Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyperspectral Anomaly Detection by Graph Pixel Selection

Hyperspectral anomaly detection (AD) is an important problem in remote sensing field. It can make full use of the spectral differences to discover certain potential interesting regions without any target priors. Traditional Mahalanobis-distance-based anomaly detectors assume the background spectrum distribution conforms to a Gaussian distribution. However, this and other similar distributions may not be satisfied for the real hyperspectral images. Moreover, the background statistics are susceptible to contamination of anomaly targets which will lead to a high false-positive rate. To address these intrinsic problems, this paper proposes a novel AD method based on the graph theory. We first construct a vertex- and edge-weighted graph and then utilize a pixel selection process to locate the anomaly targets. Two contributions are claimed in this paper: 1) no background distributions are required which makes the method more adaptive and 2) both the vertex and edge weights are considered which enables a more accurate detection performance and better robustness to noise. Intensive experiments on the simulated and real hyperspectral images demonstrate that the proposed method outperforms other benchmark competitors. In addition, the robustness of the proposed method has been validated by using various window sizes. This experimental result also demonstrates the valuable characteristic of less computational complexity and less parameter tuning for real applications.
Discriminant Kernel Assignment for Image Coding

This paper proposes discriminant kernel assignment (DKA) in the bag-of-features framework for image representation. DKA slightly modifies existing kernel assignment to learn width-variant Gaussian kernel functions to perform discriminant local feature assignment. When directly applying gradient-descent method to solve DKA, the optimization may contain multiple time-consuming reassignment implementations in iterations. Accordingly, we introduce a more practical way to locally linearize the DKA objective and the difficult task is cast as a sequence of easier ones. Since DKA only focuses on the feature assignment part, it seamlessly collaborates with other discriminative learning approaches, e.g., discriminant dictionary learning or multiple kernel learning, for even better performances. Experimental evaluations on multiple benchmark datasets verify that DKA outperforms other image assignment approaches and exhibits significant efficiency in feature coding.
Identification of IL6R and Chromosome 11q13.5 as Risk Loci for Asthma

MA Ferreira, MC Matheson, DL Duffy. Lancet. 2011;378(9795):1006–1014 To identify novel genetic variations imputing asthma risk. Pediatric and adult Australians of European descent consisting of 2669 asthmatic subjects (28% diagnosed by clinical examination, 54% childhood onset, 59% atopic) and 4528 controls (40% with unknown asthma status) produced candidate asthma genetic variations. Meta-analysis of 12 475 physician-diagnosed asthmatic subjects and 19 967 controls provided a prioritization cohort. Four additional individual cohorts totaling 3322 asthmatic subjects and 22 036 controls provided replication. Microarray-derived genotypes of Australian asthmatic subjects and controls underwent a …
Optoelectronically innervated soft prosthetic hand via stretchable optical waveguides

Stretchable optical waveguides can sense curvature, elongation, and force in a prosthetic hand. Because of their continuous and natural motion, fluidically powered soft actuators have shown potential in a range of robotic applications, including prosthetics and orthotics. Despite these advantages, robots using these actuators require stretchable sensors that can be embedded in their bodies for sophisticated functions. Presently, stretchable sensors usually rely on the electrical properties of materials and composites for measuring a signal; many of these sensors suffer from hysteresis, fabrication complexity, chemical safety and environmental instability, and material incompatibility with soft actuators. Many of these issues are solved if the optical properties of materials are used for signal transduction. We report the use of stretchable optical waveguides for strain sensing in a prosthetic hand. These optoelectronic strain sensors are easy to fabricate, are chemically inert, and demonstrate low hysteresis and high precision in their output signals. As a demonstration of their potential, the photonic strain sensors were used as curvature, elongation, and force sensors integrated into a fiber-reinforced soft prosthetic hand. The optoelectronically innervated prosthetic hand was used to conduct various active sensation experiments inspired by the capabilities of a real hand. Our final demonstration used the prosthesis to feel the shape and softness of three tomatoes and select the ripe one.
Importance Weighted Import Vector Machine for Unsupervised Domain Adaptation

In real-world applications, the assumption of independent and identical distribution is no longer consistent. To alleviate the significant mismatch between source and target domains, importance weighting import vector machine, which is an adaptive classifier, is proposed. This adaptive probabilistic classification method, which is sparse and computationally efficient, can be used for unsupervised domain adaptation (DA). The effectiveness of the proposed approach is demonstrated via a toy problem, and a real-world cross-domain object recognition task. Even though the sparseness, the proposed method outperforms the state-of-the-art in both unsupervised and semisupervised DA scenarios. We also introduce a reliable importance weighted cross validation (RIWCV), which is an improvement of importance weighted cross validation, for parameter and model selection. The RIWCV avoid falling down in local minimum, by selecting a more reliable combination of the parameters instead of the best parameters.
New soft robots really suck: Vacuum-powered systems empower diverse capabilities

A foam-based, vacuum-powered actuator enables a multifunctional soft robotic system. We introduce a vacuum-powered soft pneumatic actuator (V-SPA) that leverages a single, shared vacuum power supply and enables complex soft robotic systems with multiple degrees of freedom (DoFs) and diverse functions. In addition to actuation, other utilities enabled by vacuum pressure include gripping and stiffening through granular media jamming, as well as direct suction adhesion to smooth surfaces, for manipulation or vertical fixation. We investigate the performance of the new actuator through direct characterization of a 3-DoF, plug-and-play V-SPA Module built from multiple V-SPAs and demonstrate the integration of different vacuum-enabled capabilities with a continuum-style robot platform outfitted with modular peripheral mechanisms. We show that these different vacuum-powered modules can be combined to achieve a variety of tasks—including multimodal locomotion, object manipulation, and stiffness tuning—to illustrate the utility and viability of vacuum as a singular alternative power source for soft pneumatic robots and not just a peripheral feature in itself. Our results highlight the effectiveness of V-SPAs in providing core soft robot capabilities and facilitating the consolidation of previously disparate subsystems for actuation and various specialized tasks, conducive to improving the compact design efficiency of larger, more complex multifunctional soft robotic systems.
Design principles of a human mimetic humanoid: Humanoid platform to study human intelligence and internal body system

Human mimetic humanoids incorporate anatomical fidelity to the human musculoskeletal structure. Many systems and mechanisms in the human body are not fully understood, such as the principles of muscle control, the sensory nervous system that connects the brain and the body, learning in the brain, and the human walking motion. To address this knowledge deficit, we propose a human mimetic humanoid with an unprecedented degree of anatomical fidelity to the human musculoskeletal structure. The fundamental concept underlying our design is to consider the human mechanism, which contrasts with the conventional engineering approach used in the design of existing humanoids. We believe that the proposed human mimetic humanoid can be used to provide new opportunities in science, for instance, to quantitatively analyze the internal data of a human body in movement. We describe the principles and development of human mimetic humanoids, Kenshiro and Kengoro, and compare their anatomical fidelity with humans in terms of body proportions, skeletal structures, muscle arrangement, and joint performance. To demonstrate the potential of human mimetic humanoids, Kenshiro and Kengoro performed several typical human motions.
pathFinder: A Static Network Analysis Tool for Pharmacological Analysis of Signal Transduction Pathways

The study of signal transduction is becoming a de facto part of the analysis of gene expression and protein profiling techniques. Many online tools are used to cluster genes in various ways or to assign gene products to signal transduction pathways. Among these, pathFinder is a unique tool that can find signal transduction pathways between first, second, or nth messengers and their targets within the cell. pathFinder can identify qualitatively all possible signal transduction pathways connecting any starting component and target within a database of two-component pathways (directional dyads). One or more intermediate pathway components can be excluded to simulate the use of pharmacological inhibitors or genetic deletion (knockout). Missing elements in a pathway connecting the activator or initiator and target can also be inferred from a null pathway result. The value of this static network analysis tool is illustrated by the predication from pathFinder analysis of a novel cyclic AMP–dependent, protein kinase A–independent signaling pathway in neuroendocrine cells, which has been experimentally confirmed.
Application of Fluorescence Resonance Energy Transfer and Magnetic Twisting Cytometry to Quantify Mechanochemical Signaling Activities in a Living Cell

Mechanotransduction is the process by which living cells sense mechanical forces and then convert them into biochemical signaling. Recently, we showed that mechanical stress is transduced from the cell surface to remote cytoplasmic sites within 0.3 seconds, which is at least 40 to 50 times faster than soluble factor–induced signal transduction, and the sites of mechanotransduction colocalize with mechanical stress–induced microtubule displacements. These results suggest that mechanotransduction employs mechanisms different from those of soluble factor–induced signal transduction. Here, we describe a protocol that utilizes fluorescence resonance energy transfer (FRET) and a magnetic twisting cytometry (MTC) device to capture rapid mechanochemical signaling activities in living cells.
An Enhanced Fuzzy Linguistic Term Generation and Representation for time series forecasting

This paper introduces an enhancement to linguistic forecast representation using Triangular Fuzzy Numbers (TFNs) called Enhanced Linguistic Generation and Representation Approach (ElinGRA). Since there is always an error margin in the predictions, there is a need to define error bounds in the forecast. The interval of the proposed presentation is generated from a Fuzzy logic based Lower and Upper Bound Estimator (FLUBE) by getting the models of forecast errors. Thus, instead of a classical statistical approaches, the level of uncertainty associated with the point forecasts will be defined within the FLUBE bounds and these bound can be used for defining fuzzy linguistic terms for the forecasts. Here, ElinGRA is proposed to generate triangular fuzzy numbers (TFNs) for the predictions. In addition to opportunity to handle the forecast as linguistic terms which will increase the interpretability, ElinGRA improved forecast accuracy of constructed TFNs by adding an extra correction term. The results of the experiments, which are conducted on two data sets, show the benefit of using ElinGRA to represent the uncertainty and the quality of the forecast.
NAADP: A Universal Ca2+ Trigger

NAADP elicits an initial release of calcium, which is subsequently amplified through the action of other calcium messengers. Cells possess multiple calcium ion (Ca2+) stores and multiple messenger molecules to mobilize them. These include d-myo-inositol 1,4,5-trisphosphate (IP3), cyclic adenosine diphosphoribose (cADPR), and the most recently identified Ca2+-mobilizing messenger, nicotinic acid adenine dinucleotide phosphate (NAADP), which acts on a wide spectrum of cells, from plant cells to mammalian cells. Accumulating evidence indicates that NAADP targets both acidic (lysosome-like) Ca2+ stores and endoplasmic reticular stores. Recent studies in invertebrate and mammalian cells suggest that NAADP provides an initiating Ca2+ signal, which is amplified by cADPR- or IP3-dependent mechanisms (or both) through Ca2+-induced Ca2+ release. Diverse stimuli activate a rapid rise of endogenous NAADP concentration, resulting in severalfold increases of NAADP over basal values within seconds. The enzyme CD38 can catalyze both the synthesis and hydrolysis of NAADP, making it ideal for effecting the rapid metabolism of NAADP. The crystal structure of CD38 and the structures of its various substrate complexes have now been determined, clarifying the mechanism of its multifunctional catalysis. We anticipate that these advances will lead to the unmasking of all the key components of the Ca2+ signaling pathway mediated by NAADP. Intracellular signal transduction is fundamental to the ability of multicellular organisms to convert incoming signals into meaningful cellular responses. Calcium ions (Ca2+) released from intracellular stores transduce numerous physiological functions, from fertilization (at the beginning of life) to muscle contraction, induction of cell differentiation and proliferation, and finally apoptotic cell death. The free cytosolic Ca2+ concentration is much lower than that in the extracellular space or in intracellular stores, such as the endoplasmic reticulum (ER). Moreover, the free cytosolic Ca2+ concentration is tightly controlled through the activity of ion carrier proteins and adenosine 5′-triphosphate (ATP)–driven Ca2+ pumps, and through the open probability of Ca2+ channels. The latter is modulated by various mechanisms, including regulation by small signaling molecules generated in response to incoming extracellular signals, which are called second messengers. NAADP is the most recently identified and also the most potent messenger molecule known to mobilize Ca2+ stores in cells. Here, we summarize recent advances concerning NAADP, including the Ca2+ stores it targets, the receptor(s) with which it interacts, its Ca2+-signaling functions, and its metabolism, thereby unraveling a universal cellular signaling pathway.
Untangling the Complex Web of IL-4– and IL-13–Mediated Signaling Pathways

Understanding how IL-4 and IL-13 signaling differ could result in the development of better anti-inflammatory therapies. Unraveling the exact signaling events mediating the distinct functions of the T cell–derived cytokines interleukin-4 (IL-4) and IL-13 has been challenging because they are structurally similar and share a functional signaling receptor chain. A study now proposes a potential molecular mechanism to explain the functional differences between IL-4 and IL-13 that involves the ability of IL-4, but not IL-13, to effectively activate the insulin receptor substrate–2 (IRS-2) signaling cascade through binding to its receptor. A better understanding of the interactions of IL-4 and IL-13 with their cognate receptors may facilitate the development of therapies without unintended side effects.
Hispanic Women and Physical Activity: A Community Approach

Older Hispanic women are at higher risk for chronic debilitating health problems related to higher rates of inactivity and obesity when compared to other ethnic groups. The purpose of this article was to review the literature on the factors which effect levels of physical activity in older Hispanic women. A literature search was conducted using the terms Hispanic or Latina, physical activity, barriers and benefits, self-efficacy, stages of exercise change, and health promotion behaviors. Forty-two studies between 1988 and mid 2007 were selected. Research methods included qualitative, quantitative, interventional, experimental, and cross sectional. There is some research available to guide nurses in assessment of unique factors that may impact the development of culturally appropriate nursing interventions that increase a women’s level of physical activity and promote health and well-being. Practical take home points are presented. Areas for future nursing research are identified.
A New Differential Evolution Algorithm for Minimax Optimization in Robust Design

Minimax optimization, which is actively involved in numerous robust design problems, aims at pursuing the solutions with best worst-case performances. Although considerable research has been devoted to the development of minimax optimization algorithms, there still exist several fundamental limitations for existing approaches, e.g., restriction on problem types, excessively high computational cost, and low optimization efficiency. To address these issues, a minimax differential evolution algorithm is proposed in this paper. First, a novel bottom-boosting scheme enables the algorithm to identify the promising solutions in a reliable yet efficient manner. After that, a partial-regeneration strategy together with a new mutation operator contribute to an in-depth exploration over solution space. Finally, a proper integration of these newly proposed mechanisms leads to an algorithmic structure that can appropriately handle various types of problems. Empirical comparison with seven famous methods demonstrates the statistical superiority of the proposed algorithm. Successful applications in two open problems of robust design further validate the effectiveness of the new approach.
Semantic Feature Learning for Heterogeneous Multitask Classification via Non-Negative Matrix Factorization

Multitask learning (MTL) aims to learn multiple related tasks simultaneously instead of separately to improve the generalization performance of each task. Most existing MTL methods assumed that the multiple tasks to be learned have the same feature representation. However, this assumption may not hold for many real-world applications. In this paper, we study the problem of MTL with heterogeneous features for each task. To address this problem, we first construct an integrated graph of a set of bipartite graphs to build a connection among different tasks. We then propose a non-negative matrix factorization-based multitask method (MTNMF) to learn a common semantic feature space underlying different heterogeneous feature spaces of each task. Moreover, an improved version of MTNMF (IMTNMF) is proposed, in which we do not need to construct the correlation matrix between input features and class labels, avoiding the information loss. Finally, based on the common semantic features and original heterogeneous features, we model the heterogenous MTL problem as a multitask multiview learning (MTMVL) problem. In this way, a number of existing MTMVL methods can be applied to solve the problem effectively. Extensive experiments on three real-world problems demonstrate the effectiveness of our proposed methods, and the improved version IMTNMF can gain about 2% average accuracy improvement compared with MTNMF.
Effect of α-tocopherol on pro-oxidant and antioxidant enzyme status in radiation-treated oral squamous cell carcinoma

Objectives: The relationships between α-tocopherol, pro-oxidant and antioxidant enzyme status, and radiation toxicity were studied in stage II, III, and IVA oral squamous cell carcinoma patients. The low levels of malondialdehyde and increased activities of antioxidant enzymes were correlated with decreased oxidative stress by α-tocopherol in oral cancer patients treated with radiotherapy. The objective of the present study was to evaluate the effect of α-tocopherol on oxidant-antioxidant enzyme status in oral squamous cell carcinoma patients treated with radiotherapy. Materials and Methods: The study included three groups with histologically confirmed oral squamous cell carcinoma patients (untreated), and they were further divided into two groups, viz., one consisting of patients who underwent radiotherapy alone (radiotherapy was given at the dosage of 6000 cGy in five fractions per week for a period of 6 weeks); and the other group treated with radiotherapy plus α-tocopherol supplementation (α-tocopherol was supplemented at a dosage of 400 IU/day) for the entire period of radiotherapy. Results: A significant decrease ( P P Conclusion: It was seen that α-tocopherol played a role in protecting against the damage caused by irradiation in oral squamous cell carcinoma patients treated with radiotherapy, by enhancing the antioxidant enzyme status and reducing the pro-oxidant status.
Multiview Clustering Based on Non-Negative Matrix Factorization and Pairwise Measurements

As we all know, multiview clustering has become a hot topic in machine learning and pattern recognition. Non-negative matrix factorization (NMF) has been one popular tool in multiview clustering due to its competitiveness and interpretation. However, the existing multiview clustering methods based on NMF only consider the similarity of intra-view, while neglecting the similarity of inter-view. In this paper, we propose a novel multiview clustering algorithm, named multiview clustering based on NMF and pairwise measurements, which incorporates pairwise co-regularization and manifold regularization with NMF. In the proposed algorithm, we consider the similarity of the inter-view via pairwise co-regularization to obtain the more compact representation of multiview data space. We can also obtain the part-based representation by NMF and preserve the locally geometrical structure of the data space by utilizing the manifold regularization. Furthermore, we give the theoretical proof that the objective function of the proposed algorithm is convergent for multiview clustering. Experimental results show that the proposed algorithm outperforms the state-of-the-arts for multiview clustering.
Multitask Feature Selection by Graph-Clustered Feature Sharing

Multitask feature selection (MTFS) methods have become more important for many real world applications, especially in a high-dimensional setting. The most widely used assumption is that all tasks share the same features, and the $l_{2,1}$ regularization method is usually applied. However, this assumption may not hold when the correlations among tasks are not obvious. Learning with unrelated tasks together may result in negative transfer and degrade the performance. In this paper, we present a flexible MTFS by graph-clustered feature sharing approach. To avoid the above limitation, we adopt a graph to represent the relevance among tasks instead of adopting a hard task set partition. Furthermore, we propose a graph-guided regularization approach such that the sparsity of the solution can be achieved on both the task level and the feature level, and a variant of the smooth proximal gradient method is developed to solve the corresponding optimization problem. An evaluation of the proposed method on multitask regression and multitask binary classification problem has been performed. Extensive experiments on synthetic datasets and real-world datasets demonstrate the effectiveness of the proposed approach to capture task structure.