Miracle-dz/UniFAR-FTUs huggingface dataset:
How Are Information Seeking, Scanning, and Processing Related to Beliefs About the Roles of Genetics and Behavior in Cancer Causation?

Understanding that cancer is caused by both genetic and behavioral risk factors is an important component of genomic literacy. However, a considerable percentage of people in the United States do not endorse such multifactorial beliefs. Using nationally representative cross-sectional data from the U.S. Health Information National Trends Survey (N = 2,529), we examined how information seeking, information scanning, and key information-processing characteristics were associated with endorsing a multifactorial model of cancer causation. Multifactorial beliefs about cancer were more common among respondents who engaged in cancer information scanning (p = .001), were motivated to process health information (p = .005), and reported a family history of cancer (p = .0002). Respondents who reported having previous negative information-seeking experiences had lower odds of endorsing multifactorial beliefs (p = .01). Multifactorial beliefs were not associated with cancer information seeking, trusting cancer information obtained from the Internet, trusting cancer information from a physician, self-efficacy for obtaining cancer information, numeracy, or being aware of direct-to-consumer genetic testing (ps > .05). Gaining additional understanding of how people access, process, and use health information will be critical for the continued development and dissemination of effective health communication interventions and for the further translation of genomics research to public health and clinical practice.
Stomatal Development and Pattern Controlled by a MAPKK Kinase

Stomata are epidermal structures that modulate gas exchange between a plant and its environment. During development, stomata are specified and positioned nonrandomly by the integration of asymmetric cell divisions and intercellular signaling. The Arabidopsis mitogen-activated protein kinase kinase kinase gene, YODA, acts as part of a molecular switch controlling cell identities in the epidermis. Null mutations in YODA lead to excess stomata, whereas constitutive activation of YODA eliminated stomata. Transcriptome analysis of seedlings with altered YODA activity was used to identify potential stomatal regulatory genes. A putative transcription factor from this set was shown to regulate the developmental behavior of stomatal precursors.
CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms

How to optimally dispatch orders to vehicles and how to trade off between immediate and future returns are fundamental questions for a typical ride-hailing platform. We model ride-hailing as a large-scale parallel ranking problem and study the joint decision-making task of order dispatching and fleet management in online ride-hailing platforms. This task brings unique challenges in the following four aspects. First, to facilitate a huge number of vehicles to act and learn efficiently and robustly, we treat each region cell as an agent and build a multi-agent reinforcement learning framework. Second, to coordinate the agents from different regions to achieve long-term benefits, we leverage the geographical hierarchy of the region grids to perform hierarchical reinforcement learning. Third, to deal with the heterogeneous and variant action space for joint order dispatching and fleet management, we design the action as the ranking weight vector to rank and select the specific order or the fleet management destination in a unified formulation. Fourth, to achieve the multi-scale ride-hailing platform, we conduct the decision-making process in a hierarchical way where a multi-head attention mechanism is utilized to incorporate the impacts of neighbor agents and capture the key agent in each scale. The whole novel framework is named as CoRide. Extensive experiments based on multiple cities real-world data as well as analytic synthetic data demonstrate that CoRide provides superior performance in terms of platform revenue and user experience in the task of city-wide hybrid order dispatching and fleet management over strong baselines.
Multi-User Detection Based on Expectation Propagation for the Non-Coherent SIMO Multiple Access Channel

We consider the non-coherent single-input multiple-output (SIMO) multiple access channel with general signaling under spatially correlated Rayleigh block fading. We propose a novel soft-output multi-user detector that computes an approximate marginal posterior of each transmitted signal using only the knowledge about the channel distribution. Our detector is based on expectation propagation (EP) approximate inference and has polynomial complexity in the number of users, number of receive antennas and channel coherence time. We also propose two simplifications of this detector with reduced complexity. With Grassmannian signaling, the proposed detectors outperform a state-of-the-art non-coherent detector with projection-based interference mitigation. With pilot-assisted signaling, the EP detector outperforms, in terms of symbol error rate, some conventional coherent pilot-based detectors, including a sphere decoder and a joint channel estimation–data detection scheme. Our EP-based detectors produce accurate approximates of the true posterior leading to high achievable sum-rates. The gains of these detectors are further observed in terms of the bit error rate when using their soft outputs for a turbo channel decoder.
Saliency Prediction in the Deep Learning Era: Successes, Limitations, and Future Challenges

Visual saliency models have enjoyed a big leap in performance in recent years, thanks to advances in deep learning and large scale annotated data. Despite enormous effort and huge breakthroughs, however, models still fall short in reaching human-level accuracy. In this work, I explore the landscape of the field emphasizing on new deep saliency models, benchmarks, and datasets. A large number of image and video saliency models are reviewed and compared over two image benchmarks and two large scale video datasets. Further, I identify factors that contribute to the gap between models and humans and discuss remaining issues that need to be addressed to build the next generation of more powerful saliency models. Some specific questions that are addressed include: in what ways current models fail, how to remedy them, what can be learned from cognitive studies of attention, how explicit saliency judgments relate to fixations, how to conduct fair model comparison, and what are the emerging applications of saliency models.
Algorithmic and geometric aspects of data depth with focus on $\beta$-skeleton depth

The statistical rank tests play important roles in univariate non-parametric data analysis. If one attempts to generalize the rank tests to a multivariate case, the problem of defining a multivariate order will occur. It is not clear how to define a multivariate order or statistical rank in a meaningful way. One approach to overcome this problem is to use the notion of data depth which measures the centrality of a point with respect to a given data set. In other words, a data depth can be applied to indicate how deep a point is located with respect to a given data set. Using data depth, a multivariate order can be defined by ordering the data points according to their depth values. Various notions of data depth have been introduced over the last decades. In this thesis, we discuss three depth functions: two well-known depth functions halfspace depth and simplicial depth, and one recently defined depth function named as β-skeleton depth, β ≥ 1. The β-skeleton depth is equivalent to the previously defined spherical depth and lens depth when β = 1 and β = 2, respectively. Our main focus in this thesis is to explore the geometric and algorithmic aspects of β-skeleton depth. For the geometric part, we study
LAW: Learning to Auto Weight

Example weighting algorithm is an effective solution to the training bias problem. However, typical methods are usually limited to human knowledge and require laborious tuning of hyperparameters. In this study, we propose a novel example weighting framework called Learning to Auto Weight (LAW), which can learn weighting policy from data adaptively based on reinforcement learning (RL). To shrink the huge searching space in a complete training process, we divide the training procedure consisting of numerous iterations into a small number of stages, and then search a low-deformational continuous vector as action, which determines the weight of each sample. To make training more efficient, we make an innovative design of the reward to remove randomness during the RL process. Experimental results demonstrate the superiority of weighting policy explored by LAW over standard training pipeline. Especially, compared with baselines, LAW can find a better weighting schedule which achieves higher accuracy in the origin CIFAR dataset, and over 10% higher in accuracy on the contaminated CIFAR dataset with 30% label noises. Our code will be released soon.
Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning (ML) offers a wealth of techniques to extract information from data that can be translated into knowledge about the underlying fluid mechanics. Moreover, ML algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of ML for fluid mechanics. We outline fundamental ML methodologies and discuss their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experiments, and simulations. ML provides a powerful information-processing framework that can augment, and possibly even transform, current lines of fluid mechanics research and industrial applications.
Generative Latent Flow: A Framework for Non-adversarial Image Generation

Generative Adversarial Networks (GANs) have been shown to outperform non-adversarial generative models in terms of the image generation quality by a large margin. Recently, researchers have looked into improving non-adversarial alternatives that can close the gap of generation quality while avoiding some common issues of GANs, such as unstable training and mode collapse. Examples in this direction include Two-stage VAE and Generative Latent Nearest Neighbors. However, a major drawback of these models is that they are slow to train, and in particular, they require two training stages. To address this, we propose Generative Latent Flow (GLF), which uses an auto-encoder to learn the mapping to and from the latent space, and an invertible flow to map the distribution in the latent space to simple i.i. d noise. The advantages of our method include a simple conceptual framework, single stage training and fast convergence. Quantitatively, the generation quality of our model significantly outperforms that of VAEs, and is competitive with GANs' benchmark on commonly used datasets.
An Unconditionally Stable First-Order Constraint Solver for Multibody Systems

This article describes an absolutely stable, first-order constraint solverfor multi-rigid body systems that calculates (predicts) constraint forces for typical bilateral and unilateral constraints, contact constraints with friction, and many other constraint types. Redundant constraints do not pose numerical problems or require regularization. Coulomb friction for contact is modeled using a true friction cone, rather than a linearized approximation. The computational expense of the solver is dependent upon the types of constraints present in the input. The hardest (in a computational complexity sense) inputs are reducible to solving convex optimization problems, i.e., polynomial time solvable. The simplest inputs require only solving a linear system. The solver is L-stable, which will imply that the forces due to constraints induce no computational stiffness into the multi-body dynamics differential equations. This approach is targeted to multibodies simulated with coarse accuracy, subject to computational stiffness arising from constraints, and where the number of constraint equations is not large compared to the number of multibody position and velocity state variables. For such applications, the approach should prove far faster than using other implicit integration approaches. I assess the approach on some fundamental multibody dynamics problems.
Complexity estimates for triangular hierarchical matrix algorithms

Triangular factorizations are an important tool for solving integral equations and partial differential equations with hierarchical matrices (H-matrices). Experiments show that using an H-matrix LR factorization to solve a system of linear questions is superior to direct inversion both with respect to accuracy and efficiency, but so far theoretical estimates quantifying these advantages were missing. Due to a lack of symmetry in H-matrix algorithms, we cannot hope to prove that the LR factorization takes one third of the operations of the inversion or the matrix multiplication, as in standard linear algebra. We can, however, prove that the LR factorization together with two other operations of similar complexity, i.e., the inversion and multiplication of triangular matrices, requires not more operations than the matrix multiplication. We can complete the estimates by proving an improved upper bound for the complexity of the matrix multiplication, designed for recently introduced variants of classical H-matrices.
Average Individual Fairness: Algorithms, Generalization and Experiments

We propose a new family of fairness definitions for classification problems that combine some of the best properties of both statistical and individual notions of fairness. We posit not only a distribution over individuals, but also a distribution over (or collection of) classification tasks. We then ask that standard statistics (such as error or false positive/negative rates) be (approximately) equalized across individuals, where the rate is defined as an expectation over the classification tasks. Because we are no longer averaging over coarse groups (such as race or gender), this is a semantically meaningful individual-level constraint. Given a sample of individuals and classification problems, we design an oracle-efficient algorithm (i.e. one that is given access to any standard, fairness-free learning heuristic) for the fair empirical risk minimization task. We also show that given sufficiently many samples, the ERM solution generalizes in two directions: both to new individuals, and to new classification tasks, drawn from their corresponding distributions. Finally we implement our algorithm and empirically verify its effectiveness.
Influence of nonmagnetic dielectric spacers on the spin-wave response of one-dimensional planar magnonic crystals

The one-dimensional planar magnonic crystals are usually fabricated as a sequence of stripes intentionally or accidentally separated by non-magnetic spacers. The influence of spacers on shaping the spin wave spectra is complex and still not completely clarified. We performed the detailed numerical studies of the one-dimensional single- and bi-component magnonic crystals comprised of a periodic array of thin ferromagnetic stripes separated by non-magnetic spacers. We showed that the dynamic dipolar interactions between the stripes mediated by non-magnetic spacer, even ultra-narrow, significantly shift up the frequency of the ferromagnetic resonance and simultaneously reduce the spin wave group velocity, which is manifested by the flattening of the magnonic band. We attributed these changes in the spectra to the modifications of dipolar pinning and shape anisotropy both dependent on the width of the spacers and the thickness of the stripes, as well as to the dynamical magnetic volume charges formed due to inhomogeneous spin wave amplitude.
Mitochondria in the striatum of subjects with schizophrenia: Relationship to treatment response

Schizophrenia (SZ) is a severe mental illness with neuropathology in many regions, including the striatum. The typical symptoms of this disease are psychosis (such as hallucinations and delusions), cognitive impairments, and the deficit syndrome. Not all patients respond to treatment and, in those who do, only psychotic symptoms are improved. Imaging studies support a biological distinction between treatment response and resistance, but postmortem examinations of this issue are rare. This study tests the hypotheses that abnormalities in mitochondria, the energy producing organelles in the cell, may correlate with treatment response. Postmortem striatal tissue was obtained from the Maryland Brain Collection. The density of mitochondria (in various neuropil compartments) and the number of mitochondria per synapse (all types of synapses combined) were tallied using electron microscopy and stereology in striatum from SZ subjects (rated treatment responsive or not) and normal controls. The number of mitochondria per synapse was significantly different among groups for both the caudate nucleus (P < 0.025) and putamen (P < 0.002). Compared to controls, treatment‐responsive SZ subjects had a 37–43% decrease in the number of mitochondria per synapse in the caudate nucleus and putamen. In the putamen, treatment‐responsive subjects also had decreases in this measure compared to treatment‐resistant subjects (34%). Our results provide further support for a biological distinction between treatment response and treatment resistance in SZ. Because treatment responders have fewer mitochondria per synapse than controls, although the treatment‐resistant subjects have similar results to that of controls, fewer mitochondria per synapse may be related to treatment response. Synapse, 2010. © 2010 Wiley‐Liss, Inc.
Group-theoretical classification of superconducting states of strontium ruthenate

The possible superconducting states of strontium ruthenate (Sr$_2$RuO$_4$) are organized into irreducible representations of the point group $D_{4h}$, with a special emphasis on nodes occurring within the superconducting gap. Our analysis covers the cases with and without spin-orbit coupling and takes into account the possibility of inter-orbital pairing within a three-band, tight-binding description of Sr$_2$RuO$_4$. No dynamical treatment if performed: we are confining ourselves to a group-theoretical analysis. The case of uniaxial deformations, under which the point group symmetry is reduced to $D_{2h}$, is also covered. It turns out that nodal lines, in particular equatorial nodal lines, occur in most representations. We also highlight some results specific to multiorbital superconductivity. Among other things, we find that odd inter-orbital pairing allows to combine singlet and triplet superconductivity whithin the same irreducible representation, that pure inter-orbital superconductivity leads to nodal surfaces and that the notion of nodes imposed by symmetry is not clearly defined.
Nonreciprocal and collimated surface plasmons in drift-biased graphene metasurfaces

We explore the unusual non-reciprocal and diffraction-less properties of surface plasmon polaritons propagating in drift-biased graphene-based metasurfaces. We show that applying a drift-current on a graphene sheet leads to extremely asymmetric in-plane modal dispersions from terahertz to infrared frequencies, associated with plasmons with low-loss (high-loss and ultra-high confinement) traveling along (against) the bias. Strikingly, truly unidirectional wave propagation is prevented by the intrinsic nonlocal response of a graphene, a mechanism that shapes the energy flow over the surface. We also show that highly-directive hyperbolic plasmons completely immune to backscattering propagate obliquely along the drift in nanostructured graphene. Finally, we discuss how spin-orbit interactions can be exploited in this platform to efficiently launch collimated plasmons along a single direction while maintaining giant non-reciprocal responses. Our findings open a new paradigm to excite, collimate, steer, and process surface plasmons over a broad frequency band.
The complex case of MACS J0717.5+3745 and its extended filament: intra-cluster light, galaxy luminosity function, and galaxy orientations

Context. The properties of galaxies are known to be affected by their environment, but although galaxies in clusters and groups have been quite thoroughly investigated, little is known about galaxies belonging to filaments of the cosmic web, or about the properties of the filaments themselves. Aims. Here we investigate the properties of the rich cluster MACS J0717.5+3745 and its extended filament by analyzing the distribution and fractions of intra-cluster light (ICL) in its core and by trying to detect intra-filament light (IFL) in the filament. We analyze the galaxy luminosity function (GLF) of the cluster core and of the filament. We also study the orientations of galaxies in the filament to better constrain the filament properties. Methods. This work is based on Hubble Space Telescope (HST) archive data, both from the Hubble Frontier Fields in the F435W, F606W, F814W, and F105W bands, and from a mosaic of images in the F606W and F814W bands. The spatial distribution of the ICL was determined with our new wavelet-based software, DAWIS. The GLFs were extracted in the F606W and F814W bands, with a statistical subtraction of the background, and fit with Schechter functions. The galaxy orientations in the filaments were estimated with SExtractor after correction for the point spread function. Results. We detect a large amount of ICL in the cluster core, but no IFL in the cosmic filament. The fraction of ICL in the core peaks in the F606W filter before decreasing with wavelength. Though relatively noisy, the GLFs in the filament are notably different from those of field galaxies, with a flatter faint end slope and an excess of bright galaxies. We do not detect a significant alignment of the galaxies in the filament region analyzed.
Performance of insulators treated with semiconductive silicone rubber coating during fog and polluted conditions

Ice accumulation on insulator surface imposes serious problems on power systems. One approach is to develop a hydrophobic coating with semiconducting characteristic resulting in heat generation, which is only coated on the bottom side. This paper focuses on the performance of the insulators treated with semiconductive coating during fog and pollution conditions. Three kinds of samples were tested, including insulator without any coating, insulator coated with normal RTV coatings and the one with semiconductive coatings. The artificial pollution tests were carried out in a fog chamber. The leakage current and the flashover voltage during the fog and polluted conditions were investigated. FEM calculation was also used to help understand the anti-contaminate performance. Test results indicate that the pollution flashover voltage of the semiconductive ones is almost twice of the pure ones, but a little lower than the RTV ones. The primary causes of high pollution flashover voltage are the joint action of the hydrophobicity of the coating and the drying effect generated by the leakage current.