Miracle-dz/UniFAR-FTUs huggingface dataset:
Exceeding Computational Complexity Trial-and-Error Dynamic Action and Intelligence

Computational complexity is a core theory of computer science, which dictates the degree of difficulty of computation. There are many problems with high complexity that we have to deal, which is especially true for AI. This raises a big question: Is there a better way to deal with these highly complex problems other than bounded by computational complexity? We believe that ideas and methods from intelligence science can be applied to these problems and help us to exceed computational complexity. In this paper, we try to clarify concepts, and we propose definitions such as unparticularized computing, particularized computing, computing agents, and dynamic search. We also propose and discuss a framework, i.e., trial-and-error + dynamic search. Number Partition Problem is a well-known NP-complete problem, and we use this problem as an example to illustrate the ideas discussed.
Negative Results of Fusing Code and Documentation for Learning to Accurately Identify Sensitive Source and Sink Methods : An Application to the Android Framework for Data Leak Detection

Apps on mobile phones manipulate all sorts of data, including sensitive data, leading to privacy-related concerns. Recent regulations like the European GDPR provide rules for the processing of personal and sensitive data, like that no such data may be leaked without the consent of the user.Researchers have proposed sophisticated approaches to track sensitive data within mobile apps, all of which rely on specific lists of sensitive SOURCE and SINK API methods. The data flow analysis results greatly depend on these lists’ quality. Previous approaches either used incomplete hand-written lists that quickly became outdated or relied on machine learning. The latter, however, leads to numerous false positives, as we show.This paper introduces CoDoC, a tool that aims to revive the machine-learning approach to precisely identify privacy-related SOURCE and SINK API methods. In contrast to previous approaches, CoDoC uses deep learning techniques and combines the source code with the documentation of API methods. Firstly, we propose novel definitions that clarify the concepts of sensitive SOURCE and SINK methods. Secondly, based on these definitions, we build a new ground truth of Android methods representing sensitive SOURCE, SINK, and NEITHER (i.e., no source or sink) methods that will be used to train our classifier.We evaluate CoDoC and show that, on our validation dataset, it achieves a precision, recall, and F1 score of 91% in 10-fold cross-validation, outperforming the state-of-the-art SUSI when used on the same dataset. However, similarly to existing tools, we show that in the wild, i.e., with unseen data, CoDoC performs poorly and generates many false positive results. Our findings, together with time-tested results of previous approaches, suggest that machine-learning models for abstract concepts such as privacy fail in practice despite good lab results. To encourage future research, we release all our artifacts to the community.
Score vs. Winrate in Score-Based Games: which Reward for Reinforcement Learning?

In the last years, DeepMind algorithm AlphaZero has become the state of the art to efficiently tackle perfect information two-player zero-sum games with a win/lose outcome. However, when the win/lose outcome is decided by a final score difference, AlphaZero may play score-suboptimal moves, because all winning final positions are equivalent from the win/lose outcome perspective. This can be an issue, for instance when used for teaching, or when trying to understand whether there is a better move. Moreover, there is the theoretical quest of the perfect game. A naive approach would be training a AlphaZero-like agent to predict score differences instead of win/lose outcomes. Since the game of Go is deterministic, this should as well produce outcome-optimal play. However, it is a folklore belief that "this does not work". In this paper we first provide empirical evidence to this belief. We then give a theoretical interpretation of this suboptimality in a general perfect information two-player zero-sum game where the complexity of a game like Go is replaced by randomness of the environment. We show that an outcome-optimal policy has a different preference for uncertainty when it is winning or losing. In particular, when in a losing state, an outcome-optimal agent chooses actions leading to a higher variance of the score. We then posit that when approximation is involved, a deterministic game behaves like a nondeterministic game, where the score variance is modeled by how uncertain the position is. We validate this hypothesis in a AlphaZero-like software with a human expert.
On Solar Photovoltaic Parameter Estimation: Global Optimality Analysis and a Simple Efficient Differential Evolution Method

The literature presents a vast array of advanced metaheuristic methods for photovoltaic parameter estimation. However, the focus of this study is not to introduce another new method into this already crowded field. Instead, we examine two important but often overlooked questions: (i) are existing results globally optimal, and (ii) can a simpler method achieve comparable performance. We conduct case studies using two widely used I-V curve datasets. To address the first issue, we develop a branch and bound algorithm that, despite its sluggishness, either certifies the global minimum or provides a tight upper bound. These values serve as useful benchmarks for fair metaheuristic evaluation and further development. To answer the second question, our extensive examination and comparison surprisingly reveal that a basic differential evolution (DE) algorithm can achieve the certified global minimum or obtain the best known result. Additionally, the DE algorithm's runtime is much shorter than that of current state-of-the-art metaheuristic methods, making it a great choice for time-sensitive applications. This remarkable finding suggests that using increasingly complex metaheuristics in ordinary PV parameter estimation problems might be unnecessary. Finally, we discuss the implications of these outcomes and propose the simple DE method as the premier choice for industrial applications.
An Equity-Focused Redesign of an Introductory Organismal Biology Lab Course To Develop Foundational Scientific Practices

Laboratory courses can serve as important avenues to equitably support introductory biology students to develop foundational scientific literacy skills while experiencing the authentic research process. We present a model for an equity-focused redesign of an introductory organismal biology laboratory course at a teaching institution with limited research infrastructure. ABSTRACT Laboratory courses can serve as important avenues to equitably support introductory biology students to develop foundational scientific literacy skills while experiencing the authentic research process. We present a model for an equity-focused redesign of an introductory organismal biology laboratory course at a teaching institution with limited research infrastructure. We incorporated elements of inquiry, structure, and climate into our three redesigned course components: weekly research investigations, skill-building assignments, and student-designed group projects. Students were trained in the research process through weekly experiments using locally relevant model organisms, collecting and analyzing novel data and writing brief results sections in the conventions of a research journal article. Student groups then collaborated to complete a student-designed research project and poster presentation using one of the model organisms. Through weekly inquiry labs and practice in skill-building assignments, most students in the sample mastered skills in analyzing, graphing, and writing about experimental results. Notably, students mastered skills that were practiced more frequently throughout the lab course, demonstrating the value of repeated and scaffolded practice. Students reported significant gains in self-efficacy and science identity, as well as sense of project ownership. Student gains were influenced by instructor but not their major or the semester in which they took the course, and growth occurred across students regardless of their incoming score on the presemester survey. This intentional course design model, combined with consistent expectations for instructors across multiple sections, has the potential to equitably support students with a range of prior knowledge and experiences to make meaningful gains in science literacy skills during an introductory semester.
Wahlfach „Sinnhaftigkeit und Selbstfürsorge im Klinikalltag“

Wie können Rollenbilder bei zukünftigen Ärztinnen oder Ärzten schon im Studium thematisiert werden? Dieser Beitrag beschreibt einen Ansatz zur Vermittlung „Übergeordneter Kompetenzen“ aus dem neuen Nationalen Kompetenzbasierten Lernzielkatalog Medizin NKLM 2.0.
Customer Volume Prediction Using Fusion of Shared-private Dynamic Weighting over Multiple Modalities

Customer volume prediction is crucial for a variety of urban applications, such as store location selection. So far, the key challenge lies in how to fuse multiple modalities from different data sources, on account of the massive amount of data accessible, for example, spatio-temporal data and satellite images. In this article, we investigate three dynamic weighting ensemble learning models to fuse spatio-temporal features and visual features for predicting customer volume in the urban commercial district of interest. Specifically, we propose the shared-private dynamic weighting model by incorporating graph neural networks, which is proposed to capture geographic dependencies (i.e., competitiveness or dependencies) between urban commercial districts in an end-to-end manner. To the best of our knowledge, it is the first work to utilize graph neural networks to model such geographic relationships. We conduct a series of experiments to demonstrate the effectiveness of the proposed models based on two real datasets. Furthermore, an elaborated visualization method is performed for knowledge discovery.
The Effects of Natural Sounds and Proxemic Distances on the Perception of a Noisy Domestic Flying Robot

When flying robots are used in close-range interaction with humans, the noise they generate, also called consequential sound, is a critical parameter for user acceptance. We conjecture that there is a benefit in adding natural sounds to noisy domestic drones. To test our hypothesis experimentally, we carried out a mixed-methods research study (N = 56) on reported user perception of a sonified domestic flying robot with three sound conditions at three distances. The natural sounds studied were, respectively, added to the robot’s inherent noises during flying; namely, a birdsong and a rain sound, plus a control condition of no added sound. The distances studied were set according to proxemics; namely, near, middle, and far. Our results show that adding birdsong or rain sound affects the participants’ perceptions, and the proxemic distances play a nonnegligible role. For instance, we found that participants liked the bird condition the most when the drone was at far, while they disliked the same sound the most when at near. We also found that participants’ perceptions strongly depended on their associations and interpretations deriving from previous experience. We derived six concrete design recommendations.
Fully Linear Graph Convolutional Networks for Semi-Supervised and Unsupervised Classification

This article presents FLGC, a simple yet effective fully linear graph convolutional network for semi-supervised and unsupervised learning. Instead of using gradient descent, we train FLGC based on computing a global optimal closed-form solution with a decoupled procedure, resulting in a generalized linear framework and making it easier to implement, train, and apply. We show that (1) FLGC is powerful to deal with both graph-structured data and regular data, (2) training graph convolutional models with closed-form solutions improve computational efficiency without degrading performance, and (3) FLGC acts as a natural generalization of classic linear models in the non-Euclidean domain (e.g., ridge regression and subspace clustering). Furthermore, we implement a semi-supervised FLGC and an unsupervised FLGC by introducing an initial residual strategy, enabling FLGC to aggregate long-range neighborhoods and alleviate over-smoothing. We compare our semi-supervised and unsupervised FLGCs against many state-of-the-art methods on a variety of classification and clustering benchmarks, demonstrating that the proposed FLGC models consistently outperform previous methods in terms of accuracy, robustness, and learning efficiency. The core code of our FLGC is released at https://github.com/AngryCai/FLGC.
Real-estate price prediction with deep neural network and principal component analysis

Abstract Despite the wide application of deep neural networks (DNN) models, their application over small-sized real-estate price prediction is limited due to the reduced prediction accuracy and the high-dimensionality of the dataset. This study motivates small-sized real-estate agencies to take DNN-driven decisions using the available local dataset. To improve the high-dimensionality of real-estate price datasets and thus enhance the price-prediction accuracy of a DNN model, this paper adopts principal component analysis (PCA). The PCA benefits in improving the prediction accuracy of a DNN model are threefold: dimensionality reduction, dataset transformation and localisation of influential price features. The results indicate that, through the PCA-DNN model, the transformed dataset achieves higher accuracy (90%–95%) and better generalisation ability compared with other benchmark price predictors. The spatial and building age proved to have the most impact in determining the overall real-estate price. The application of PCA not only reduces the high-dimensionality of the dataset but also enhances the quality of the encoded feature attributes. The model is beneficial in real-estate and construction applications, where the absence of medium and big datasets decreases the price-prediction accuracy.
Effect of Maltodextrin, Arabic Gum, and Beetroot Juice Concentration on the Powder Properties of Spray-Dried Beetroot-Skim Milk Mixtures

Abstract Milk products are widely consumed as a beverage and used as the main ingredient in many food applications. Beetroot is suitable as an ingredient for the natural red color in food products. Color stability of natural colorants is mainly affected by mixtures of materials and temperatures. The effect of beetroot juice concentration and types of carrier agents on the physical properties of the red-colored skim-milk powder and rehydrated powder at different temperatures were studied. Beet juice concentration significantly affects the redness (a*) and lightness (L*) of skim milk in both powders and reconstituted forms. Powder with mixtures of maltodextrin and Arabic gum showed better retention of redness, high solubility, low moisture content, water activity, and less hygroscopic. The finding from this study may benefit food product development, beverage, and confectionery in the food industry.
WFPC2 Observations of Two dSph Galaxies in the M81 Group

We have obtained HST WFPC2 and ground-based images of two low surface brightness dwarf spheroidal galaxies in the M81 group, FM1 and KKH57. Their colour-magnitude diagrams show red giant branches with tips at I = 23.77 +- 0.14 and I = 23.97 +- 0.17, respectively. The derived true distance moduli, 27.66 +- 0.16 and 27.96 +- 0.19, agree well with the mean distance modulus of the M81 group, 27.84 +- 0.05. Absolute V magnitudes of the galaxies (-11.46 and -10.85), their colours ((B-V) = 0.88 and 0.80), and central surface brightnesses ($\Sigma_{0,V}$ = 24.8 and 24.4 mag/$\sq\arcsec$) are in the range of other dSph companions of M81, M31, and Milky Way. With two new objects the maximum projected radius of the dwarf spheroidal subsystem around M81 is 380 kpc.
A Bayesian regression analysis of truck drivers’ use of cooperative adaptive cruise control (CACC) for platooning on California highways

Abstract Cooperative Adaptive Cruise Control (CACC), as an advanced version of adaptive cruise control (ACC), automates brake and engine controls based on the information received from wireless V2V communications and remote sensors, enabling smaller vehicle-following time gaps. It can improve the safety of vehicle platooning and increase fuel savings. As an extension of our previous investigation of truck drivers’ acceptance of CACC, this case study investigates factors affecting the use of CACC for truck platooning. Nine commercial fleet drivers were recruited to operate two following trucks in a CACC-enabled string on freeways in Northern California. We analyzed the usage of CACC time gaps and its correlation with truck drivers’ stated preferences for these time gaps, and we found that the highest preferred Gap 3 (1.2 s) was used the most. Moreover, a Bayesian regression model was built to show that truck drivers are more likely to disengage CACC when driving in low-speed traffic or on downgrades where this CACC could not provide sufficient braking. In high-speed traffic or on upgrades, truck drivers are more likely to engage CACC, particularly at Gap 3. Truck position, however, does not affect truck drivers’ time gap selection. The findings encourage the adoption of CACC in the trucking industry through implementing driver-preferred time gaps and responsive braking systems, and operating on routes with minimal interference to truck speeds.
Which Types of Amotivation Affect School Absenteeism and Academic Performance of Middle School Students? A Structural Equation Modeling Analysis

Abstract This study investigated the mediation role of school absenteeism in the relationships between different types of amotivation and academic performance. Structural equation modeling (SEM) was used to assess the hypotheses of study. Participants were 311 eighth-grade middle school students who were absent from school for at least three days during the 2018–2019 spring semester. Data were collected from Academic Amotivation Inventory, which comprises four different types of amotivation. The significance of the indirect effects of amotivation types on academic performance was tested by using the bootstrapping method. Results demonstrated that each of four amotivation types were robust predictors of absenteeism. SEM analysis results showed that types of amotivation, including ability beliefs, effort beliefs, and value placed on task directly predicted students’ academic performance. Bootstrap analysis revealed that each of those types had an indirect effect on academic performance via the partial mediation of absenteeism. However, task characteristics did not directly predict academic performance, and this relationship was mediated fully through absenteeism. Absenteeism acted as a mediator between amotivation types and academic performance. Findings provided preliminary evidence of negative effects of different types of amotivation that, when combined with absenteeism rates, may lead to high risk for students’ academic performance.
An Algorithm for Computation of Radial-Harmonic-Fourier Moments

A fast algorithm for the computation of radial- harmonic-fourier moments (RHFM) is presented in this paper. This algorithm is based on some properties of the radial- harmonic-fourier (RHF) basis functions. As RHF basis functions have specific symmetry or anti-symmetry about the x-axis, the y-axis, the origin, and the straight line of y=x, we can compute one eighth range of the RHF basis functions instead of the whole. Both theoretical analysis and experimental testing show that the fast algorithm makes the time of the computation shorter than the direct method.
Extension of the Piecewise Parabolic Method to One-Dimensional Relativistic Hydrodynamics

An extension to 1D relativistic hydrodynamics of the piecewise parabolic method (PPM) of Colella and Woodward using an exact relativistic Riemann solver is presented. Results of several tests involving ultrarelativistic flows, strong shocks and interacting discontinuities are shown. A comparison with Godunov's method demonstrates that the main features of PPM are retained in our relativistic version.
A new radio molecular line survey of planetary nebulae

Certain planetary nebulae (PNe) contain shells, filaments, or globules of cold gas and dust whose heating and chemistry are likely driven by UV and X-ray emission from their central stars and from wind-collision-generated shocks. We present the results of a survey of molecular line emission in the 88–236 GHz range from nine nearby (<1.5 kpc) planetary nebulae spanning a range of UV and X-ray luminosities, using the 30 m telescope of the Institut de Radioastronomie Millimétrique. Rotational transitions of thirteen molecules, including CO isotopologues and chemically important trace species, were observed and the results compared with and augmented by previous studies of molecular gas in PNe. Lines of the molecules HCO+, HNC, HCN, and CN, which were detected in most objects, represent new detections for four planetary nebulae in our study. Specifically, we present the first detections of 13CO (1–0, 2–1), HCO+, CN, HCN, and HNC in NGC 6445; HCO+ in BD+30°3639; 13CO (2–1), CN, HCN, and HNC in NGC 6853; and 13CO (2–1) and CN in NGC 6772. Flux ratios were analyzed to identify correlations between the central star and/or nebular UV and X-ray luminosities and the molecular chemistries of the nebulae. This analysis reveals a surprisingly robust dependence of the HNC/HCN line ratio on PN central star UV luminosity. There exists no such clear correlation between PN X-rays and various diagnostics of PN molecular chemistry. The correlation between HNC/HCN ratio and central star UV luminosity demonstrates the potential of molecular emission line studies of PNe for improving our understanding of the role that high-energy radiation plays in the heating and chemistry of photodissociation regions.
Privacy Amplification for Episodic Training Methods

It has been shown that differential privacy bounds improve when subsampling within a randomized mechanism. Episodic training, utilized in many standard machine learning techniques, uses a multistage subsampling procedure which has not been previously analyzed for privacy bound amplification. In this paper, we focus on improving the calculation of privacy bounds in episodic training by thoroughly analyzing privacy amplification due to subsampling with a multi-stage subsampling procedure. The newly developed bound can be incorporated into existing privacy accounting methods.
Studies of the Marchesi model for female registration

This paper reports a series of studies in which a model for female registration central to the well‐documented vocal method of Mathilda Marchesi (1821–1913) and consistent with the views of today's artists Joan Sutherland and Marily Horne was utilized. Marchesi produced more prima donnas (e. g., Nellie Melba, Emma Calve, Emma Eames) than any other teacher in the history of Western vocal music. Equalized and nonequalized paired tones (same fundamental frequency, sound level—A‐weighting, and vowel—/α/) in the areas of register boundaries (upper limit, chest register—varies from E4, 330 Hz, to F4‐sharp, 370 Hz; upper limit, medium register—varies from F5, 698 Hz, to F5‐sharp, 740 Hz) were recorded (1) on a high‐fidelity tape recorder for Sonagraphic analysis, (2) by a pneumotachographic system for measurement of airflow rate, and (3) with photography for analysis of laryngeal movements. Additionally, samples were taken in which “equal effort” was substituted for equal SPL. The results of these investigations.