Miracle-dz/UniFAR-FTUs huggingface dataset:
Physics potential of the Tokai-to-Kamioka-and-Korea proposal: An extension of the Tokai-to-Kamioka neutrino oscillation experiment with a far detector in Korea

We study the physics potential of placing a far detector in the east coast of Korea, where the off-axis neutrino beam from J-PARC at Tokai village for the Tokai-to-Kamioka project has significant intensity at a few GeV range. In particular, we examine the capability of determining the mass hierarchy pattern and the $CP$ phase of the lepton-flavor-mixing matrix when a 100 kt water \ifmmode \check{C}\else \v{C}\fi{}erenkov detector is placed at various locations in Korea for the off-axis beam (OAB) of 2.5\ifmmode^\circ\else\textdegree\fi{} and 3.0\ifmmode^\circ\else\textdegree\fi{} at the Super-Kamiokande site. The best results are found for a combination of 3.0\ifmmode^\circ\else\textdegree\fi{} OAB at SK ($L=295\text{ }\text{ }\mathrm{km}$) and 0.5\ifmmode^\circ\else\textdegree\fi{} OAB at $L=1000\text{ }\text{ }\mathrm{km}$, where the mass hierarchy pattern can be determined at $3\ensuremath{\sigma}$ level for ${sin﻿}^{2}2{\ensuremath{\theta}}_{\mathrm{RCT}}\ensuremath{\gtrsim}0.05$ (0.06) when the hierarchy is normal (inverted), after 5 yr of running ($5\ifmmode\times\else\texttimes\fi{}{10}^{21}\text{ }\text{ }\mathrm{POT}$). We also find that the leptonic $CP$ phase, ${\ensuremath{\delta}}_{\mathrm{MNS}}$, can be constrained uniquely, without invoking antineutrino beams, as long as the mass hierarchy pattern is determined. Those results are obtained by assuming that the charged current quasielastic events can be separated from the other backgrounds with high efficiency, the neutrino energy can be reconstructed with a hundred MeV uncertainty, and the earth matter density along the baseline can be determined with 3% accuracy.
Multi-institutional Validation of Improved Vesicoureteral Reflux Assessment With Simple and Machine Learning Approaches

Purpose: Vesicoureteral reflux grading from voiding cystourethrograms is highly subjective with low reliability. We aimed to demonstrate improved reliability for vesicoureteral reflux grading with simple and machine learning approaches using ureteral tortuosity and dilatation on voiding cystourethrograms. Materials and Methods: Voiding cystourethrograms were collected from our institution for training and 5 external data sets for validation. Each voiding cystourethrogram was graded by 5-7 raters to determine a consensus vesicoureteral reflux grade label and inter- and intra-rater reliability was assessed. Each voiding cystourethrogram was assessed for 4 features: ureteral tortuosity, proximal, distal, and maximum ureteral dilatation. The labels were then assigned to the combination of the 4 features. A machine learning-based model, qVUR, was trained to predict vesicoureteral reflux grade from these features and model performance was assessed by AUROC (area under the receiver-operator-characteristic). Results: A total of 1,492 kidneys and ureters were collected from voiding cystourethrograms resulting in a total of 8,230 independent gradings. The internal inter-rater reliability for vesicoureteral reflux grading was 0.44 with a median percent agreement of 0.71 and low intra-rater reliability. Higher values for each feature were associated with higher vesicoureteral reflux grade. qVUR performed with an accuracy of 0.62 (AUROC=0.84) with stable performance across all external data sets. The model improved vesicoureteral reflux grade reliability by 3.6-fold compared to traditional grading (P < .001). Conclusions: In a large pediatric population from multiple institutions, we show that machine learning-based assessment for vesicoureteral reflux improves reliability compared to current grading methods. qVUR is generalizable and robust with similar accuracy to clinicians but the added prognostic value of quantitative measures warrants further study.
The glutamate receptor-like 3.3 and 3.6 mediate systemic resistance to insect herbivores in Arabidopsis

Abstract Herbivory activates responses in local and systemic leaves, and the glutamate receptor-like genes GLR3.3 and GLR3.6 are critical in leaf-to-leaf systemic signalling. However, whether and how these genes mediate plant systemic resistance to insects remain largely unexplored. We show that a piercing-sucking insect Myzus persicae (green peach aphid, GPA) or chewing insect Spodoptera litura (cotton leafworm, CLW) feeding-induced systemic defences were attenuated in the glr3.3 glr3.6 mutants. In response to herbivory from either insect, glr3.3 glr3.6 mutants exhibited reduced accumulation of the hormone jasmonic acid (JA) and defensive metabolites glucosinolates (GSs) in systemic (but not local) leaves. Transcriptome analysis indicated that GLR3.3 and GLR3.6 play an important role in regulating the transcriptional responses to GPA and simulated CLW feeding in both local and systemic leaves, including JA- and GS-related genes. Metabolome analysis also revealed that in response to GPA or simulated CLW feeding, GLR3.3 and GLR3.6 are involved in the regulation of various metabolites locally and systemically, including amino acids, carbohydrates, and organic acids. Taken together, this study provides new insights into the function of GLR3.3 and GLR3.6 in mediating transcripts and metabolites in local and systemic leaves under insect attack, and highlights their role in regulating insect resistance in systemic leaves.
Surface and finite size effect on fluctuations dynamics in nanoparticles with long-range order

The influence of surface and finite size on the dynamics of the order parameter fluctuations and critical phenomena in the three-dimensional (3D)-confined systems with long-range order was not considered theoretically. In this paper, we study the influence of surface and finite size on the dynamics of the order parameter fluctuations in the particles of arbitrary shape. We consider concrete examples of the spherical and cylindrical ferroic nanoparticles within Landau–Ginzburg–Devonshire phenomenological approach. Allowing for the strong surface energy contribution in micro and nanoparticles, the analytical expressions derived for the Ornstein–Zernike correlator of the long-range order parameter spatial-temporal fluctuations, dynamic generalized susceptibility, relaxation times, and correlation radii discrete spectra are different from those known for bulk systems. Obtained analytical expressions for the correlation function of the order parameter spatial-temporal fluctuations in micro and nanosized system.
Seasonality and predictors of childhood stunting and wasting in drought-prone areas in Ethiopia: a cohort study

Background and objectives Over centuries, Ethiopia has experienced severe famines and periods of serious drought, and malnutrition remains a major public health problem. The aims of this study were to estimate seasonal variations in child stunting and wasting, and identify factors associated with both forms of child malnutrition in drought-prone areas. Methods This cohort study was conducted among a random sample of 909 children in rural southern Ethiopia. The same children were followed for 1 year (2017–2018) with quarterly repeated measurements of their outcomes: height-for-age and weight-for-height indices (Z-scores). Linear regression models were used to analyse the association between both outcomes and baseline factors (eg, household participation in a social safety net programme and water access) and some time-varying factors (eg, household food insecurity). Results Child wasting rates varied with seasonal household food insecurity (ᵪ2trend = 15.9, p=0.001), but stunting rates did not. Household participation in a social safety net programme was associated with decreased stunting (p=0.001) and wasting (p=0.002). In addition to its association with decreased wasting (p=0.001), protected drinking water access enhanced the association between household participation in a social safety net programme and decreased stunting (p=0.009). Absence of a household latrine (p=0.011), lower maternal education level (p=0.001), larger family size (p=0.004) and lack of non-farming income (p=0.002) were associated with increased child stunting. Conclusions Seasonal household food insecurity was associated with child undernutrition in rural Ethiopia. Strengthening community-based food security programmes, such as the Ethiopian social safety net programme, could help to reduce child undernutrition in drought-prone areas. Improving clean water access and sanitation could also decrease child undernutrition. Key terms: Z-scores; Social safety net program; Water access
Explaining Predictive Uncertainty by Looking Back at Model Explanations

Predictive uncertainty estimation of pretrained language models is an important measure of how likely people can trust their predictions. However, little is known about what makes a model prediction uncertain. Explaining predictive uncertainty is an important complement to explaining prediction labels in helping users understand model decision making and gaining their trust on model predictions, while has been largely ignored in prior works. In this work, we propose to explain the predictive uncertainty of pre-trained language models by extracting uncertain words from existing model explanations. We ﬁnd the uncertain words are those identiﬁed as making negative contributions to prediction labels, while actually explaining the predictive uncertainty. Experiments show that uncertainty explanations are indispensable to explaining models and helping humans understand model prediction behavior.
Generative Language Models for Paragraph-Level Question Generation

Powerful generative models have led to recent progress in question generation (QG). However, it is difficult to measure advances in QG research since there are no standardized resources that allow a uniform comparison among approaches. In this paper, we introduce QG-Bench, a multilingual and multidomain benchmark for QG that unifies existing question answering datasets by converting them to a standard QG setting. It includes general-purpose datasets such as SQuAD for English, datasets from ten domains and two styles, as well as datasets in eight different languages. Using QG-Bench as a reference, we perform an extensive analysis of the capabilities of language models for the task. First, we propose robust QG baselines based on fine-tuning generative language models. Then, we complement automatic evaluation based on standard metrics with an extensive manual evaluation, which in turn sheds light on the difficulty of evaluating QG models. Finally, we analyse both the domain adaptability of these models as well as the effectiveness of multilingual models in languages other than English.QG-Bench is released along with the fine-tuned models presented in the paper (https://github.com/asahi417/lm-question-generation), which are also available as a demo (https://autoqg.net/).
Less is More: Facial Landmarks can Recognize a Spontaneous Smile

Smile veracity classification is a task of interpreting social interactions. Broadly, it distinguishes between spontaneous and posed smiles. Previous approaches used hand-engineered features from facial landmarks or considered raw smile videos in an end-to-end manner to perform smile classification tasks. Feature-based methods require intervention from human experts on feature engineering and heavy pre-processing steps. On the contrary, raw smile video inputs fed into end-to-end models bring more automation to the process with the cost of considering many redundant facial features (beyond landmark locations) that are mainly irrelevant to smile veracity classification. It remains unclear to establish discriminative features from landmarks in an end-to-end manner. We present a MeshSmileNet framework, a transformer architecture, to address the above limitations. To eliminate redundant facial features, our landmarks input is extracted from Attention Mesh, a pre-trained landmark detector. Again, to discover discriminative features, we consider the relativity and trajectory of the landmarks. For the relativity, we aggregate facial landmark that conceptually formats a curve at each frame to establish local spatial features. For the trajectory, we estimate the movements of landmark composed features across time by self-attention mechanism, which captures pairwise dependency on the trajectory of the same landmark. This idea allows us to achieve state-of-the-art performances on UVA-NEMO, BBC, MMI Facial Expression, and SPOS datasets.
The typical width of Herschel filaments

Context. Dense molecular ﬁlaments are widely believed to be representative of the initial conditions of star formation in interstellar clouds. Characterizing their physical properties such as their transverse size is therefore of paramount importance. Herschel studies suggest that nearby ( d < 500pc) molecular ﬁlaments have a typical half-power width ∼ 0.1pc, but this ﬁnding has been questioned recently on the ground that the measured widths tend to increase with the distance to the ﬁlaments. Aims. Here we revisit the dependence of measured ﬁlament widths on distance or equivalently spatial resolution, in an e ﬀ ort to determine whether nearby molecular ﬁlaments have a characteristic half-power width or whether this is an artifact of the ﬁnite resolution of the Herschel data. Methods. We perform a convergence test on the well-documented B211 / 213 ﬁlament in Taurus, by degrading the resolution of the Herschel data several times and re-estimating the ﬁlament width from the resulting column density proﬁles. We also compare the widths measured for the Taurus ﬁlament and other ﬁlaments from the Herschel Gould Belt survey with those found for synthetic ﬁlaments with various types of simple, idealized column density proﬁles (Gaussian, power law, Plummer-like). ﬁnd that the measured ﬁlament widths do increase slightly as the spatial resolution worsens and / or the distance to the ﬁlaments increases. However, this trend is entirely consistent with what is expected from simple beam convolution for ﬁlaments with density proﬁles that are Plummer-like and have intrinsic half-power diameters ∼ 0.08–0.1pc and logarithmic slopes 1 . 5 < p < 2 . 5 at large radii, as directly observed in many cases including the Taurus ﬁlament. Due to the presence of background noise ﬂuctuations, deconvolution of the measured widths from the telescope beam is di ﬃ cult and quickly becomes inaccurate. Conclusions. We conclude that the typical half-power ﬁlament width ∼ 0.1pc measured with Herschel in nearby clouds most likely reﬂects the presence of a true common scale in the ﬁlamentary structure of the cold interstellar medium, at least in the solar neighbor-hood. We suggest that this common scale may correspond to the magnetized turbulent correlation length in molecular clouds.
Improving Fine-Grain Segmentation via Interpretable Modifications: A Case Study in Fossil Segmentation

Most interpretability research focuses on datasets containing thousands of images of commonplace objects. However, many high-impact datasets, such as those in medicine and the geosciences, contain ﬁne-grain objects that require domain-expert knowledge to recognize and are time-consuming to collect and annotate. As a result, these datasets contain few annotated images, and current machine vision models cannot train intensively on them. Thus, adapting interpretability techniques to maximize the amount of information that models can learn from small, ﬁne-grain datasets is an important endeavor. Using a Mask R-CNN to segment ancient reef fossils in rock sample images, we present a general paradigm for identifying and mitigating model weaknesses. Speciﬁcally, we apply image perturbations to expose the Mask R-CNN’s inability to distinguish between different classes of fossils and its inconsistency in segmenting fossils with different textures. To address these shortcomings, we extend an existing model-editing method for correcting systematic mistakes in image classiﬁcation to image segmentation and introduce a novel application of the technique: encouraging a greater separation between positive and negative pixels for a given class. Through extensive experiments, we ﬁnd that editing the model by perturbing all pixels for a given class in one image is most effective (compared to using multiple images and/or fewer pixels). Our paradigm may also generalize to other segmentation models trained on small, ﬁne-grain datasets.
APUD(1,1) Recognition in Polynomial Time

A unit disk graph is the intersection graph of a set of disk of unit radius in the Euclidean plane. In 1998, Breu and Kirkpatrick showed that the recognition problem for unit disk graphs is NP-hard. Given $k$ horizontal and $m$ vertical lines, an APUD($k,m$) is a unit disk graph such that each unit disk is centered either on a given horizontal or vertical line. \c{C}a\u{g}{\i}r{\i}c{\i} showed in 2020 that APUD($k,m$) recognition is NP-hard. In this paper, we show that APUD($1,1$) recognition is polynomial time solvable.
Highly sensitive giant magnetoimpedance in a solenoid containing FeCo-based ribbon

The highly sensitive giant magneto-impedance effect in a solenoid containing a magnetic core of Fe36Co36Nb4Si4.8B19. 2 (FeCo-based) ribbon under a weak magnetic field (WMF) is presented in this paper. The FeCo-based amorphous ribbon is prepared by single roller quenching and annealed with Joule heat in a flowing nitrogen atmosphere. The giant magnetoimpedance effect in solenoid (GMIES) profiles are measured with an HP4294A impedance analyzer. The result shows that the GMIES responds to the WMF sensitively (as high as 1580 %/A·m−1). The high sensitivity can be obtained in a moderate narrow range of annealing current density (30–34 A/mm2) and closely depends on the driven current frequency. The highest sensitivity (1580 %/A·m−1) is obtained when the FeCo-based amorphous ribbon is annealed at 32 A/mm2 for 10 min and then driven with an alterning current (AC) at the frequency of 350 kHz. The highly sensitive GMIES under the WMF may result from the multiple magnetic-anisotropic structure, which is induced by the temperature gradient produced during Joule-heating the ribbon.
Fine-grained Object Categorization for Service Robots

—A robot working in a human-centered environment is frequently confronted with ﬁne-grained objects that must be distinguished from one another. Fine-grained visual classiﬁcation (FGVC) still remains a challenging problem due to large intra-category dissimilarity and small inter-category dissimilarity. Furthermore, ﬂaws such as the inﬂuence of illumination and information inadequacy persist in ﬁne-grained RGB datasets. We propose a novel deep mixed multi-modality approach based on Vision Transformer (ViT) and Convolutional Neural Network (CNN) to improve the performance of FGVC. Furthermore, we generate two synthetic ﬁne-grained RGB-D datasets consisting of 13 car objects with 720 views and 120 shoes with 7200 sample views. Finally, to assess the performance of the proposed approach, we conducted several experiments using ﬁne-grained RGB-D datasets. Experimental results show that our method outperformed other baselines in terms of recognition accuracy, and achieved 93.40 % and 91.67 % recognition accuracy on shoe and car dataset respectively. We made the ﬁne-grained RGB-D datasets publicly available for the beneﬁt of research communities. The video is available at https://youtu.be/c8Tqy6uLV08
Coresets for Relational Data and The Applications

A coreset is a small set that can approximately preserve the structure of the original input data set. Therefore we can run our algorithm on a coreset so as to reduce the total computational complexity. Conventional coreset techniques assume that the input data set is available to process explicitly. However, this assumption may not hold in real-world scenarios. In this paper, we consider the problem of coresets construction over relational data. Namely, the data is decoupled into several relational tables, and it could be very expensive to directly materialize the data matrix by joining the tables. We propose a novel approach called ``aggregation tree with pseudo-cube'' that can build a coreset from bottom to up. Moreover, our approach can neatly circumvent several troublesome issues of relational learning problems [Khamis et al., PODS 2019]. Under some mild assumptions, we show that our coreset approach can be applied for the machine learning tasks, such as clustering, logistic regression and SVM.
5G NR-LTE Coexistence: Opportunities, Challenges, and Solutions

5G New Radio (NR) promises to support diverse services such as enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC). This requires spectrum, most of which is occupied by 4G Long Term Evolution (LTE). Hence, network operators are expected to deploy 5G using the existing LTE infrastructure while migrating to NR. In addition, operators must support legacy LTE devices during the migration, so LTE and NR systems will coexist for the foreseeable future. In this article, we address LTE-NR coexistence starting with a review of both radio access technologies. We then describe the contributions by the 3rd Generation Partnership Project (3GPP) to solving the coexistence issue and catalog the major coexistence scenarios. Lastly, we introduce a novel spectrum sharing scheme that can be applied to the coexistence scenarios under study.
Analysis of bistable behavior and early warning signals of extinction in a class of predator-prey models

In this paper, we develop a method of detecting an early warning signal of catastrophic population collapse in a class of predator-prey models with two species of predators competing for their common prey, where the prey evolves on a faster timescale than the predators. In a parameter regime near singular Hopf bifurcation of a coexistence equilibrium point, we assume that the class of models exhibits bistability between a periodic attractor and a boundary equilibrium point, where the invariant manifolds of the coexistence equilibrium play central roles in organizing the dynamics. To determine whether a solution that starts in a vicinity of the coexistence equilibrium approaches the periodic attractor or the point attractor, we reduce the equations to a suitable normal form, which is valid near the singular Hopf bifurcation, and study its geometric structure. A key component of our study includes an analysis of the tran- sient dynamics, characterized by their rapid oscillations with a slow variation in amplitude, by applying a moving average technique. As a result of our analysis, we could devise a method for identifying early warning signals, signiﬁcantly in advance, of a future crisis that could lead to extinction of one of the predators. The analysis is applied to the predator-prey model considered in [ Discrete and Continuous Dynamical Systems - B 2021, 26(10), pp. 5251-5279] and we ﬁnd that our theory is in good agreement with the numerical simulations carried out for this model.