Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence of body weight dissatisfaction among adolescents: a systematic review

ABSTRACT Objective: To identify the prevalence of weight dissatisfaction among adolescents aged 10-19 years and stratify the analysis by sex. Data source: A literature review of cross-sectional studies among healthy adolescents was performed. The U.S. National Library of Medicine/National Institutes of Health (PubMed), Ovid® (Wolters Kluwer), The Cumulative Index to Nursing and Allied Health Literature (CINAHL), and American Psychological Association (PsycINFO®) databases were searched between May 2019 and January 2020. Data synthesis: Initially, 3,700 records were identified, and 10 papers were obtained through other sources. After the removal of duplicates, 1,732 records were screened based on the titles and abstracts, and 126 were preselected for full-text analysis. After the application of the eligibility criteria, 34 papers were included in the present review. The studies were published between 1997 and 2020. The sample size ranged from <150 to >103,000 adolescents. The prevalence of weight dissatisfaction ranged from 18.0 to 56.6% in both sexes (10.8-82.5% among boys and 19.2-83.8% among girls). Conclusions: Based on the findings of the present systematic review, the prevalence of weight dissatisfaction is high among adolescents, especially girls. Such information can contribute to the planning of health and education programs addressing the issue of weight in adolescents.
The Fragility of Multi-Treebank Parsing Evaluation

Treebank selection for parsing evaluation and the spurious effects that might arise from a biased choice have not been explored in detail. This paper studies how evaluating on a single subset of treebanks can lead to weak conclusions. First, we take a few contrasting parsers, and run them on subsets of treebanks proposed in previous work, whose use was justified (or not) on criteria such as typology or data scarcity. Second, we run a large-scale version of this experiment, create vast amounts of random subsets of treebanks, and compare on them many parsers whose scores are available. The results show substantial variability across subsets and that although establishing guidelines for good treebank selection is hard, some inadequate strategies can be easily avoided.
Overleaf Example

Tensegrity robots, which are composed of compressive elements (rods) and flexible tensile elements (e.g., cables), have a variety of advantages, including flexibility, low weight, and resistance to mechanical impact. Nevertheless, the hybrid soft-rigid nature of these robots also complicates the ability to localize and track their state. This work aims to address what has been recognized as a grand challenge in this domain, i.e., the state estimation of tensegrity robots through a marker-less, vision-based method, as well as novel, on-board sensors that can measure the length of the robot’s cables. In particular, an iterative optimization process is proposed to track the 6-DoF pose of each rigid element of a tensegrity robot from an RGB-D video as well as endcap distance measurements from the cable sensors. To ensure that the pose estimates of rigid elements are physically feasible, i.e., they are not resulting in collisions between rods or with the environment, physical constraints are introduced during the optimization. Real-world experiments are performed with a 3-bar tensegrity robot, which performs locomotion gaits. Given ground truth data from a motion capture system, the proposed method achieves less than 1 cm translation error and 3 degrees rotation error, which significantly outperforms alternatives. At the same time, the approach can provide accurate pose estimation throughout the robot’s motion, while motion capture often fails due to occlusions.
Fast-response hot-wire flow sensors for wind and gust estimation on UAVs

Due to limitations in available sensor technology, unmanned aerial vehicles (UAVs) lack an active sensing capability to measure turbulence, gusts, or other unsteady aerodynamic phenomena. Conventional in situ anemometry techniques fail to deliver in the harsh and dynamic multirotor environment due to form factor, resolution, or robustness requirements. To address this capability gap, a novel, fast-response sensor system to measure a wind vector in two dimensions is introduced and evaluated. This system, known as ‘MAST’ (for MEMS Anemometry Sensing Tower), leverages advances in microelectromechanical (MEMS) hot-wire devices to produce a solid-state, lightweight, and robust flow sensor suitable for real-time wind estimation onboard an UAV. The MAST uses five pentagonally-arranged microscale hot-wires to determine the wind vector’s direction and magnitude. The MAST’s performance was evaluated in a wind tunnel at speeds up to 5 m s−1 and orientations of 0∘–360∘. A neural network sensor model was trained from the wind tunnel data to estimate the wind vector from sensor signals. The average error of the sensor is 0.14 m s−1 for speed and 1.6∘ for direction. Furthermore, 95% of measurements are within 0.36 m s−1 for speed and 5.0∘ for direction. With a bandwidth of 570 Hz determined from square-wave testing, the MAST stands to greatly enhance UAV wind estimation capabilities and enable capturing relevant high-frequency phenomena in flow conditions.
Study on the Concentration Measurement in a Liquid Jet by the Optical Fiber LIF Method

In this study, new techniques for measuring the concentration of diffusing matter in a liquid axisymmetric turbulent jet is developed based on the laser-induced fluorescence (LIF) method using optical fibers with GRIN lens. Firstly, the concentration measurement of the diffusing matter in a liquid jet by means of two optical fibers is performed. A laser light whose beam spot diameter is 60 μm is emitted on the diffusing matter by means of an optical fiber with condenser lens and the fluorescence is measured by the other optical fiber. Secondly, the concentration measurement in the same liquid jet by means of the same optical fiber which emits the laser beam and collects the fluorescence is performed. Preliminary calibration tests show that the output voltage of the photomultiplier linearly increases with the concentration of the diffusing matter as theoretically predicted in both two measurement techniques. In addition, the mean concentration and fluctuating concentration field in a liquid jet can be measured in both two measurement techniques, whereas the measurement accuracy is better in the measurement with using only one optical fiber from the results of the power spectra of the concentration fluctuation.
Detection of Smart Grid Integrity Attacks Using Signal Temporal Logic

Cyber-attacks can have severe impacts on critical infrastructures, from outages to economical loss and physical damage to people and environment. One of the main targets of these attacks is the smart grid. In this paper, we propose a new software detector for integrity attacks targeting smart meter readings. The detector relies upon mining parameters of temporal logic specifications for integrity attack classification. To this end, we use Signal Temporal Logic (STL) for specifying properties over time series. Our approach considers different"attack scenarios"found in last years: given a parametric formula for each"attack scenario"and a set of labeled traces, we aim at finding the parameter valuation that validates each template.
Importance of radiative pumping for the excitation of the H2O submillimeter lines in galaxies

H_2O submillimeter emission is a powerful diagnostic of the molecular interstellar medium in a variety of sources, including low- and high-mass star forming regions of the Milky Way, and from local to high redshift galaxies. However, the excitation mechanism of these lines in galaxies has been debated, preventing a basic consensus on the physical information that H_2O provides. Both radiative pumping due to H_2O absorption of far-infrared photons emitted by dust and collisional excitation in dense shocked gas have been proposed to explain the H_2O emission. Here we propose two basic diagnostics to distinguish between the two mechanisms: 1) in shock excited regions, the ortho-H_2O 3_{21}-2_{12} 75um and the para-H_2O 2_{20}-1_{11} 101um rotational lines are expected to be in emission while, if radiative pumping dominates, both far-infrared lines are expected to be in absorption; 2) based on statistical equilibrium of H_2O level populations, the radiative pumping scenario predicts that the apparent isotropic net rate of far-infrared absorption in the 3_{21}-2_{12} (75um) and 2_{20}-1_{11} (101um) lines should be higher than or equal to the apparent isotropic net rate of submillimeter emission in the 3_{21}-3_{12} (1163 GHz) and 2_20-2_{11} (1229 GHz) lines, respectively. Applying both criteria to all 16 galaxies and several galactic high-mass star-forming regions where the H_2O 75um and submillimeter lines have been observed with Herschel/PACS and SPIRE, we show that in most (extra)galactic sources the H_2O submillimeter line excitation is dominated by far-infrared pumping, with collisional excitation of the low-excitation levels in some of them. Based on this finding, we revisit the interpretation of the correlation between the luminosity of the H_2O 988 GHz line and the source luminosity in the combined galactic and extragalactic sample.
Tuple Packing: Efficient Batching of Small Graphs in Graph Neural Networks

When processing a batch of graphs in machine learning models such as Graph Neural Networks (GNN), it is common to combine several small graphs into one overall graph to accelerate processing and remove or reduce the overhead of padding. This is for example supported in the PyG library. However, the sizes of small graphs can vary substantially with respect to the number of nodes and edges, and hence the size of the combined graph can still vary considerably, especially for small batch sizes. Therefore, the costs of excessive padding and wasted compute are still incurred when working with static shapes, which are preferred for maximum acceleration. This paper proposes a new hardware agnostic approach -- tuple packing -- for generating batches that cause minimal overhead. The algorithm extends recently introduced sequence packing approaches to work on the 2D tuples of (|nodes|, |edges|). A monotone heuristic is applied to the 2D histogram of tuple values to define a priority for packing histogram bins together with the objective to reach a limit on the number of nodes as well as the number of edges. Experiments verify the effectiveness of the algorithm on multiple datasets.
Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs

Single-cell RNA-seq datasets are growing in size and complexity, enabling the study of cellular composition changes in various biological/clinical contexts. Scalable dimensionality reduction techniques are in need to disentangle biological variation in them, while accounting for technical and biological confounders. In this work, we extend a popular approach for probabilistic non-linear dimensionality reduction, the Gaussian process latent variable model, to scale to massive single-cell datasets while explicitly accounting for technical and biological confounders. The key idea is to use an augmented kernel which preserves the factorisability of the lower bound allowing for fast stochastic variational inference. We demonstrate its ability to reconstruct latent signatures of innate immunity recovered in Kumasaka et al. (2021) with 9x lower training time. We further analyze a COVID dataset and demonstrate across a cohort of 130 individuals, that this framework enables data integration while capturing interpretable signatures of infection. Specifically, we explore COVID severity as a latent dimension to refine patient stratification and capture disease-specific gene expression.
Neural Point-based Shape Modeling of Humans in Challenging Clothing

Parametric 3D body models like SMPL only represent minimally-clothed people and are hard to extend to clothing because they have a fixed mesh topology and resolution. To address these limitations, recent work uses implicit surfaces or point clouds to model clothed bodies. While not limited by topology, such methods still struggle to model clothing that deviates significantly from the body, such as skirts and dresses. This is because they rely on the body to canonicalize the clothed surface by reposing it to a reference shape. Unfortunately, this process is poorly defined when clothing is far from the body. Additionally, they use linear blend skinning to pose the body and the skinning weights are tied to the underlying body parts. In contrast, we model the clothing deformation in a local coordinate space without canonicalization. We also relax the skinning weights to let multiple body parts influence the surface. Specifically, we extend point-based methods with a coarse stage, that replaces canonicalization with a learned pose-independent “coarse shape” that can capture the rough surface geometry of clothing like skirts. We then refine this using a network that infers the linear blend skinning weights and pose dependent displacements from the coarse representation. The approach works well for garments that both conform to, and deviate from, the body. We demonstrate the usefulness of our approach by learning person-specific avatars from examples and then show how they can be animated in new poses and motions. We also show that the method can learn directly from raw scans with missing data, greatly simplifying the process of creating realistic avatars. Code is available for research purposes at https://qianlim.github. io/SkiRT.
SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers

This paper aims to improve the performance of text-to-SQL parsing by exploring the intrinsic uncertainties in the neural network based approaches (called SUN). From the data uncertainty perspective, it is indisputable that a single SQL can be learned from multiple semantically-equivalent questions. Different from previous methods that are limited to one-to-one mapping, we propose a data uncertainty constraint to explore the underlying complementary semantic information among multiple semantically-equivalent questions (many-to-one) and learn the robust feature representations with reduced spurious associations. In this way, we can reduce the sensitivity of the learned representations and improve the robustness of the parser. From the model uncertainty perspective, there is often structural information (dependence) among the weights of neural networks. To improve the generalizability and stability of neural text-to-SQL parsers, we propose a model uncertainty constraint to refine the query representations by enforcing the output representations of different perturbed encoding networks to be consistent with each other. Extensive experiments on five benchmark datasets demonstrate that our method significantly outperforms strong competitors and achieves new state-of-the-art results.
The Role of Executable Abstract Programs in Software Development and Documentation

We present Executable Abstract Programs and analyse their role for software development and documentation. The intuitive understanding of these programs fits the computational mindset of software system engineers and is supported by a simple but precise behavioural definition. Therefore, they can be smoothly integrated in the practitioner's daily work to rigorously formulate every design and implementation decision taken on the path from the Executable Abstract Program for the requirements to the targeted and efficiently runnable code. The Executable Abstract Programs of the resulting system documentation represent definitions of implementation steps one can check and justify by testing (due to their executable character) or by reasoning (due to the mathematical definition of their behaviour). For complex systems the implementation involves multiple (orthogonal or successive) implementation steps which represent instances of a practical computational refinement concept. Such a system development process is driven by computational refinements and is strictly limited to explicitly formulate and justify -- besides the requirements -- only the necessary implementation steps. As a consequence, it produces as side-effect a corpus of documentation that facilitates the understandability of the final code and improves its reliability and resilience; it also enhances the maintenance process (including reuse and change of abstract programs and code) and reduces maintenance cost.
Picosecond Time‐Scale Resistive Switching Monitored in Real‐Time

The resistance state of filamentary memristors can be tuned by relocating only a few atoms at interatomic distances in the active region of a conducting filament. Thereby the technology holds promise not only in its ultimate downscaling potential and energy efficiency but also in unprecedented speed. Yet, the breakthrough in high‐frequency applications still requires the clarification of the dominant mechanisms and inherent limitations of ultra‐fast resistive switching. Here bipolar, multilevel resistive switchings are investigated in tantalum pentoxide based memristors with picosecond time resolution. Cyclic resistive switching operation due to 20 ps long voltage pulses of alternating polarity are experimentally demonstrated. The analysis of the real‐time response of the memristor reveals that the set switching can take place at the picosecond time‐scale where it is only compromised by the bandwidth limitations of the experimental setup. In contrast, the completion of the reset transitions significantly exceeds the duration of the ultra‐short voltage bias, demonstrating the dominant role of thermal diffusion and underlining the importance of dedicated thermal engineering for future high‐frequency memristor circuit applications.
Applying wav2vec2 for Speech Recognition on Bengali Common Voices Dataset

Speech is inherently continuous, where discrete words, phonemes and other units are not clearly segmented, and so speech recognition has been an active research problem for decades. In this work we have fine-tuned wav2vec 2.0 to recognize and transcribe Bengali speech -- training it on the Bengali Common Voice Speech Dataset. After training for 71 epochs, on a training set consisting of 36919 mp3 files, we achieved a training loss of 0.3172 and WER of 0.2524 on a validation set of size 7,747. Using a 5-gram language model, the Levenshtein Distance was 2.6446 on a test set of size 7,747. Then the training set and validation set were combined, shuffled and split into 85-15 ratio. Training for 7 more epochs on this combined dataset yielded an improved Levenshtein Distance of 2.60753 on the test set. Our model was the best performing one, achieving a Levenshtein Distance of 6.234 on a hidden dataset, which was 1.1049 units lower than other competing submissions.
Cornucopia : A Framework for Feedback Guided Generation of Binaries

Binary analysis is an important capability required for many security and software engineering applications. Consequently, there are many binary analysis techniques and tools with varied capabilities. However, testing these tools requires a large, varied binary dataset with corresponding source-level information. In this paper, we present Cornucopia, an architecture agnostic automated framework that can generate a plethora of binaries from corresponding program source by exploiting compiler optimizations and feedback-guided learning. Our evaluation shows that Cornucopia was able to generate 309K binaries across four architectures (x86, x64, ARM, MIPS) with an average of 403 binaries for each program and outperforms BinTuner [53], a similar technique. Our experiments revealed issues with the LLVM optimization scheduler resulting in compiler crashes (∼ 300). Our evaluation of four popular binary analysis tools angr, Ghidra, ida, and radare, using Cornucopia generated binaries, revealed various issues with these tools. Specifically, we found 263 crashes in angr and one memory corruption issue in ida. Our differential testing on the analysis results revealed various semantic bugs in these tools. We also tested machine learning tools, Asm2Vec, SAFE, and Debin, that claim to capture binary semantics and show that they perform poorly (e.g., Debin F1 score dropped to 12.9% from reported 63.1%) on Cornucopia generated binaries. In summary, our exhaustive evaluation shows that Cornucopia is an effective mechanism to generate binaries for testing binary analysis techniques effectively.
SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding

Pre-training methods with contrastive learning objectives have shown remarkable success in dialog understanding tasks. However, current contrastive learning solely considers the self-augmented dialog samples as positive samples and treats all other dialog samples as negative ones, which enforces dissimilar representations even for dialogs that are semantically related. In this paper, we propose SPACE-2, a tree-structured pre-trained conversation model, which learns dialog representations from limited labeled dialogs and large-scale unlabeled dialog corpora via semi-supervised contrastive pre-training. Concretely, we first define a general semantic tree structure (STS) to unify the inconsistent annotation schema across different dialog datasets, so that the rich structural information stored in all labeled data can be exploited. Then we propose a novel multi-view score function to increase the relevance of all possible dialogs that share similar STSs and only push away other completely different dialogs during supervised contrastive pre-training. To fully exploit unlabeled dialogs, a basic self-supervised contrastive loss is also added to refine the learned representations. Experiments show that our method can achieve new state-of-the-art results on the DialoGLUE benchmark consisting of seven datasets and four popular dialog understanding tasks.
On the Classification of Universal Rotor-Routers

The combinatorial theory of rotor-routers has connections with problems of statistical mechanics, graph theory, chaos theory, and computer science. A rotor-router network defines a deterministic walk on a digraph G in which a particle walks from a source vertex until it reaches one of several target vertices. Motivated by recent results due to Giacaglia et al., we study rotor-router networks in which all non-target vertices have the same type. A rotor type r is universal if every hitting sequence can be achieved by a homogeneous rotor-router network consisting entirely of rotors of type r. We give a conjecture that completely classifies universal rotor types. Then, this problem is simplified by a theorem we call the Reduction Theorem that allows us to consider only two-state rotors. A rotor-router network called the compressor, because it tends to shorten rotor periods, is introduced along with an associated algorithm that determines the universality of almost all rotors. New rotor classes, including boppy rotors, balanced rotors, and BURD rotors, are defined to study this algorithm rigorously. Using the compressor the universality of new rotor classes is proved, and empirical computer results are presented to support our conclusions. Prior to these results, less than 100 of the roughly 260,000 possible two-state rotor types of length up to 17 were known to be universal, while the compressor algorithm proves the universality of all but 272 of these rotor types.