Miracle-dz/UniFAR-FTUs huggingface dataset:
To adopt or reject? Testing the robustness of a principle-based scenario for guiding effective student questioning

Guiding student questioning to become effective for attaining curriculum objectives is a challenge for many teachers. In two previous studies a principle-based scenario was developed in two primary schools to enhance teacher guidance of effective student questioning. This study aims to determine to what extent the scenario for teacher guidance is robust and transferrable to other teachers in different primary school settings. To test its robustness, 15 trainers introduced the scenario in 23 primary schools to 103 teachers. After teachers completed a six-week trial, they indicated in a questionnaire if they were inclined to adopt, adapt, or reject the scenario for future use. Results show that approximately 80% of all teachers would like to adopt the scenario. About 55% of the teachers see opportunities to adapt the scenario to their needs. However, about 20% of the teachers feel not yet able to judge if and how to adapt, having completed only one trial. The conclusion is that most teachers, despite differences in age, gender, grade, experience and school contexts, are willing and able to guide effective student questioning with the help of the scenario. From a theoretical point of view, this study provides further insight in how successful implementation can supported by a principle-based design.
Optical clocks based on molecular vibrations as probes of variation of the proton-to-electron mass ratio

Some new physics models of quantum gravity or dark matter predict drifts or oscillations of the fundamental constants. A relatively simple model relates molecular vibrations to the proton-to-electron mass ratio μ. Many vibrational transitions are at optical frequencies with prospects for use as highly accurate optical clocks. We give a brief summary of new physics models that lead to changes in μ and the current limits on drifts and oscillation amplitudes. After an overview of laboratory procedures, we give examples of molecules with experiments currently in development or underway. These experiments’ projected systematic and statistical uncertainties make them leading candidates in next-generation searches for time-variation of μ.
DynaMiTe: A Dynamic Local Motion Model with Temporal Constraints for Robust Real-Time Feature Matching

Feature based visual odometry and SLAM methods require accurate and fast correspondence matching between consecutive image frames for precise camera pose estimation in real-time. Current feature matching pipelines either rely solely on the descriptive capabilities of the feature extractor or need computationally complex optimization schemes. We present the lightweight pipeline DynaMiTe, which is agnostic to the descriptor input and leverages spatial-temporal cues with efficient statistical measures. The theoretical backbone of the method lies within a probabilistic formulation of feature matching and the respective study of physically motivated constraints. A dynamically adaptable local motion model encapsulates groups of features in an efficient data structure. Temporal constraints transfer information of the local motion model across time, thus additionally reducing the search space complexity for matching. DynaMiTe achieves superior results both in terms of matching accuracy and camera pose estimation with high frame rates, outperforming state-of-the-art matching methods while being computationally more efficient.
Materials Graph Transformer predicts the outcomes of inorganic reactions with reliable uncertainties

A common bottleneck for materials discovery is synthesis. While recent methodological advances have resulted in major improvements in the ability to predicatively design novel materials, researchers often still rely on trial-and-error approaches for determining synthesis procedures. In this work, we develop a model that predicts the major product of solid-state reactions. The cardinal feature of this approach is the construction of fixed-length, learned representations of reactions. Precursors are represented as nodes on a `reaction graph', and message-passing operations between nodes are used to embody the interactions between precursors in the reaction mixture. Through an ablation study, it is shown that this framework not only outperforms less physically-motivated baseline methods but also more reliably assesses the uncertainty in its predictions.
Alzheimer's Disease Diagnosis Based on Cortical and Subcortical Features

Alzheimer's disease (AD) is a common neurodegenerative disease with an often seen prodromal mild cognitive impairment (MCI) phase, where memory loss is the main complaint progressively worsening with behavior issues and poor self-care. However, not all patients clinically diagnosed with MCI progress to the AD. Currently, several high-dimensional classification techniques have been developed to automatically distinguish among AD, MCI, and healthy control (HC) patients based on T1-weighted MRI. However, these method features are based on wavelets, contourlets, gray-level co-occurrence matrix, etc., rather than using clinical features which helps doctors to understand the pathological mechanism of the AD. In this study, a new approach is proposed using cortical thickness and subcortical volume for distinguishing binary and tertiary classification of the National Research Center for Dementia dataset (NRCD), which consists of 326 subjects. Five classification experiments are performed: binary classification, i.e., AD vs HC, HC vs mAD (MCI due to the AD), and mAD vs aAD (asymptomatic AD), and tertiary classification, i.e., AD vs HC vs mAD and AD vs HC vs aAD using cortical and subcortical features. Datasets were divided in a 70/30 ratio, and later, 70% were used for training and the remaining 30% were used to get an unbiased estimation performance of the suggested methods. For dimensionality reduction purpose, principal component analysis (PCA) was used. After that, the output of PCA was passed to various types of classifiers, namely, softmax, support vector machine (SVM), k-nearest neighbors, and naïve Bayes (NB) to check the performance of the model. Experiments on the NRCD dataset demonstrated that the softmax classifier is best suited for the AD vs HC classification with an F1 score of 99.06, whereas for other groups, the SVM classifier is best suited for the HC vs mAD, mAD vs aAD, and AD vs HC vs mAD classifications with the F1 scores being 99.51, 97.5, and 99.99, respectively. In addition, for the AD vs HC vs aAD classification, NB performed well with an F1 score of 95.88. In addition, to check our proposed model efficiency, we have also used the OASIS dataset for comparing with 9 state-of-the-art methods.
Quantum monitoring the metabolism of individual yeast mutant strain cells when aged, stressed or treated with antioxidant

Free radicals play a key role in the ageing process. The strongly debated free radical theory of ageing even states that damage caused by free radicals is the main cause of aging on a cellular level. However, free radicals are small, reactive and short lived and thus challenging to measure. We utilize a new technique called diamond magnetometry for this purpose. We make use of nitrogen vacancy centers in nanodiamonds. Via a quantum effect these defects convert a magnetic resonance signal into an optical signal. While this method is increasingly popular for its unprecedented sensitivity in physics, we use this technique here for the first time to measure free radicals in living cells. Our signals are equivalent to T1 signals in conventional MRI but from nanoscale voxels from single cells with sub-cellular resolution. With this powerful tool we are able to follow free radical generation after chemically inducing stress. In addition, we can observe free radical reduction in presence of an antioxidant. We were able to clearly differentiate between mutant strains with altered metabolism. Finally, the excellent stability of our diamond particles allowed us to follow the ageing process and differentiate between young and old cells. We could confirm the expected increase of free radical load in old wild type and sod1{\Delta} mutants. We further applied this new technique to investigate tor1{\Delta} and pex19{\Delta} cells. For these mutants an increased lifespan has been reported but the exact mechanism is unclear. We find a decreased free radical load in, which might offer an explanation for the increased lifespan in these cells.
Algebras of quotients of Hom-Lie algebras

In this paper, we introduce the notion of algebras of quotients of Hom-Lie algebras and investigate some properties which can be lifted from a Hom-Lie algebra to its algebra of quotients. We also give some necessary and sufficient conditions for Hom-Lie algebras having algebras of quotients. We also examine the relationship between a Hom-Lie algebra and the associative algebra generated by inner derivations of the corresponding Hom-Lie algebra of quotients. Moreover, we introduce the notion of dense extensions and get a proposition about Hom-Lie algebras of quotients via dense extensions.
New Results in Sona Drawing: Hardness and TSP Separation

Given a set of point sites, a sona drawing is a single closed curve, disjoint from the sites and intersecting itself only in simple crossings, so that each bounded region of its complement contains exactly one of the sites. We prove that it is NP-hard to find a minimum-length sona drawing for $n$ given points, and that such a curve can be longer than the TSP tour of the same points by a factor $> 1.5487875$. When restricted to tours that lie on the edges of a square grid, with points in the grid cells, we prove that it is NP-hard even to decide whether such a tour exists. These results answer questions posed at CCCG 2006.
Origin of micrometer-scale propagation lengths of heat-carrying acoustic excitations in amorphous silicon

The heat-carrying acoustic excitations of amorphous silicon are of interest because their mean free paths may approach micron scales at room temperature. Despite extensive investigation, the origin of the weak acoustic damping in the heat-carrying frequencies remains a topic of debate. Here, we report measurements of the frequency-dependent mean free path in amorphous silicon thin films from ∼0.1−3 THz and over temperatures from 60 - 315 K using picosecond acoustics and transient grating spectroscopy. The mean free paths are independent of temperature and exhibit a Rayleigh scattering trend from ∼0.3−3 THz, below which the trend is characteristic of damping from density fluctuations or two-level systems. The observed trend is inconsistent with the predictions of numerical studies based on normal mode analysis but agrees with diverse measurements on other glasses. The micron-scale MFPs in amorphous Si arise from the absence of Akhiezer and two-level system damping in the sub-THz frequencies, leading to heat-carrying acoustic excitations with room-temperature damping comparable to that of other glasses at cryogenic temperatures.
Anomaly Payload Signature Generation System Based on Efficient Tokenization Methodology

Signature-based intrusion detection systems are widely used as an efficient network security control. Unfortunately, security experts manually craft attack signatures after capturing and analyzing the exploit code. Therefore, those systems are only able to detect known attacks. In this paper, we propose a new automated and content-based signature generation system that generates anomaly profiles to detect new and previously unknown attacks and worms. The proposed system, denoted SCANS, uses a natural tokenization method that speeds up the signature generation process by producing a fewer number of substrings. In this system, we propose a new stop character technique that will help to overcome signatures’ substrings granularity limitations of the old stop word techniques. In addition, SCANS introduces an improved normalized binary detection model specifically tailored for attacks detection. Experimental testing using DARPA IDS dataset shows a 95% malicious packets detection rate for port 23, with specificity of 88.4% and 94.6% for ports 21 and 25, respectively.
Genetic diversity assessment of wild and cultivated varieties of Jatropha curcas (L.) in India by RAPD analysis

The present study deals with evaluation of genetic diversity and pedigree analysis through RAPD analysis. A total number of 40 Jatropha curcas accessions collected from different geographical regions and 43 random decamer primers were screened to assess polymorphism. 10 primers were amplified and 94 polymorphic bands were found out of 125 scored. Accounting for 75.2 % polymorphism across the genotypes 12.5 bands per primer, out of 9.4 were polymorphic. Jaccard’s coefficient of similarity varied from 0.00 to 1.00 indicative of high levels of genetic variation among the genotypes studied. Cluster analysis of data using UPGMA algorithm placed the 40 accessions into 2 main clusters, with cluster II divided into six sub-clusters. The result provides valid guidelines for the collection, conservation and characterization of Jatropha curcas genetic resources.
Descriptive business process models at run-time

Today's competitive markets require organisations to react proactively to changes in their environment if financial and legal consequences are to be avoided. Since business processes are elementary parts of modern organisations they are also required to efficiently adapt to these changes in quick and flexible ways. This requirement demands a more dynamic handling of business processes, i.e. treating business processes as run-time artefacts rather than design-time artefacts. One general approach to address this problem is provided by the community of models@run.time, which promotes methodologies concerned with self-adaptive systems where models reflect the system's current state at any point in time and allow immediate reasoning and adaptation mechanisms. However, in contrast to common self-adaptive systems the domain of business processes features two additional challenges: (i) a bigger than usual abstraction gap between the business process models and the actual run-time information of the enterprise system and (ii) the possibility of run-time deviations from the planned models. Developing an understanding of such processes is a crucial necessity in order to optimise business processes and dynamically adapt to changing demands. This thesis explores the potential of adopting and enhancing principles and mechanisms from the models@run.time domain to the business process domain for the purpose of run-time reasoning, i.e. investigating the potential role of Descriptive Business Process Models at Run-time (DBPMRTs) in the business process management domain. The DBPMRT is a model describing the enterprise system at run-time and thus enabling higher-level reasoning on the as-is state. Along with the specification of the DBPMRT, algorithms and an overall framework are proposed to establish and maintain a causal link from the enterprise system to the DBPMRT at run-time. Furthermore, it is shown that proactive higher-level reasoning on a DBPMRT in the form of performance prediction allows for more accurate results. By taking these steps the thesis addresses general challenges of business process management, e.g. dealing with frequently changing processes and shortening the business process life cycle. At the same time this thesis contributes to research in models@run.time by providing a complex real-world use case as well as a reference approach for dealing with volatile models@run.time of a higher abstraction level.
Greedy Bandits with Sampled Context

Bayesian strategies for contextual bandits have proved promising in single-state reinforcement learning tasks by modeling uncertainty using context information from the environment. In this paper, we propose Greedy Bandits with Sampled Context (GB-SC), a method for contextual multi-armed bandits to develop the prior from the context information using Thompson Sampling, and arm selection using an epsilon-greedy policy. The framework GB-SC allows for evaluation of context-reward dependency, as well as providing robustness for partially observable context vectors by leveraging the prior developed. Our experimental results show competitive performance on the Mushroom environment in terms of expected regret and expected cumulative regret, as well as insights on how each context subset affects decision-making.
Main Manuscript for From parameter calibration to parameter learning: Revolutionizing large-scale geoscientific modeling with big data

The behaviors and skills of models in many geoscientific domains strongly depend on spatially varying parameters that lack direct observations and must be determined by calibration. Calibration, which solves inverse problems, is a classical but inefficient and stochasticity-ridden approach to reconcile models and observations. Using a widely applied hydrologic model and soil moisture observations as a case study, here we propose a novel, forward-mapping parameter learning ( fPL ) framework. Whereas evolutionary algorithm (EA)-based calibration solves inversion problems one by one, fPL solves a pattern recognition problem and learns a more robust, universal mapping. fPL can save orders-of-magnitude computational time compared to EA-based calibration, while, surprisingly, producing equivalent ending skill metrics. With more training data, fPL learned across sites and showed super-convergence, scaling much more favorably. Moreover, a more important benefit emerged: fPL produced spatially-coherent parameters in better agreement with physical processes. As a result, it demonstrated better results for out-of-training-set locations and uncalibrated variables. Compared to purely data-driven models, fPL can output unobserved variables, in this case simulated evapotranspiration, which agrees better with satellite-based estimates than the comparison EA. The deep-learning-powered fPL frameworks can be uniformly applied to myriad other geoscientific models. We contend that a paradigm shift from inverse parameter calibration to parameter learning will greatly propel various geoscientific domains.
Topologically nontrivial interband plasmons in type-II Weyl semimetal MoTe2

In many realistic topological materials, more than one kind of fermions contribute to the electronic bands crossing the Fermi level, leading to various novel phenomena. Here, using momentum-resolved inelastic electron scattering, we investigate the plasmons and their evolution across the phase transition in a type-II Weyl semimetal MoTe2, in which both Weyl fermions and trivial electrons contribute to the Fermi surface in the Td phase. One plasmon mode in the 1T′ phase at high temperature and two plasmon modes in the topological Td phase at low temperature are observed. Combining with first-principles calculations, we show that all the plasmon modes are dominated by the interband correlations between the inverted bands of MoTe2. Especially in the Td phase, since the electronic bands split due to inversion symmetry breaking and spin–orbit coupling, the plasmon modes manifest the interband correlation between the topological Weyl fermions and the trivial electrons. Our work emphasizes the significance of the interplay between different kinds of carriers in plasmons of topological materials.
Exploiting Global Camera Network Constraints for Unsupervised Video Person Re-Identification

Many unsupervised approaches have been proposed recently for the video-based re-identification problem since annotations of samples across cameras are time-consuming. However, higher-order relationships across the entire camera network are ignored by these methods, leading to contradictory outputs when matching results from different camera pairs are combined. In this paper, we address the problem of unsupervised video-based re-identification by proposing a consistent cross-view matching (CCM) framework, in which global camera network constraints are exploited to guarantee the matched pairs are with consistency. Specifically, we first propose to utilize the first neighbor of each sample to discover relations among samples and find the groups in each camera. Additionally, a cross-view matching strategy followed by global camera network constraints is proposed to explore the matching relationships across the entire camera network. Finally, we learn metric models for camera pairs progressively by alternatively mining consistent cross-view matching pairs and updating metric models using these obtained matches. Rigorous experiments on two widely-used benchmarks for video re-identification demonstrate the superiority of the proposed method over current state-of-the-art unsupervised methods; for example, on the MARS dataset, our method achieves an improvement of 4.2% over unsupervised methods, and even 2.5% over one-shot supervision-based methods for rank-1 accuracy.
Canopy density estimation in perennial horticulture crops using 3D spinning lidar SLAM

We propose a novel, canopy density estimation solution using a three‐dimensional (3D) ray cloud representation for perennial horticultural crops at the field scale. To attain high spatial and temporal fidelity in field conditions, we propose the application of continuous‐time 3D SLAM (simultaneous localization and mapping) to a spinning lidar payload (AgScan3D) mounted on a moving farm vehicle. The AgScan3D data are processed through a Continuous‐Time SLAM algorithm into a globally registered 3D ray cloud. The global ray cloud is a canonical data format (a digital twin) from which we can compare vineyard snapshots over multiple times within a season and across seasons. Then, the vineyard rows are automatically extracted from the ray cloud and a novel density calculation is performed to estimate the maximum likelihood canopy densities of the vineyard. This combination of digital twinning, together with the accurate extraction of canopy structure information, allows entire vineyards to be analyzed and compared, across the growing season and from year to year. The proposed method is evaluated both in simulation and field experiments. Field experiments were performed at four sites, which varied in vineyard structure and vine management, over two growing seasons and 64 data collection campaigns, resulting in a total traversal of 160 km, 42.4 scanned hectares of vines with a combined total of approximately 93,000 scanned vines. Our experiments show canopy density repeatability of 3.8% (relative root mean square error) per vineyard panel, for acquisition speeds of 5–6 km/h, and under half the standard deviation in estimated densities when compared with an industry standard gap‐fraction based solution. The code and field data sets are available at https://github.com/csiro-robotics/agscan3d.
SIMULEVAL: An Evaluation Toolkit for Simultaneous Translation

Simultaneous translation on both text and speech focuses on a real-time and low-latency scenario where the model starts translating before reading the complete source input. Evaluating simultaneous translation models is more complex than offline models because the latency is another factor to consider in addition to translation quality. The research community, despite its growing focus on novel modeling approaches to simultaneous translation, currently lacks a universal evaluation procedure. Therefore, we present SimulEval, an easy-to-use and general evaluation toolkit for both simultaneous text and speech translation. A server-client scheme is introduced to create a simultaneous translation scenario, where the server sends source input and receives predictions for evaluation and the client executes customized policies. Given a policy, it automatically performs simultaneous decoding and collectively reports several popular latency metrics. We also adapt latency metrics from text simultaneous translation to the speech task. Additionally, SimulEval is equipped with a visualization interface to provide better understanding of the simultaneous decoding process of a system. SimulEval has already been extensively used for the IWSLT 2020 shared task on simultaneous speech translation. Code will be released upon publication.