Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Constrained Inference Optimization on Structural Groups for Human Pose Estimation

We observe that human poses exhibit strong group-wise structural correlation and spatial coupling between keypoints due to the biological constraints of different body parts. This group-wise structural correlation can be explored to improve the accuracy and robustness of human pose estimation. In this work, we develop a self-constrained prediction-verification network to characterize and learn the structural correlation between keypoints during training. During the inference stage, the feedback information from the verification network allows us to perform further optimization of pose prediction, which significantly improves the performance of human pose estimation. Specifically, we partition the keypoints into groups according to the biological structure of human body. Within each group, the keypoints are further partitioned into two subsets, high-confidence base keypoints and low-confidence terminal keypoints. We develop a self-constrained prediction-verification network to perform forward and backward predictions between these keypoint subsets. One fundamental challenge in pose estimation, as well as in generic prediction tasks, is that there is no mechanism for us to verify if the obtained pose estimation or prediction results are accurate or not, since the ground truth is not available. Once successfully learned, the verification network serves as an accuracy verification module for the forward pose prediction. During the inference stage, it can be used to guide the local optimization of the pose estimation results of low-confidence keypoints with the self-constrained loss on high-confidence keypoints as the objective function. Our extensive experimental results on benchmark MS COCO and CrowdPose datasets demonstrate that the proposed method can significantly improve the pose estimation results.
LETTER TO THE EDITOR: Photoionization of atomic scandium in the region of the 2p resonances

The 2p resonances of atomic scandium at 391-410 eV were studied by photoion spectroscopy using monochromatized synchrotron radiation and the atomic beam technique. The partial ion yield spectra for singly to quadruply charged photoions were observed. The comparison of the total ion yield spectrum with the calculated absorption spectrum shows surprisingly good results.
Enhancing Adversarial Attacks on Single-Layer NVM Crossbar-Based Neural Networks with Power Consumption Information

Adversarial attacks on state-of-the-art machine learning models pose a significant threat to the safety and security of mission-critical autonomous systems. This paper considers the additional vulnerability of machine learning models when attackers can measure the power consumption of their underlying hardware platform. In particular, we explore the utility of power consumption information for adversarial attacks on non-volatile memory crossbar-based single-layer neural networks. Our results from experiments with MNIST and CIFAR-10 datasets show that power consumption can reveal important information about the neural network’s weight matrix, such as the 1-norm of its columns. That information can be used to infer the sensitivity of the network’s loss with respect to different inputs. We also find that surrogate-based black box attacks that utilize crossbar power information can lead to improved attack efficiency.
Multi-Label Retinal Disease Classification Using Transformers

Early detection of retinal diseases is one of the most important means of preventing partial or permanent blindness in patients. In this research, a novel multi-label classification system is proposed for the detection of multiple retinal diseases, using fundus images collected from a variety of sources. First, a new multi-label retinal disease dataset, the MuReD dataset, is constructed, using a number of publicly available datasets for fundus disease classification. Next, a sequence of post-processing steps is applied to ensure the quality of the image data and the range of diseases, present in the dataset. For the first time in fundus multi-label disease classification, a transformer-based model optimized through extensive experimentation is used for image analysis and decision making. Numerous experiments are performed to optimize the configuration of the proposed system. It is shown that the approach performs better than state-of-the-art works on the same task by 7.9% and 8.1% in terms of AUC score for disease detection and disease classification, respectively. The obtained results further support the potential applications of transformer-based architectures in the medical imaging field.
Unsupervised Learning for Stellar Spectra with Deep Normalizing Flows

Stellar spectra encode detailed information about the stars. However, most machine learning approaches in stellar spectroscopy focus on supervised learning. We introduce Mendis, an unsupervised learning method, which adopts normalizing flows consisting of Neural Spline Flows and GLOW to describe the complex distribution of spectral space. A key advantage of Mendis is that we can describe the conditional distribution of spectra, conditioning on stellar parameters, to unveil the underlying structures of the spectra further. In particular, our study demonstrates that Mendis can robustly capture the pixel correlations in the spectra leading to the possibility of detecting unknown atomic transitions from stellar spectra. The probabilistic nature of Mendis also enables a rigorous determination of outliers in extensive spectroscopic surveys without the need to measure elemental abundances through existing analysis pipelines beforehand.
TractoFormer: A Novel Fiber-level Whole Brain Tractography Analysis Framework Using Spectral Embedding and Vision Transformers

Diffusion MRI tractography is an advanced imaging technique for quantitative mapping of the brain’s structural connectivity. Whole brain tractography (WBT) data contains over hundreds of thousands of individual fiber streamlines (estimated brain connections), and this data is usually parcellated to create compact representations for data analysis applications such as disease classification. In this paper, we propose a novel parcellation-free WBT analysis framework, TractoFormer , that leverages tractography information at the level of individual fiber streamlines and provides a natural mechanism for interpretation of results using the attention mechanism of transformers. TractoFormer includes two main contributions. First, we propose a novel and simple 2D image representation of WBT, TractoEmbedding , to encode 3D fiber spatial relation-ships and any feature of interest that can be computed from individual fibers (such as FA or MD). Second, we design a network based on vision transformers (ViTs) that includes: 1) data augmentation to overcome model overfitting on small datasets, 2) identification of discriminative fibers for interpretation of results, and 3) ensemble learning to leverage fiber information from different brain regions. In a synthetic data experiment, TractoFormer successfully identifies discriminative fibers with simulated group differences. In a disease classification experiment comparing several methods, TractoFormer achieves the highest accuracy in classifying schizophrenia vs control. Discriminative fibers are identified in left hemispheric frontal and parietal superficial white matter regions, which have previously been shown to be affected in schizophrenia patients.
Tensor Neural Network and Its Numerical Integration

In this paper, we introduce a type of tensor neural network. For the first time, we propose its numerical integration scheme and prove the computational complexity to be the polynomial scale of the dimension. Based on the tensor product structure, we develop an efficient numerical integration method by using fixed quadrature points for the functions of the tensor neural network. The corresponding machine learning method is also introduced for solving high-dimensional problems. Some numerical examples are also provided to validate the theoretical results and the numerical algorithm.
Domain Adaptive Video Segmentation via Temporal Pseudo Supervision

Video semantic segmentation has achieved great progress under the supervision of large amounts of labelled training data. However, domain adaptive video segmentation, which can mitigate data labelling constraints by adapting from a labelled source domain toward an unlabelled target domain, is largely neglected. We design temporal pseudo supervision (TPS), a simple and effective method that explores the idea of consistency training for learning effective representations from unlabelled target videos. Unlike traditional consistency training that builds consistency in spatial space, we explore consistency training in spatiotemporal space by enforcing model consistency across augmented video frames which helps learn from more diverse target data. Specifically, we design cross-frame pseudo labelling to provide pseudo supervision from previous video frames while learning from the augmented current video frames. The cross-frame pseudo labelling encourages the network to produce high-certainty predictions, which facilitates consistency training with cross-frame augmentation effectively. Extensive experiments over multiple public datasets show that TPS is simpler to implement, much more stable to train, and achieves superior video segmentation accuracy as compared with the state-of-the-art.
Linear Jamming Bandits: Sample-Efficient Learning for Non-Coherent Digital Jamming

It has been shown (Amuru et al. 2015) that online learning algorithms can be effectively used to select optimal physical layer parameters for jamming against digital modulation schemes without a priori knowledge of the victim's transmission strategy. However, this learning problem involves solving a multi-armed bandit problem with a mixed action space that can grow very large. As a result, convergence to the optimal jamming strategy can be slow, especially when the victim and jammer's symbols are not perfectly synchronized. In this work, we remedy the sample efficiency issues by introducing a linear bandit algorithm that accounts for inherent similarities between actions. Further, we propose context features which are well-suited for the statistical features of the non-coherent jamming problem and demonstrate significantly improved convergence behavior compared to the prior art. Additionally, we show how prior knowledge about the victim's transmissions can be seamlessly integrated into the learning framework. We finally discuss limitations in the asymptotic regime.
Multimodal Hydrostatic Actuators for Wearable Robots: A Preliminary Assessment of Mass-Saving and Energy-Efficiency Opportunities

Wearable robots are limited by their actuators performances because they must bear the weight of their own power system and energy source. This paper explores the idea of leveraging hybrid modes to meet multiple operating points with a lightweight and efficient system by using hydraulic valves to dynamically reconfigure the connections of a hydrostatic actuator. The analyzed opportunities consist in 1) switching between a highly geared power source or a fast power source, 2) dynamically connecting an energy accumulator and 3) using a locking mechanism for holding. Based on a knee exoskeleton case study analysis, results show that switching between gearing ratio can lead to a lighter and more efficient actuator. Also, results show that using an accumulator to provide a preload continuous force has great mass-saving potential, but does not reduce mass significantly if used as a power booster for short transients. Finally, using a locking valve can slightly reduce battery mass if the work cycle includes frequent stops. The operating principles of the proposed multimodal schemes are demonstrated with a one-DOF prototype.
Characterising acylindrical hyperbolicity via permutation actions

We characterise acylindrical hyperbolicity of a group in terms of properties of an action of the group on a set (without any extra structure). In particular, this applies to the action of the group on itself by left multiplication, as well as the action on a (full measure subset of the) Furstenberg-Poisson boundary.
White Matter Tracts are Point Clouds: Neuropsychological Score Prediction and Critical Region Localization via Geometric Deep Learning

White matter tract microstructure has been shown to influence neuropsychological scores of cognitive performance. However, prediction of these scores from white matter tract data has not been attempted. In this paper, we propose a deep-learning-based framework for neuropsychological score prediction using microstructure measurements estimated from diffusion magnetic resonance imaging (dMRI) tractography, focusing on predicting performance on a receptive vocabulary assessment task based on a critical fiber tract for language, the arcuate fasciculus (AF). We directly utilize information from all points in a fiber tract, without the need to average data along the fiber as is traditionally required by diffusion MRI tractometry methods. Specifically, we represent the AF as a point cloud with microstructure measurements at each point, enabling adoption of point-based neural networks. We improve prediction performance with the proposed Paired-Siamese Loss that utilizes information about differences between continuous neuropsychological scores. Finally, we propose a Critical Region Localization (CRL) algorithm to localize informative anatomical regions containing points with strong contributions to the prediction results. Our method is evaluated on data from 806 subjects from the Human Connectome Project dataset. Results demonstrate superior neuropsychological score prediction performance compared to baseline methods. We discover that critical regions in the AF are strikingly consistent across subjects, with the highest number of strongly contributing points located in frontal cortical regions (i.e., the rostral middle frontal, pars opercularis, and pars triangularis), which are strongly implicated as critical areas for language processes.
Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia

While neural networks demonstrate a remarkable ability to model linguistic content, capturing contextual information related to a speaker’s conversational role is an open area of research. In this work, we analyze the effect of speaker role on language use through the game of Mafia, in which participants are assigned either an honest or a deceptive role. In addition to building a framework to collect a dataset of Mafia game records, we demonstrate that there are differences in the language produced by players with different roles. We confirm that classification models are able to rank deceptive players as more suspicious than honest ones based only on their use of language. Furthermore, we show that training models on two auxiliary tasks outperforms a standard BERT-based text classification approach. We also present methods for using our trained models to identify features that distinguish between player roles, which could be used to assist players during the Mafia game.
A Survey of Recent Machine Learning Solutions for Ship Collision Avoidance and Mission Planning

: Machine Learning (ML) techniques have gained significant traction as a means of improving the autonomy of marine vehicles over the last few years. This article surveys the recent ML approaches utilised for ship collision avoidance (COLAV) and mission planning. To this end, following an overview of ever-expanding ML exploitation for maritime vehicles, essential topics in the mission planning of ships are outlined. Besides, notable papers with direct and indirect applications to the COLAV subject are technically reviewed and compared. Critiques, challenges, and future directions are also identified. The outcome clearly demonstrates the thriving research in this field, even though commercial marine ship incorporating machine intelligence that are able to perform autonomously under all operating conditions are still a long way off.
Are We There Yet? A Systematic Review of Visual Perception Knowledge for Visualization Recommendation Systems

— Visualization recommendation systems must select appropriate visual encodings, yet few ﬁndings from visual perception are typically applied within these systems. Knowledge bases provide one way to inscribe perception guidelines, but how do we systematically translate the perception literature into a structured format? We present a literature review across 59 papers that study how to rank effective visualizations based on user performance in various visual analysis tasks. We contribute a comprehensive schema to collate existing theoretical and experimental knowledge and summarize study outcomes at three levels: between encodings, within chart types, and between chart types. We demonstrate how the resulting survey dataset can be utilized to inform automated encoding decisions with three representative visualization recommendation systems. Based on our ﬁndings, we highlight new challenges and opportunities for the community in collating visualization design knowledge for a range of visualization recommendation scenarios.
O ct 2 00 5 P o S ( L A T 2 0 0 5 ) 1 8 6 Transport Coefficients of Gluon Plasma from Lattice QCD ∗

RHIC is now confirming the discovery of the “New State of Matte r”, whose properties are gradually revealed by experimental data. It is not the free gas st te, but well described by a fluid with very small viscosity. It is now highly desired to calcul ate the value of the viscosity from the fundamental theory, i.e., QCD just above the transition tem p rature. In this report we present our calculation of the transport coefficient of gluon system on 2 43×8 lattice in the quench approximation. Simulations are carried out in the range, 1 . 4≤ T/Tc ≤ 24. In the temperature region slightly above the transition, where the perturbative calculation i s not applicable, the shear viscosity( η) is smaller than typical hadron masses. The bulk viscosity is co n istent with zero within the range of error bars in 1.4≤ T/Tc ≤ 24. We compare our results with the perturbative calculatio ns n large T/Tc region. It is found that the lattice and perturbative result s are consistent with each other there. The ratioη/s is around 0.1−0.4 in T/Tc < 3 region and satisfies the KSS bound[14]. In order to estimate the contribution from high frequency part of the spectral function, we study the effects of a termρhigh proposed by Aarts and Resco[3]. It is found that until the thr s old mass becomes small, its effect is quite small, and that viscosity decreases as the threshold decreases. From these studies we think that although our result is obtai ned under an assumptions for the spectral function, it gives a reasonable estimation for η(= πdρ/dω at ω = 0), and qualitative results will not be changed when the accurate spectral funct ion is obtained.
Image Watermarking in DCT:an Embedding Strategy and Algorithm

Where should invisible image watermarks be embedded in DCT domain in order for the image watermarks to be invisible and robust? Though many papers in the literature agree that watermarks should be embedded in perceptually significant components,DC components are explicitly excluded from watermark embedding.In this paper,a new embedding strategy and an algorithm applying the strategy for watermarking in DCT domain is proposed.Based on a quantitative analysis on magnitude of DCT components,we argue that more robustness can be achieved if watermarks are embedded in DC components since DC components have much larger perceptual capacity than any AC components.Applying this embedding strategy,an adaptive watermarking algorithm incorporating the feature of visual masking of human vision system is presented.Experimental results demonstrate that the embedding strategy is effective and the invisible watermarks thus generated are very robust.
miR-138-5p promotes chicken granulosa cell apoptosis via targeting SIRT1

Abstract Granulosa cell (GC) apoptosis is the main trigger of follicular atresia. MicroRNAs (miRNAs) are 18–22 nt RNAs whose function is primarily determined by their extended seed region and are considered to be involved in the biological functions of follicular development, including follicular atresia, folliculogenesis, and oogenesis. MiR-138-5p is known to act on chicken GCs. In this study, we found that miR-138-5p was enriched in reproductive organs, such as the uterus and ovaries. To examine whether miR-138-5p could regulate the biological process of GCs, miR-138-5p was examined by transfection of cells with a mimic or inhibitor of miR-138-5p. Expression levels of caspase-3 and caspase-9 mRNA and protein were markedly increased or decreased after transfection of the mimic or inhibitor, respectively. Furthermore, following miR-138-5p inhibition, SIRT1, one of the target genes of miR-138-5p, was found to increase the mRNA, which is correlated with the increased levels of BCL2 expression, an anti-apoptotic gene in the chicken GCs. These results suggest that miR-138-5p promotes apoptosis in chicken GCs by targeting SIRT1.
Epithelial HNF4A shapes the intraepithelial lymphocyte compartment via direct regulation of immune signaling molecules

The nuclear receptor HNF4A is associated with susceptibility to inflammatory bowel disease. This study identifies the importance of HNF4A in controlling crosstalk between intestinal epithelial cells and intraepithelial lymphocytes. Epithelial HNF4A serves as a transcription factor for immune signaling molecules and promotes the development of natural intraepithelial lymphocytes.
UiO-66-NH2 nanocomposites incorporated cellulose acetate for forward osmosis membranes of high desalination performance

ABSTRACT In this paper, the hydrophilic UiO-66-NH2 nanomaterial was synthesized by the solvent-thermal method and characterized. Then, UiO-66-NH2 was introduced into the casting membrane solution of cellulose acetate (CA) forward osmosis (FO) membrane, and CA/UiO-66-NH2 forward osmosis membrane was prepared by the phase inversion method. The optimum preparation conditions of CA/UiO-66-NH2 mixed matrix membranes were determined as follows: the content of UiO-66-NH2 was 0.4 wt%, the coagulation bath temperature was 35°C, the mixing temperature was 50°C and the heat treatment temperature was 50°C. FTIR, SEM, water contact angle and AFM were carried out on CA/UiO-66-NH2 forward osmosis membrane prepared under the best preparation conditions. Compared to the CA forward osmosis membrane, the permeability and selectivity of the CA/UiO-66-NH2 membrane were improved. The water flux and reverse salt flux of the CA/UiO-66-NH2 forward osmosis membrane reached 52.32 L/(m2·h) and 2.43 g/(m2·h), respectively. The permeability selectivity of CA membranes and CA/UiO-66-NH2 membranes did not change much during 180 min, indicating that the two membranes had good long-term stability. This study shows a potential advantage of UiO-66-NH2 as additives for improvement in the desalination performance of forward osmosis membranes. GRAPHICAL ABSTRACT