in the Yellow plain of Inner Mongolia. The experimental results show that the accuracy of the model can reach 85.2%, which is better than the traditional machine learning method using the original NDVI features.
NeuLens: spatial-based dynamic acceleration of convolutional neural networks on edge

Convolutional neural networks (CNNs) play an important role in today's mobile and edge computing systems for vision-based tasks like object classification and detection. However, state-of-the-art methods on CNN acceleration are trapped in either limited practical latency speed-up on general computing platforms or latency speed-up with severe accuracy loss. In this paper, we propose a spatial-based dynamic CNN acceleration framework, NeuLens, for mobile and edge platforms. Specially, we design a novel dynamic inference mechanism, assemble region-aware convolution (ARAC) supernet, that peels off redundant operations inside CNN models as many as possible based on spatial redundancy and channel slicing. In ARAC supernet, the CNN inference flow is split into multiple independent micro-flows, and the computational cost of each can be autonomously adjusted based on its tiled-input content and application requirements. These micro-flows can be loaded into hardware like GPUs as single models. Consequently, its operation reduction can be well translated into latency speed-up and is compatible with hardware-level accelerations. Moreover, the inference accuracy can be well preserved by identifying critical regions on images and processing them in the original resolution with large micro-flow. Based on our evaluation, NeuLens outperforms baseline methods by up to 58% latency reduction with the same accuracy and by up to 67.9% accuracy improvement under the same latency/memory constraints.
Integrability properties of the dispersionless Kadomtsev-Petviashvili hierarchy

In the paper, we investigate integrability characteristics for the dispersionless Kadomtsev-Petviashvili hierarchy. These characteristics include symmetries, Hamiltonian structures, and conserved quantities. We give a Lax triad to construct a master symmetry and a hierarchy of non-isospectral dispersionless Kadomtsev-Petviashvili flows. These non-isospectral flows, together with the known isospectral dispersionless Kadomtsev-Petviashvili flows, form a Lie algebra, which is used to derive two sets of symmetries for the isospectral dispersionless Kadomtsev-Petviashvili hierarchy. By means of the master symmetry, symmetries, Noether operator, and conserved covariants, Hamiltonian structures are constructed for both isospectral and non-isospectral dispersionless Kadomtsev-Petviashvili hierarchies. Finally, two sets of conserved quantities and their Lie algebra are derived for the isospectral dispersionless Kadomtsev-Petviashvili hierarchy.
LQR Optimal Control of Four-steering Vehicle Based on Particle Swarm Optimization Algorithm

This paper proposes a linear quadratic controller based on particle swarm algorithm for the rear wheel control of four-wheel steering vehicle. Particle swarm optimization with fitness functions is used to optimize the coefficients of the weight matrix offline. The fuzzy rules following the controller is used if the road condition is terrible. The simulation results show that the LQR control model based on particle swarm optimization makes the trajectory tracking of the vehicle better and the side slip angle of the vehicle lower. It can be proved that the controller has positive effect on handling stability of the vehicle and safety of drivers.