Miracle-dz/UniFAR-FTUs huggingface dataset:
Counting and comparing school performance: an analysis of media coverage of PISA in Australia, 2000–2014

Abstract This paper empirically documents media portrayals of Australia’s performance on the Program for the International Student Assessment (PISA), 2000–2014. We analyse newspaper articles from two national and eight metropolitan newspapers. This analysis demonstrates increased media coverage of PISA over the period in question. Our research data were analysed using ‘framing theory’, documenting how the media frames stories about Australia’s performance on PISA. Three frames were identified: counts and comparisons; criticisms; and contexts. Most of the media coverage (41%) was concerned with the first frame, counts and comparisons, which analysed PISA data to provide ‘evidence’ that was then used to comparatively position Australia against other countries, reference societies, which do better, with particular emphasis on Finland and also Shanghai after the 2009 PISA. The other two frames dealt with criticisms and contextual issues. This paper only focuses on the first frame. The analysis demonstrates the ways in which media coverage of Australia’s PISA performance has had policy impact.
V-STRONG: Visual Self-Supervised Traversability Learning for Off-road Navigation

Reliable estimation of terrain traversability is critical for the successful deployment of autonomous systems in wild, outdoor environments. Given the lack of large-scale annotated datasets for off-road navigation, strictly-supervised learning approaches remain limited in their generalization ability. To this end, we introduce a novel, image-based self-supervised learning method for traversability prediction, leveraging a state-of-the-art vision foundation model for improved out-of-distribution performance. Our method employs contrastive representation learning using both human driving data and instance-based segmentation masks during training. We show that this simple, yet effective, technique drastically outperforms recent methods in predicting traversability for both on- and off-trail driving scenarios. We compare our method with recent baselines on both a common benchmark as well as our own datasets, covering a diverse range of outdoor environments and varied terrain types. We also demonstrate the compatibility of resulting costmap predictions with a model-predictive controller. Finally, we evaluate our approach on zero- and few-shot tasks, demonstrating unprecedented performance for generalization to new environments. Videos and additional material can be found here: https://sites.google. com/view/visual-traversability-learning.
Error-free Training for Artificial Neural Network

Conventional training methods for artificial neural network (ANN) models never achieve zero error rate systematically for large data. A new training method consists of three steps: first create an auxiliary data from conventionally trained parameters which correspond exactly to a global minimum for the loss function of the cloned data; second create a one-parameter homotopy (hybrid) of the auxiliary data and the original data; and third train the model for the hybrid data iteratively from the auxiliary data end of the homotopy parameter to the original data end while maintaining the zero-error training rate at every iteration. This continuationmethod is guaranteed to converge numerically by a theorem which converts the ANN training problem into a continuation problem for fixed points of a parameterized transformation in the training parameter space to which the Uniform Contraction Mapping Theorem from dynamical systems applies.
Stable higher-order topological Dirac semimetals with 
Z2
 monopole charge in alternating-twist multilayer graphene and beyond

We demonstrate that a class of stable $\mathbb{Z}_2$ monopole charge Dirac point ($\mathbb{Z}_2$DP) phases can robustly exist in real materials, which surmounts the understanding: that is, a $\mathbb{Z}_2$DP is unstable and generally considered to be only the critical point of a $\mathbb{Z}_2$ nodal line ($\mathbb{Z}_2$NL) characterized by a $\mathbb{Z}_2$ monopole charge (the second Stiefel-Whitney number $w_2$) with space-time inversion symmetry but no spin-orbital coupling. For the first time, we explicitly reveal the higher-order bulk-boundary correspondence in the stable $\mathbb{Z}_2$DP phase. We propose the alternating-twisted multilayer graphene, which can be regarded as 3D twisted bilayer graphene (TBG), as the first example to realize such stable $\mathbb{Z}_2$DP phase and show that the Dirac points in the 3D TBG are essential degenerate at high symmetric points protected by crystal symmetries and carry a nontrivial $\mathbb{Z}_2$ monopole charge ($w_2=1$), which results in higher-order hinge states along the entire Brillouin zone of the $k_z$ direction. By breaking some crystal symmetries or tailoring interlayer coupling we are able to access $\mathbb{Z}_2$NL phases or other $\mathbb{Z}_2$DP phases with hinge states of adjustable length. In addition, we present other 3D materials which host $\mathbb{Z}_2$DPs in the electronic band structures and phonon spectra. We construct a minimal eight-band tight-binding lattice model that captures these nontrivial topological characters and furthermore tabulate all possible space groups to allow the existence of the stable $\mathbb{Z}_2$DP phases, which will provide direct and strong guidance for the realization of the $\mathbb{Z}_2$ monopole semimetal phases in electronic materials, metamaterials and electrical circuits, etc.
BalMCTS: Balancing Objective Function and Search Nodes in MCTS for Constraint Optimization Problems

Constraint Optimization Problems (COP) pose intricate challenges in combinatorial problems usually addressed through Branch and Bound (B\&B) methods, which involve maintaining priority queues and iteratively selecting branches to search for solutions. However, conventional approaches take a considerable amount of time to find optimal solutions, and it is also crucial to quickly identify a near-optimal feasible solution in a shorter time. In this paper, we aim to investigate the effectiveness of employing a depth-first search algorithm for solving COP, specifically focusing on identifying optimal or near-optimal solutions within top $n$ solutions. Hence, we propose a novel heuristic neural network algorithm based on MCTS, which, by simultaneously conducting search and training, enables the neural network to effectively serve as a heuristic during Backtracking. Furthermore, our approach incorporates encoding COP problems and utilizing graph neural networks to aggregate information about variables and constraints, offering more appropriate variables for assignments. Experimental results on stochastic COP instances demonstrate that our method identifies feasible solutions with a gap of less than 17.63% within the initial 5 feasible solutions. Moreover, when applied to attendant Constraint Satisfaction Problem (CSP) instances, our method exhibits a remarkable reduction of less than 5% in searching nodes compared to state-of-the-art approaches.
Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training

Graph summarization as a preprocessing step is an effective and complementary technique for scalable graph neural network (GNN) training. In this work, we propose the Coarsening Via Convolution Matching (CONVMATCH) algorithm and a highly scalable variant, A-CONVMATCH, for creating summarized graphs that preserve the output of graph convolution. We evaluate CONVMATCH on six real-world link prediction and node classification graph datasets, and show it is efficient and preserves prediction performance while significantly reducing the graph size. Notably, CONVMATCH achieves up to 95% of the prediction performance of GNNs on node classification while trained on graphs summarized down to 1% the size of the original graph. Furthermore, on link prediction tasks, CONVMATCH consistently outperforms all baselines, achieving up to a 2x improvement.
APLIKASI PUPUK HAYATI (PLANT GROWTH PROMOTING RHIZOBACTERIA) YANG TELAH DISIMPAN TERHADAP PERTUMBUHAN TANAMAN JAGUNG Var. BISMA

Aplikasi pupuk hayati yang mengandung mikrob hidup dapat memacu pertumbuhan tanaman. Percobaan dimaksudkan untuk menguji viabilitas Bacillus sp. , Pseudomonas sp. , Azospirillum sp. , dan Azotobacter sp. yang terdapat pada pupuk hayati dikeringkan dengan metode kering beku pada beberapa bulan penyimpanan. Viabilitas sel bakteri diuji setelah disimpan 0, 1, 2, 4, dan 6 bulan. Pupuk hayati tersebut diaplikasi pada tanaman jagung var. Bisma untuk diamati efektivitasnya. Penelitian ini dilakukan di rumah kaca dengan menggunakan metode Rancangan Acak Lengkap (RAL) yang terdiri atas 8 perlakuan dengan 5 ulangan. Perlakuan tanah tanpa pemupukan (P1); tanah dengan pupuk NPK (P2); tanah dengan kompos (P3); tanah dengan kompos dan pupuk hayati penyimpanan 0, 1, 2, 4, dan 6 bulan (P4-P8). Hasil uji menunjukkan viabilitas bakteri sedikit menurun setelah proses kering beku (freeze drying) kemudian stabil sampai penyimpanan 2 bulan. Setelah 2 bulan, viabilitas masing-masing bakteri yang terkandung dalam pupuk hayati menurun. Aplikasi pupuk hayati penyimpanan 0, 1, 2, 4, dan 6 bulan meningkatkan pertumbuhan dan produksi tanaman jagung. Namun semakin lama penyimpanan menyebabkan efektivitas pupuk hayati untuk mendukung pertumbuhan dan produksi jagung menurun. Produksi jagung menurun dari 109.36 gram pada P5 menjadi 70.46 gram pada P8.
Fidelity and Consistency in Iterative Probabilistic Set Replication: Comparing Bi-Parental and Mono-Parental Methods

We report results of a restricted mathematical computation that may be relevant in various contexts, including the evolution of sexual reproduction. The computation is an iterative probabilistic replication of sets of real numbers. Given a set of random values sampled from a certain interval, we replicate it probabilistically by encoding it as its arithmetic mean and then sampling a random same-size set of real numbers using uniform distribution over the same interval length around that mean. The encoded set and its code are considered the parent of the sampled one. We first sample a cohort of several such sets around an initial code, and probabilistically replicate each set once, creating a new same-size cohort, which we then subject to the same process. In a bi-parental variant of the above mono-parental process, we use a pair of codes rather than a single one for sampling a new set: each parent contributes half of the elements of the child set. Neither variant depends on judging properties as harmful, or on selection: all individuals are paired and reproduced. Comparing the two variants over many iterations, bi-parental processing shows enhanced fidelity and consistency, where fidelity is defined as the mean of the distances between a cohort's codes and the initial code, and consistency is the standard deviation of code values within a cohort. We do not attempt here to directly mimic natural reproduction, which includes copying and recombination of DNA codes subject to mutations. Still, the result intuitively aligns with the idea that sexual reproduction in nature helps preserve, and perhaps even define, core traits of each species. The cause for the prevalence, in eukaryotic species, of sexual reproduction over asexual, despite increased complexity and unpredictability, is subject to debate and diverse theories. The mathematical phenomenon reported here might be part of the answer.
Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloading

While network coverage maps continue to expand, many devices located in remote areas remain unconnected to terrestrial communication infrastructures, preventing them from getting access to the associated data-driven services. In this paper, we propose a ground-to-satellite cooperative federated learning (FL) methodology to facilitate machine learning service management over remote regions. Our methodology orchestrates satellite constellations to provide the following key functions during FL: (i) processing data offloaded from ground devices, (ii) aggregating models within device clusters, and (iii) relaying models/data to other satellites via inter-satellite links (ISLs). Due to the limited coverage time of each satellite over a particular remote area, we facilitate satellite transmission of trained models and acquired data to neighboring satellites via ISL, so that the incoming satellite can continue conducting FL for the region. We theoretically analyze the convergence behavior of our algorithm, and develop a training latency minimizer which optimizes over satellite-specific network resources, including the amount of data to be offloaded from ground devices to satellites and satellites' computation speeds. Through experiments on three datasets, we show that our methodology can significantly speed up the convergence of FL compared with terrestrial-only and other satellite baseline approaches.
Runge-Kutta methods are stable

We prove that Runge-Kutta (RK) methods for numerical integration of arbitrarily large systems of Ordinary Differential Equations are linearly stable. Standard stability arguments -- based on spectral analysis, resolvent condition or strong stability, fail to secure the stability of arbitrarily large RK systems. We explain the failure of different approaches, offer a new stability theory and demonstrate a few examples.
BDIS-SLAM: A lightweight CPU-based dense stereo SLAM for surgery

PURPOSE Common dense stereo simultaneous localization and mapping (SLAM) approaches in minimally invasive surgery (MIS) require high-end parallel computational resources for real-time implementation. Yet, it is not always feasible since the computational resources should be allocated to other tasks like segmentation, detection, and tracking. To solve the problem of limited parallel computational power, this research aims at a lightweight dense stereo SLAM system that works on a single-core CPU and achieves real-time performance (more than 30 Hz in typical scenarios). METHODS A new dense stereo mapping module is integrated with the ORB-SLAM2 system and named BDIS-SLAM. Our new dense stereo mapping module includes stereo matching and 3D dense depth mosaic methods. Stereo matching is achieved with the recently proposed CPU-level real-time matching algorithm Bayesian Dense Inverse Searching (BDIS). A BDIS-based shape recovery and a depth mosaic strategy are integrated as a new thread and coupled with the backbone ORB-SLAM2 system for real-time stereo shape recovery. RESULTS Experiments on in vivo data sets show that BDIS-SLAM runs at over 30 Hz speed on modern single-core CPU in typical endoscopy/colonoscopy scenarios. BDIS-SLAM only consumes around an additional [Formula: see text] time compared with the backbone ORB-SLAM2. Although our lightweight BDIS-SLAM simplifies the process by ignoring deformation and fusion procedures, it can provide a usable dense mapping for modern MIS on computationally constrained devices. CONCLUSION The proposed BDIS-SLAM is a lightweight stereo dense SLAM system for MIS. It achieves 30 Hz on a modern single-core CPU in typical endoscopy/colonoscopy scenarios (image size around [Formula: see text]). BDIS-SLAM provides a low-cost solution for dense mapping in MIS and has the potential to be applied in surgical robots and AR systems. Code is available at https://github.com/JingweiSong/BDIS-SLAM .
SoK: Technical Implementation and Human Impact of Internet Privacy Regulations

Growing recognition of the potential for exploitation of personal data and of the shortcomings of prior privacy regimes has led to the passage of a multitude of new online privacy regulations. Some of these laws -- notably the European Union's General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) -- have been the focus of large bodies of research by the computer science community, while others have received less attention. In this work, we analyze a set of Internet privacy and data protection regulations drawn from around the world -- both those that have frequently been studied by computer scientists and those that have not -- and develop a taxonomy of rights granted and obligations imposed by these laws. We then leverage this taxonomy to systematize 270 technical research papers published in computer science venues that investigate the impact of these laws and explore how technical solutions can complement legal protections. Finally, we analyze the results in this space through an interdisciplinary lens and make recommendations for future work at the intersection of computer science and legal privacy.
TMAP: A Threat Modeling and Attack Path Analysis Framework for Industrial IoT Systems (A Case Study of IoM and IoP)

Industrial cyber-physical systems (ICPS) are gradually integrating information technology and automating industrial processes, leading systems to become more vulnerable to malicious actors. Thus, to deploy secure Industrial Control and Production Systems (ICPS) in smart factories, cyber threats and risks must be addressed. To identify all possible threats, Threat Modeling is a promising solution. Despite the existence of numerous methodological solutions for threat modeling in cyber-physical systems (CPS), current approaches are ad hoc and inefficient in providing clear insights to researchers and organizations involved in IIoT technologies. These approaches lack a comprehensive analysis of cyber threats and fail to facilitate effective path analysis across the ICPS lifecycle, incorporating smart manufacturing technologies and tools. To address these gaps, a novel quantitative threat modeling approach is proposed, aiming to identify probable attack vectors, assess the path of attacks, and evaluate the magnitude of each vector. This paper also explains the execution of the proposed approach with two case studies, namely the industrial manufacturing line, i.e., the Internet of Manufacturing (IoM), and the power and industry, i.e., the Internet of Production (IoP).
SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes

Novel view synthesis for dynamic scenes is still a challenging problem in computer vision and graphics. Recently, Gaussian splatting has emerged as a robust technique to represent static scenes and enable high-quality and real-time novel view synthesis. Building upon this technique, we propose a new representation that explicitly decomposes the motion and appearance of dynamic scenes into sparse control points and dense Gaussians, respectively. Our key idea is to use sparse control points, significantly fewer in number than the Gaussians, to learn compact 6 DoF transformation bases, which can be locally interpolated through learned interpolation weights to yield the motion field of 3D Gaussians. We employ a deformation MLP to predict time-varying 6 DoF transformations for each control point, which reduces learning complexities, enhances learning abilities, and facilitates obtaining temporal and spatial coherent motion patterns. Then, we jointly learn the 3D Gaussians, the canonical space locations of control points, and the deformation MLP to reconstruct the appearance, geometry, and dynamics of 3D scenes. During learning, the location and number of control points are adaptively adjusted to accommodate varying motion complexities in different regions, and an ARAP loss following the principle of as rigid as possible is developed to enforce spatial continuity and local rigidity of learned motions. Finally, thanks to the explicit sparse motion representation and its decomposition from appearance, our method can enable user-controlled motion editing while retaining high-fidelity appearances. Extensive experiments demonstrate that our approach outperforms existing approaches on novel view synthesis with a high rendering speed and enables novel appearance-preserved motion editing applications. Project page: https://yihua7.github. io/SC-GS-web/
Line-Plane Incidence Bound in $\mathbb{R}^4$

We consider an incidence problem in $\mathbb{R}^4$ which asks, for a set of $L$ lines and a set of $S$ planes in general position, what the maximum number of line-plane incidences is. A line-plane incidence is defined as a point where a line and a plane intersect. We prove that, when the lines and planes are in a truly 4-dimensional configuration such that no more than $L^{\frac{1}{2}+\epsilon}$ lines are contained in any 2-dimensional surface of degree at most $D$ and no more than $S^{\frac{1}{2}+\epsilon}$ 2-planes are contained in any 3-dimensional hypersurface of degree at most $D$, and if $L^{1/2} \ll S \ll L$, then for a constant $D>1$ and an $\epsilon>0$ there exists a non-trivial upper bound for incidences between lines and planes: $L^{\frac{3}{4}+\frac{1}{2}\epsilon}S + LS^{\frac{1}{2}+\epsilon}$. We also prove several supporting lemmas.
Supersymmetric domain walls in maximal 6D gauged supergravity. II.

We continue the study of supersymmetric domain wall solutions in six-dimensional maximal gauged supergravity. We first give a classification of viable gauge groups with the embedding tensor in $\mathbf{5}^{+7}$, $\bar{\mathbf{5}}^{+3}$, $\mathbf{10}^{-1}$, $\mathbf{24}^{-5}$, and $\overline{\mathbf{45}}^{+3}$ representations of the off-shell symmetry $GL(5)\subset SO(5,5)$. We determine an explicit form of the embedding tensor for gauge groups arising from each representation together with some examples of possible combinations among them. All of the resulting gauge groups are of a non-semisimple type with abelian factors and translational groups of different dimensions. We find $\frac{1}{2}$- and $\frac{1}{4}$-supersymmetric domain walls with $SO(2)$ symmetry in $SO(2)\ltimes \mathbb{R}^8$ and $SO(2)\ltimes \mathbb{R}^6$ gauge groups from the embedding tensor in $\mathbf{24}^{-5}$ representation and in $CSO(2,0,2)\ltimes \mathbb{R}^4$, $CSO(2,0,2)\ltimes \mathbb{R}^2$, and $CSO(2,0,1)\ltimes \mathbb{R}^4$ gauge groups with the embedding tensor in $\overline{\mathbf{45}}^{+3}$ representations. These gauge groups are parametrized by a traceless matrix and electrically and magnetically embedded in $SO(5,5)$ global symmetry, respectively.
SantaQlaus: A resource-efficient method to leverage quantum shot-noise for optimization of variational quantum algorithms

We introduce SantaQlaus, a resource-efficient optimization algorithm tailored for variational quantum algorithms (VQAs), including applications in the variational quantum eigensolver (VQE) and quantum machine learning (QML). Classical optimization strategies for VQAs are often hindered by the complex landscapes of local minima and saddle points. Although some existing quantum-aware optimizers adaptively adjust the number of measurement shots, their primary focus is on maximizing gain per iteration rather than strategically utilizing quantum shot-noise (QSN) to address these challenges. Inspired by the classical Stochastic AnNealing Thermostats with Adaptive momentum (Santa) algorithm, SantaQlaus explicitly leverages inherent QSN for optimization. The algorithm dynamically adjusts the number of quantum measurement shots in an annealing framework: fewer shots are allocated during the early, high-temperature stages for efficient resource utilization and landscape exploration, while more shots are employed later for enhanced precision. Numerical simulations on VQE and QML demonstrate that SantaQlaus outperforms existing optimizers, particularly in mitigating the risks of converging to poor local optima, all while maintaining shot efficiency. This paves the way for efficient and robust training of quantum variational models.