Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Water Use of the Maize Soil–Root–Shoot System under the Integrated Effects of Organic Manure and Plant Density

On the Loess Plateau of China, water shortage and serious soil erosion are the key factors that restrict local agricultural development, especially in terms of crop yield. In order to expound the effect of treatment with organic manure in root growth, water transpiration and evaporation, biomass allocation and grain yield and WUE (water use efficiency), we took maize (Zheng Dan 958) sown for four years with three replicates at three densities. The results show that the highest rate of maize grain yield increase with organic manure is about 9.99% for a density of 90,000 plants/ha; at the same time, ET (evapotranspiration) and WUE also achieved marked increments, which the highest values of 415.47 mm with a density of 75,000 plants/ha and 7.92% with a density of 90,000 plants/ha, respectively. The results also demonstrate the obvious effect of organic manure in enhancing root growth and in the maximization of water transpiration and evaporation, and water use plays a vital and valuable role in biomass allocation. The results also serve as orientation for methods to increase maize yield and a reference for other crops in the relation of water and manure to their growth.
LOKALIZACIJA MERSKIH INSTRUMENTOV: ZGLED VPRAŠALNIKA AMS LOCALIZATION OF MEASUREMENT INSTRUMENTS: EXAMPLE OF THE AMS QUESTIONNAIRE

Izvlecek Izhodisca Uporaba vprasalnikov o kakovosti življenja in drugih zdravstvenih merskih instrumentov v medicini narasca in to dogajanje spremlja potreba po dostopnosti ustreznih instrumentov v razlicnih jezikih in kulturnih okoljih. Toda potrebna priredba ni zgolj prevod, temvec mora upostevati dolocena postopkovna pravila. Zakljucki Na podlagi splosnih mednarodnih smernic predlagamo minimalni 6-stopenjski postopek za lokalizacijo teh vprasalnikov (prevod, sinteza in prilagoditev v ciljnem jeziku, vzvratni prevod, primerjava razlicic v izvirnem jeziku in prilagoditev v ciljnem jeziku, preverjanje razumljivosti, preverjanje zanesljivosti). Predlagani postopek smo uporabili za lokalizacijo validiranega vprasalnika AMS iz anglescine v slovenscino. Opisni rezultati ter preverjanje stabilnosti in notranje konsistentnosti (ρ = 0,89; Cronbach α za celotni vprasalnik = 0,875) potrjujejo, da je slovenski AMS metodolosko zanesljiva lokalizirana razlicica, in tako nakazujejo ustreznost predlaganega postopka lokalizacije.
Memory recollection and oral history: a study of vernacular architecture transformation of the past

ABSTRACT In many rural areas, historical records are limited to analyzing the transformation of vernacular architecture. An alternate resource that this paper will use is memory recollection. This paper will examine one house owned by a Bajo informant and one owned by a person identified as Manggarai in and around Labuan Bajo, Indonesia. The desk study, observation, and interviews collected elements of their built environment classified in transformation theory. Then the findings and interactions with the informants are analyzed to see the ideal memory system best applied to these methodologies. Through this research, we were fortunate to collaborate with two memory systems of two different ethnic groups, with which the method resulted in detail. Furthermore, with the findings from this research, it is hoped that more studies of rural vernacular architecture in places with limited historical records can be conducted through memory recollection before the decrease of communities owning these memories.
In-orbit Performance Evaluation of Temperature Controlled Small Fiber Optical Gyro on Microsattelite ”REIMEI”

Scientific microsatellite “REIMEI” was launched on August 2005. This satellite achieved attitude determination and control with accuracy of about 0.05 [deg], although it weights only 70 kg in total. Small Fiber optic gyro sensors were applied and contributed this weight efficiency. This paper describes the effect of temperature control of these FOGs, and the results of in-orbit gyro performance evaluation.
Speeding up Multi-objective Non-hierarchical Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-structured Parzen Estimator

Hyperparameter optimization (HPO) is a vital step in improving performance in deep learning (DL). Practitioners are often faced with the trade-off be-tween multiple criteria, such as accuracy and latency. Given the high computational needs of DL and the growing demand for efﬁcient HPO, the acceleration of multi-objective (MO) optimization becomes ever more important. Despite the signiﬁ-cant body of work on meta-learning for HPO, existing methods are inapplicable to MO tree-structured Parzen estimator (MO-TPE), a simple yet powerful MO-HPO algorithm. In this paper, we extend TPE’s acquisition function to the meta-learning setting using a task similarity deﬁned by the overlap of top domains between tasks. We also theoretically analyze and address the limitations of our task similarity. In the experiments, we demonstrate that our method speeds up MO-TPE on tabular HPO benchmarks and attains state-of-the-art performance. Our method was also validated externally by winning the AutoML 2022 competition on “Multiobjective Hyperparameter Optimization for Transformers” .
1001 Ways of Scenario Generation for Testing of Self-driving Cars: A Survey

Scenario generation is one of the essential steps in scenario-based testing and, therefore, a significant part of the verification and validation of driver assistance functions and autonomous driving systems. However, the term scenario generation is used for many different methods, e.g., extraction of scenarios from naturalistic driving data or variation of scenario parameters. This survey aims to give a systematic overview of different approaches, establish different categories of scenario acquisition and generation, and show that each group of methods has typical input and output types. It shows that although the term is often used throughout literature, the evaluated methods use different inputs and the resulting scenarios differ in abstraction level and from a systematical point of view. Additionally, recent research and literature examples are given to underline this categorization.
Automated Static Camera Calibration with Intelligent Vehicles

Connected and cooperative driving requires precise calibration of the roadside infrastructure for having a reliable perception system. To solve this requirement in an automated manner, we present a robust extrinsic calibration method for automated geo-referenced camera calibration. Our method requires a calibration vehicle equipped with a combined GNSS/RTK receiver and an inertial measurement unit (IMU) for self-localization. In order to remove any requirements for the target’s appearance and the local traffic conditions, we propose a novel approach using hypothesis filtering. Our method does not require any human interaction with the information recorded by both the infrastructure and the vehicle. Furthermore, we do not limit road access for other road users during calibration. We demonstrate the feasibility and accuracy of our approach by evaluating our approach on synthetic datasets as well as a real-world connected intersection, and deploying the calibration on real infrastructure. Our source code is publicly available 1.
Learn to Cluster Faces with Better Subgraphs

Face clustering can provide pseudo-labels to the massive unlabeled face data and improve the performance of different face recognition models. The existing clustering methods generally aggregate the features within subgraphs that are often implemented based on a uniform threshold or a learned cutoff position. This may reduce the recall of subgraphs and hence degrade the clustering performance. This work proposed an efficient neighborhood-aware subgraph adjustment method that can significantly reduce the noise and improve the recall of the subgraphs, and hence can drive the distant nodes to converge towards the same centers. More specifically, the proposed method consists of two components, i.e. face embeddings enhancement using the embeddings from neighbors, and enclosed subgraph construction of node pairs for structural information extraction. The embeddings are combined to predict the linkage probabilities for all node pairs to replace the cosine similarities to produce new subgraphs that can be further used for aggregation of GCNs or other clustering methods. The proposed method is validated through extensive experiments against a range of clustering solutions using three benchmark datasets and numerical results confirm that it outperforms the SOTA solutions in terms of generalization capability.
Vital Sign Monitoring in Dynamic Environment via mmWave Radar and Camera Fusion

Contact-free vital sign monitoring, which uses wireless signals for recognizing human vital signs (i.e, breath and heartbeat), is an attractive solution to health and security. However, the subject's body movement and the change in actual environments can result in inaccurate frequency estimation of heartbeat and respiratory. In this paper, we propose a robust mmWave radar and camera fusion system for monitoring vital signs, which can perform consistently well in dynamic scenarios, e.g., when some people move around the subject to be tracked, or a subject waves his/her arms and marches on the spot. Three major processing modules are developed in the system, to enable robust sensing. Firstly, we utilize a camera to assist a mmWave radar to accurately localize the subjects of interest. Secondly, we exploit the calculated subject position to form transmitting and receiving beamformers, which can improve the reflected power from the targets and weaken the impact of dynamic interference. Thirdly, we propose a weighted multi-channel Variational Mode Decomposition (WMC-VMD) algorithm to separate the weak vital sign signals from the dynamic ones due to subject's body movement. Experimental results show that, the 90${^{th}}$ percentile errors in respiration rate (RR) and heartbeat rate (HR) are less than 0.5 RPM (respirations per minute) and 6 BPM (beats per minute), respectively.
Individual Fairness in Bayesian Neural Networks

We study Individual Fairness (IF) for Bayesian neural networks (BNNs). Specifically, we consider the $\epsilon$-$\delta$-individual fairness notion, which requires that, for any pair of input points that are $\epsilon$-similar according to a given similarity metrics, the output of the BNN is within a given tolerance $\delta>0. $ We leverage bounds on statistical sampling over the input space and the relationship between adversarial robustness and individual fairness to derive a framework for the systematic estimation of $\epsilon$-$\delta$-IF, designing Fair-FGSM and Fair-PGD as global,fairness-aware extensions to gradient-based attacks for BNNs. We empirically study IF of a variety of approximately inferred BNNs with different architectures on fairness benchmarks, and compare against deterministic models learnt using frequentist techniques. Interestingly, we find that BNNs trained by means of approximate Bayesian inference consistently tend to be markedly more individually fair than their deterministic counterparts.
Koios: Top-k Semantic Overlap Set Search

We study the top-k set similarity search problem using semantic overlap. While vanilla overlap requires exact matches between set elements, semantic overlap allows elements that are syntactically different but semantically related to increase the overlap. The semantic overlap is the maximum matching score of a bipartite graph, where an edge weight between two set elements is defined by a user-defined similarity function, e.g., cosine similarity between embeddings. Common techniques like token indexes fail for semantic search since similar elements may be unrelated at the character level. Further, verifying candidates is expensive (cubic versus linear for syntactic overlap), calling for highly selective filters. We propose Koios, the first exact and efficient algorithm for semantic overlap search. Koios leverages sophisticated filters to minimize the number of required graph-matching calculations. Our experiments show that for medium to large sets less than 5% of the candidate sets need verification, and more than half of those sets are further pruned without requiring the expensive graph matching. We show the efficiency of our algorithm on four real datasets and demonstrate the improved result quality of semantic over vanilla set similarity search.
Hear Me Out: A Study on the Use of the Voice Modality for Crowdsourced Relevance Assessments

The creation of relevance assessments by human assessors (often nowadays crowdworkers) is a vital step when building IR test collections. Prior works have investigated assessor quality & behaviour, and tooling to support assessors in their task. We have few insights though into the impact of a document's presentation modality on assessor efficiency and effectiveness. Given the rise of voice-based interfaces, we investigate whether it is feasible for assessors to judge the relevance of text documents via a voice-based interface. We ran a user study (n = 49) on a crowdsourcing platform where participants judged the relevance of short and long documents- sampled from the TREC Deep Learning corpus-presented to them either in the text or voice modality. We found that: (i) participants are equally accurate in their judgements across both the text and voice modality; (ii) with increased document length it takes partic- ipants significantly longer (for documents of length > 120 words it takes almost twice as much time) to make relevance judgements in the voice condition; and (iii) the ability of assessors to ignore stimuli that are not relevant (i.e., inhibition) impacts the assessment quality in the voice modality-assessors with higher inhibition are significantly more accurate than those with lower inhibition. Our results indicate that we can reliably leverage the voice modality as a means to effectively collect relevance labels from crowdworkers.
A Multi-Fidelity Bayesian Approach to Safe Controller Design

Safely controlling unknown dynamical systems is one of the biggest challenges in the field of control systems. Oftentimes, an approximate model of a system’s dynamics exists which provides beneficial information for control design. However, differences between the approximate and true systems present challenges as well as safety concerns. We propose an algorithm called SafeSlope to safely evaluate points from a Gaussian process model of a function when its Lipschitz constant is unknown. We establish theoretical guarantees for the performance of SafeSlope and quantify how multi-fidelity modeling improves the algorithm’s performance. Finally, we present a case where SafeSlope achieves lower cumulative regret than a naive sampling method by applying it to find the control gains of a linear time-invariant system.
Enhancing Wind Power Forecast Precision via Multi-head Attention Transformer: An Investigation on Single-step and Multi-step Forecasting

The main objective of this study is to propose an enhanced wind power forecasting (EWPF) transformer model for handling power grid operations and boosting power market competition. It helps reliable large-scale integration of wind power relies in large part on accurate wind power forecasting (WPF). The proposed model is evaluated for single-step and multi-step WPF, and compared with gated recurrent unit (GRU) and long short-term memory (LSTM) models on a wind power dataset. The results of the study indicate that the proposed EWPF transformer model outperforms conventional recurrent neural network (RNN) models in terms of time-series forecasting accuracy. In particular, the results reveal a minimum performance improvement of 5% and a maximum of 20% compared to LSTM and GRU. These results indicate that the EWPF transformer model provides a promising alternative for wind power forecasting and has the potential to significantly improve the precision of WPF. The findings of this study have implications for energy producers and researchers in the field of WPF.
Approximation- and Chattering-free Quasi Sliding Mode Control for Unknown Systems

Motivated by the concept of Quasi-Sliding Mode (QSM) in discrete-time systems, this paper presents a novel approach that relaxes the requirement of ubiquitous exact sliding motion in continuous-time systems, aiming to achieve an approximation- and chattering-free quasi-sliding mode controller (QSMC). The proposed QSMC provides robust and chattering-free control for an unknown nonlinear system without the need for intricate control methods, learning agents, or system identification tools. Furthermore, comprehensive simulation studies have been conducted to validate the effectiveness of the proposed strategy.
Deep-Learning-based Fast and Accurate 3D CT Deformable Image Registration in Lung Cancer

Background: Deformable Image Registration (DIR) is an essential technique required in many applications of radiation oncology. However, conventional DIR approaches typically take several minutes to register one pair of 3D CT images and the resulting deformable vector fields (DVFs) are only specific to the pair of images used, making it less appealing for clinical application. Purpose: In some proton therapy facilities, patient alignment relies on two 2D orthogonal kV images, taken at fixed, oblique angles, as no 3D on-the-bed imaging is available. The visibility of the tumor in kV images is limited since the patient’s 3D anatomy is projected onto a 2D plane, especially when the tumor is behind high-density structures such as bones. This can lead to large patient setup errors. A solution is to reconstruct the 3D CT image from the kV images obtained at the treatment isocenter in the treatment position. Methods: An asymmetric autoencoder-like network built with vision-transformer blocks was developed. The data was collected from 1 head and neck patient: 2 orthogonal kV images (1024×1024 voxels), 1 3D CT with padding (512×512×512) acquired from the in-room CT-on-rails before kVs were taken and 2 digitally-reconstructed-radiograph (DRR) images (512×512) based on the CT. We resampled kV images every 8 voxels and DRR and CT every 4 voxels, thus formed a dataset consisting of 262,144 samples, in which the images have a dimension of 128 for each direction. In training, both kV and DRR images were utilized, and the encoder was encouraged to learn the jointed feature map from both kV and DRR images. In testing, only independent kV images were used. The full-size synthetic CT (sCT) was achieved by concatenating the sCTs generated by the model according to their spatial information. The image quality of the synthetic CT (sCT) was evaluated using mean absolute error (MAE) and per-voxel-absolute-CT-number-difference volume histogram (CDVH). Results: The model achieved a speed of 2.1s and a MAE of <40HU. The CDVH showed that <5% of the voxels had a per-voxel-absolute-CT-number-difference larger than 185 HU. Conclusion: A patient-specific vision-transformer-based network was developed and shown to be accurate and efficient to reconstruct 3D CT images from kV images.
Disformal symmetry in the Universe: mimetic gravity and beyond

Symmetries play an important role in fundamental physics. In gravity and field theories, particular attention has been paid to Weyl (or conformal) symmetry. However, once the theory contains a scalar field, conformal transformations of the metric can be considered a subclass of a more general type of transformation, so-called disformal transformation. Here, we investigate the implications of pure disformal symmetry in the Universe. We derive the form of general disformal invariant tensors from which we build the most general disformal invariant action. We argue that, in cosmology, disformal symmetry amounts to require that the lapse function is fully replaced by a (time-like) scalar field at the level of the action. We then show that disformal symmetry is in general an exactly equivalent formulation of general mimetic gravity. Lastly, we go beyond mimetic gravity and find that a particular class of invariance leads to seemingly Ostrogradski-like (with higher derivatives) Lagrangians, which are nevertheless absent of Ostrogradski ghosts in a cosmological background, despite having an additional degree of freedom. We also propose an application of our formalism to find new invertible disformal transformations, where the coefficient involves higher derivatives and curvature, further expanding the theory space of scalar-tensor theories.
Interpolation property of shallow neural networks

We study the geometry of global minima of the loss landscape of overparametrized neural networks. In most optimization problems, the loss function is convex, in which case we only have a global minima, or nonconvex, with a discrete number of global minima. In this paper, we prove that in the overparametrized regime, a shallow neural network can interpolate any data set, i.e. the loss function has a global minimum value equal to zero as long as the activation function is not a polynomial of small degree. Additionally, if such a global minimum exists, then the locus of global minima has infinitely many points. Furthermore, we give a characterization of the Hessian of the loss function evaluated at the global minima, and in the last section, we provide a practical probabilistic method of finding the interpolation point.
A GPU-accelerated adaptive discontinuous Galerkin method for level set equation

ABSTRACT This paper presents a GPU-accelerated nodal discontinuous Galerkin method for the solution of two- and three-dimensional level set (LS) equation on unstructured adaptive meshes. Using adaptive mesh refinement, computations are localised mostly near the interface location to reduce the computational cost. Small global time step size resulting from the local adaptivity is avoided by local time-stepping based on a multi-rate Adams–Bashforth scheme. Platform independence of the solver is achieved with an extensible multi-threading programming API that allows runtime selection of different computing devices (GPU and CPU) and different threading interfaces (CUDA, OpenCL and OpenMP). Overall, a highly scalable, accurate and mass conservative numerical scheme that preserves the simplicity of LS formulation is obtained. Efficiency, performance and local high-order accuracy of the method are demonstrated through distinct numerical test cases.
Downstream Task-Oriented Neural Tokenizer Optimization with Vocabulary Restriction as Post Processing

This paper proposes a method to optimize tokenization for the performance improvement of already trained downstream models. Our method generates tokenization results attaining lower loss values of a given downstream model on the training data for restricting vocabularies and trains a tokenizer reproducing the tokenization results. Therefore, our method can be applied to variety of tokenization methods, while existing work cannot due to the simultaneous learning of the tokenizer and the downstream model. This paper proposes an example of the BiLSTM-based tokenizer with vocabulary restriction, which can capture wider contextual information for the tokenization process than non-neural-based tokenization methods used in existing work. Experimental results on text classification in Japanese, Chinese, and English text classification tasks show that the proposed method improves performance compared to the existing methods for tokenization optimization.