Miracle-dz/UniFAR-FTUs huggingface dataset:
Academic performance and subtypes of psychological traits in undergraduate nursing students: A latent class analysis

Background: Undergraduate nursing students often encounter emotional problems due to academic stress. However, researchers have primarily focused on the impact of stressors, rather than understanding the subtypes of psychological state among students.Objectives: To identify subgroups of psychological traits among undergraduate nursing students using latent class analysis (LCA), and to examine the relationship between subgroups and exam failure risk.Design and Methods: A cross-sectional study was performed from May to July 2019. The Ruminative Responses Scale, Learning Burnout Questionnaire and Psychological Capital Scale were used to evaluate students’ psychological traits. Subjects were categorized into subgroups by latent class analysis. Cross-sectional comparisons and longitudinal regression analyses were subsequently performed.Settings and Participants: A total of 379 undergraduate nursing students from three medical colleges in Shanghai were investigated by cluster sampling method.Results: A total of four latent classes were identified based on the mean scores of rumination, learning burnout and psychological capital: Class 1 – General group (N=181, 47.76%); Class 2 – Negative psychologically dominated group (N=54, 14.25%); Class 3 – Positive psychologically dominated group (N=65, 17.15%); and Class 4 – Ambivalence psychological group (N=79, 20.84%). Students belonging into Class 1 and 2 displayed a higher rate of exam failure compared with those in Class 3 and 4 (p<0.05). Regression analysis showed that Class 1 (OR=2.108, 95%CI 1.240-4.118, p=0.006) and Class 2 (OR=2.694, 95%CI 1.229-6.293, p=0.016) independently predicted exam failure among undergraduate nursing students after adjusting for age, gender, grade and other academic related factors.Conclusions: Our findings provide practical implications for effective interventions that target each specific subgroup. Undergraduate nursing students with “Negative psychologically dominated group” were at a higher risk of exam failure. Urgent attention should be paid to address issues associated with psychological coping in students and their effect on learning outcomes.
People, places and playlists: modeling soundscapes in a mobile context

In this thesis we aim to examine whether music listening data combined with raw sensor data from a mobile device, can identify a mobile context and facilitate social interaction. Aiming to model not only the people, places and time that make up our mobile context, but also the constantly changing frame of mind reflected in the music we listen to, music has been utilized as yet another embedded sensor. A field study has been conducted with a handful of participants, constituting a small scale social network, carrying a smart phone for two weeks. All phone activities and data from embedded sensors was recorded, along the music being played on the device. In order to be capable of extracting contextual knowledge from the music being played, social tags retrieved from Last.fm has been utilized. Using a model that is capable of extracting the underlying topics behind the most frequently co-occurring words from the tag clouds, allows us to model the emerging concept of Soundscapes that captures the underlying semantic aspects of songs within a playlist. With visualization and similarity capabilities, the Soundscapes has not only been used to support the various scenarios related to music consumption, it has also provided the basis for identifying which conditions that determine music preferences in a given context. We report initial indications that listening patterns in terms of music genre preferences are influenced by whether the user is in a static environment or on the move. Applying a simple decision tree algorithm, in order to identify what contexts determine the preferences, indicates that our listening patterns change over time. This suggest that music applications utilizing context information must be designed to adapt to our shifting preferences as they continuously evolve. Furthermore, a conceptual model assembles the concepts in order to support music navigation, recommendation and exploration for the individual as well as in a social context. Thus, patterns emerge that indicate how it might be possible to share music by traversing the social graph and finding other users (friends) being in a similar context or listening to similar playlists. This leads us to a proof-of-concept application, SocialContextPlaylist, aiming to demonstrate how context-awareness, music similarity and social relations can be exploited in order to enhance the social experience of future mobile music applications by facilitating social interaction. The application is meant to serve as a foundation for further analysis and development of new creative innovative solutions in the emerging domain of future mobile entertainment applications and services.
What is the hydrologically effective size of a catchment?

Linking human activities and climate change with their consequences for water availability is a prerequisite for sustainable water management, which is traditionally performed at topographically delineated catchments. However, inter-catchment groundwater flow results in effective catchment sizes other than sizes suggested by topography. Here, we introduce the notion of effective catchment size and analyze the relative prevalence of substantial differences between topographic and effective catchment sizes using a large global dataset. We find that 1 in 3 catchments exhibit an effective catchment size either twice as large or half as small as its topographic size. These catchments will likely be affected by management activities such as groundwater pumping or land use change outside their topographic boundaries. Or they alternatively affect water resources beyond their topographic boundaries. The observed differences are strongly linked to aridity, slope, distance to coast, and topographic size. We show that our findings also hold for other large catchment databases, e.g., GRDC, CAMELS-US, and MOPEX . Our study provides a first-order identification of catchments where additional in-depth analysis of subsurface connectivity is needed to achieve sustainable water management. This supplement describes the global distributions of ECI, the CART (Classification And Regression Tree) analysis used to indicate the importance of different predictors (catchment attributes), and an analysis of the sensitivity of our results to the minimum number of ECI combinations obtained from the different precipitation and actual evapotranspiration datasets.
散熱鰭片幾何尺寸對高功率LED模組熱傳性能之研究 A Study of Heat-Sink Geometry on the Heat Transfer Performance of High-Power LED Modules

This study is to simulate the performance of high power LED array with three types of heat sink by CFD (Computational Fluid Dynamics) code. By way of changing the geometric size of heat sink, we analyze average junction temperature of the modular chips and compare the thermal behavior of LED array module under the conditions in forward and downward lighting. The geometries of heat sink include plate fin, square and cylinder pin fin. The results indicate that the LED array module shows the better performance on using the heat sink of plate fin under the height of 10-20mm in forward lighting. In addition, the thermal performances increase with the fin height on the heat sink of square and cylinder pin fin which are better than plate fin type even if the flow channel width becomes narrow in downward lighting.
Linking Across Data Granularity: Fitting Multivariate Hawkes Processes to Partially Interval-Censored Data

The multivariate Hawkes process (MHP) is widely used for analyzing data streams that interact with each other, where events generate new events within their own dimension (via self-excitation) or across different dimensions (via cross-excitation). However, in certain applications, the timestamps of individual events in some dimensions are unobservable, and only event counts within intervals are known, referred to as partially interval-censored data. The MHP is unsuitable for handling such data since its estimation requires event timestamps. In this study, we introduce the Partial Mean Behavior Poisson (PMBP) process, a novel point process which shares parameter equivalence with the MHP and can effectively model both timestamped and interval-censored data. We demonstrate the capabilities of the PMBP process using synthetic and real-world datasets. Firstly, we illustrate that the PMBP process can approximate MHP parameters and recover the spectral radius using synthetic event histories. Next, we assess the performance of the PMBP process in predicting YouTube popularity and find that it surpasses state-of-the-art methods. Lastly, we leverage the PMBP process to gain qualitative insights from a dataset comprising daily COVID-19 case counts from multiple countries and COVID-19-related news articles. By clustering the PMBP-modeled countries, we unveil hidden interaction patterns between occurrences of COVID-19 cases and news reporting.
Automatic Embedding of Stories Into Collections of Independent Media

We look at how machine learning techniques that derive properties of items in a collection of independent media can be used to automatically embed stories into such collections. To do so, we use models that extract the tempo of songs to make a music playlist follow a narrative arc. Our work specifies an open-source tool that uses pre-trained neural network models to extract the global tempo of a set of raw audio files and applies these measures to create a narrative-following playlist. This tool is available at https://github.com/dylanashley/playlist-stor y-builder/releases/tag/v1.0. 0
Revisiting spatio-temporal layouts for compositional action recognition

Recognizing human actions is fundamentally a spatio-temporal reasoning problem, and should be, at least to some extent, invariant to the appearance of the human and the objects involved. Motivated by this hypothesis, in this work, we take an object-centric approach to action recognition. Multiple works have studied this setting before, yet it remains unclear (i) how well a carefully crafted, spatio-temporal layout-based method can recognize human actions, and (ii) how, and when, to fuse the information from layout and appearance-based models. The main focus of this paper is compositional/few-shot action recognition, where we advocate the usage of multi-head attention (proven to be effective for spatial reasoning) over spatio-temporal layouts, i.e., configurations of object bounding boxes. We evaluate different schemes to inject video appearance information to the system, and benchmark our approach on background cluttered action recognition. On the Something-Else and Action Genome datasets, we demonstrate (i) how to extend multi-head attention for spatio-temporal layout-based action recognition, (ii) how to improve the performance of appearance-based models by fusion with layout-based models, (iii) that even on non-compositional background-cluttered video datasets, a fusion between layout- and appearance-based models improves the performance.
Certifiable Deep Importance Sampling for Rare-Event Simulation of Black-Box Systems

Rare-event simulation techniques, such as importance sampling (IS), constitute powerful tools to speed up challenging estimation of rare catastrophic events. These techniques often leverage the knowledge and analysis on underlying system structures to endow desirable efficiency guarantees. However, black-box problems, especially those arising from recent safety-critical applications of AI-driven physical systems, can fundamentally undermine their efficiency guarantees and lead to dangerous under-estimation without diagnostically detected. We propose a framework called Deep Probabilistic Accelerated Evaluation (DeepPrAE) to design statistically guaranteed IS, by converting black-box samplers that are versatile but could lack guarantees, into one with what we call a relaxed efficiency certificate that allows accurate estimation of bounds on the rare-event probability. We present the theory of Deep-PrAE that combines the dominating point concept with rare-event set learning via deep neural network classifiers, and demonstrate its effectiveness in numerical examples including the safety-testing of intelligent driving algorithms.
Active Perception and Control from PrSTL Specifications

Next-generation intelligent systems must plan and execute complex tasks with imperfect information about their environment. As a result, plans must also include actions to learn about the environment. This is known as active perception. Most active perception algorithms rely on reward or cost functions, which are usually challenging to specify and offer few theoretical guarantees. On the other hand, symbolic control methods can account for complex tasks using temporal logic but often do not deal well with uncertainties. This work combines symbolic control with active perception to achieve complex tasks in a partially observed and noisy control system with hybrid dynamics. Our basic idea is to employ a counterexample-guided-inductive-synthesis approach for control from probabilistic signal temporal logic (PrSTL) specifications. Our proposed algorithm combines bounded model checking (BMC) with sampling-based trajectory synthesis for uncertain hybrid systems. Active perception is inherently built into the framework because PrSTL formulas are defined in the chance domain.
An Open-Source Web App for Creating and Scoring Qualtrics-based Implicit Association Test

The Implicit Association Test (IAT) is a common behavioral paradigm to assess implicit attitudes in various research contexts. In recent years, researchers have sought to collect IAT data remotely using online applications. Compared to laboratory-based assessments, online IAT experiments have several advantages, including widespread administration outside of artificial (i.e., laboratory) environments. Use of survey-software platforms (e.g., Qualtrics) represents an innovative and cost-effective approach that allows researchers to prepare online IAT experiments without any programming expertise. However, there are some drawbacks with the existing survey-software as well as other online IAT preparation tools, such as limited mobile device compatibility and lack of helper functionalities for easy adaptation. To address these issues, we developed an open-source web app (GitHub page: https://github.com/ycui1mda/qualtrics_iat) for creating mobile-compatible Qualtrics-based IAT experiments and scoring the collected responses. The present study demonstrates the key functionalities of this web app and describes feasibility data that were collected and scored using the app to show the tool’s validity. We show that the web app provides a complete and easy-to-adapt toolset for researchers to construct Qualtrics-based IAT experiments and process the derived IAT data.
Online Learning in Adversarial MDPs: Is the Communicating Case Harder than Ergodic?

We study online learning in adversarial communicating Markov Decision Processes with full information. We give an algorithm that achieves a regret of O ( √ T ) with respect to the best ﬁxed deterministic policy in hindsight when the transitions are deterministic. We also prove a regret lower bound in this setting which is tight up to polynomial factors in the MDP parameters. We also give an ineﬃcient algorithm that achieves O ( √ T ) regret in communicating MDPs (with an additional mild restriction on the transition dynamics).
Concert halls and opera houses : music, acoustics, and architecture

Ch. 1 - Music and Acoustics / Ch.2 - The Language of Musical Acoustics / Ch.3 - 100 Concert Halls and Opera Houses / Ch.4 - Acoustics of Concert Halls / Ch.5 - Acoustics of Opera Houses / Appendix 1 - Terminology, Definitions, and Conversion Factors / Appendix 2 - Acoustical Data for Concert Halls and Opera Houses / Appendix 3 - Technical Data, Equations, and Sound Absorption / References / Name Index / Subject Index
Augmenting Edge Connectivity via Isolating Cuts

We give an algorithm for augmenting the edge connectivity of an undirected graph by using the isolating cuts framework (Li and Panigrahi, FOCS '20). Our algorithm uses poly-logarithmic calls to any max-flow algorithm, which yields a running time of $\tilde O(m + n^{3/2})$ and improves on the previous best time of $\tilde O(n^2)$ (Bencz\'ur and Karger, SODA '98) for this problem. We also obtain an identical improvement in the running time of the closely related edge splitting off problem in undirected graphs.
Universal scaling of mean skin friction in turbulent boundary layers and fully developed pipe and channel flows

Abstract A dynamically consistent scaling of mean skin friction in zero-pressure-gradient turbulent boundary layers and fully developed pipe and channel flows, is derived. Theoretical arguments are based on transfer of kinetic energy from mean flow to large eddies of turbulence. A single new velocity scale $M/\nu$ is shown to be dynamically relevant for scaling skin friction in all flows; $M$ is the planar kinematic momentum rate of the shear flow and $\nu$ is fluid kinematic viscosity. An asymptotic $-1/2$ power scaling law (in $M$–$\nu$ scaling) is shown to be universally applicable. It is observed that the semi-empirical finite-$Re$ skin friction model, resulting from the asymptotic scaling law, applies well to individual flows, but fails to describe all flows in a universal fashion. This non-universality could be due to the differences in flow boundary conditions at finite Reynolds numbers and flow geometry, that affect the outer-layer structures in these flows. It is argued that these differences may be simply absorbed by considering differences in the shapes of mean velocity profiles amongst these flows. An empirical correction to $M$–$\nu$ scaling is proposed based on Clauser's shape factor $G$. It is demonstrated that data from all flows in this new, semi-empirical $M$–$\nu$–$G$ scaling collapse remarkably well onto a single universal curve. The corresponding universal finite-$Re$ model in $M$–$\nu$–$G$ scaling is shown to describe this curve to an excellent accuracy. These results underscore the importance of a dynamically consistent approach towards revealing universality of skin friction scaling in wall turbulence.
Physarum Inspired Dynamics to Solve Semi-Definite Programs

Physarum Polycephalum is a slime mold that can solve shortest path problems. A mathematical model based on Physarum’s behavior, known as the Physarum Directed Dynamics, can solve positive linear programs. In this paper, we present a family of Physarum-based dynamics extending the previous work and introduce a new algorithm to solve positive Semi-Deﬁnite Programs (SDP). The Physarum dynamics are governed by orthogonal projections (w.r.t. time-dependent scalar products) on the aﬃne subspace deﬁned by the linear constraints. We present a natural generalization of the scalar products used in the LP case to the matrix space for SDPs, which boils down to the linear case when all matrices in the SDP are diagonal, thus, representing an LP. We investigate the behavior of the induced dynamics theoretically and experimentally, highlight challenges arising from the non-commutative nature of matrix products, and prove soundness and convergence under mild conditions. Moreover, we consider a more abstract view on the dynamics that suggests a slight variation to guarantee unconditional soundness and convergence-to-optimality. By simulating these dynamics using suitable discretizations, one obtains numerical algorithms for solving positive SDPs, which have applications in discrete optimization, e.g., for computing the Goemans-Williamson approximation for MaxCut or the Lov´asz theta number for determining the clique/chromatic number in perfect graphs.
The integrated concurrent enterprise

The previous chapter detailed the theoretical advantages that the MATE-CON process has over other popular forms of Concurrent Engineering. But if it is a superior approach to the design of complex systems, why hasn’t it become more widely adopted? Is there an inherent conflict between the paradigm of decomposition (that is the basis for most modern organizational structures), and the philosophies that power Integrated Concurrent Engineering and MATE-CON? In order to answer these questions, the implementation of these processes in a real organization must be studied in depth.
Light feedlot shade structure: an engeneer-design

SUMMARY An ideal shade structure would be durable, cheap, and would be able to be taken down easily and folded for storage in winter. Moreover, the tension cables of the structure would create no boundaries for animal circulation. Two types of shade structures are used by the feedlot industry: iron sheets attached to cables or shade cloth. Shade cloth is not the preferred roofing material as the stitching can deteriorate, requiring replacement of the cloth or stitching every 3-5 years. New technologies are now offering life spans of up to 10 years. However, shade cloth can be affected by hail damage, bird chewing or pen cleaning machinery exhaust pipes that can burn holes in the cloth. Cloth must be placed well above machinery. Current feedlot shade designs have evolved over time. Most are of simple design to minimise capital and ongoing maintenance costs. However, even though the structures are agricultural, structures of such size should be engineer-designed and certified. This includes the structural connection details, especially where tensioned cables are involved, and the fixing details for the corrugated cover sheeting. Constraint for support poles and for tension cables become one of the major structural part to achieve safety in working loose during high winds or a storm. RIASSUNTO Una struttura ombreggiante ideale dovrebbe essere durevole, economica e facilmente smontabile per riporla durante la stagione invernale. Inoltre i cavi di ancoraggio non dovrebbero creare impedimenti alla libera circolazione degli animali. Due tipi di coperture ombreggianti sono utilizzate abitualmente: le lamiere grecate in acciaio zingato o i teli ombreggianti. I teli ombreggianti non sono il materiale preferito poiche le cuciture si deteriorano rapidamente e necessitano un rimpiazzo dopo 3-5 anni. Nuove tecnologie costruttive hanno pero allungato la vita utile a 10 anni. Comunque i teli ombreggianti sono passibili di danni causati dalla grandine, dagli uccelli, o dai tubi di scappamento dei macchinari utilizzati per la pulizia dell’area sottostante, che possono produrre delle bruciature: per questo i teli dovrebbero essere posti ad una altezza ben al di sopra dei macchinari stessi. Le attuali tendenze progettuali si sono evolute nel tempo partendo da semplici richieste di economicita e bassi costi di gestione. Comunque, anche se le strutture sono utilizzate in agricoltura, esse dovrebbero essere progettate e certificate. Soprattutto nei particolari costruttivi di connessione quando sono utilizzati dei cavi in tensione, e nei particolari di fissaggio delle lamiere. Le fondazioni per i montanti e per i cavi di tensione divengono cosi una delle parti piu importanti per raggiungere una sicurezza contro rotture durante condizioni di vento forte o tempeste.