Miracle-dz/UniFAR-FTUs huggingface dataset:
Stimulus Dependent Dynamic Reorganization of the Human Face Processing Network

Using the "face inversion effect", a hallmark of face perception, we examined network mechanisms supporting face representation by tracking functional magnetic resonance imaging (fMRI) stimulus-dependent dynamic functional connectivity within and between brain networks associated with the processing of upright and inverted faces. We developed a novel approach adapting the general linear model (GLM) framework classically used for univariate fMRI analysis to capture stimulus-dependent fMRI dynamic connectivity of the face network. We show that under the face inversion manipulation, the face and non-face networks have complementary roles that are evident in their stimulus-dependent dynamic connectivity patterns as assessed by network decomposition into components or communities. Moreover, we show that connectivity patterns are associated with the behavioral face inversion effect. Thus, we establish "a network-level signature" of the face inversion effect and demonstrate how a simple physical transformation of the face stimulus induces a dramatic functional reorganization across related brain networks. Finally, we suggest that the dynamic GLM network analysis approach, developed here for the face network, provides a general framework for modeling the dynamics of blocked stimulus-dependent connectivity experimental designs and hence can be applied to a host of neuroimaging studies.
Group Reidentification with Multigrained Matching and Integration

The task of reidentifying groups of people under different camera views is an important yet less-studied problem. Group reidentification (Re-ID) is a very challenging task since it is not only adversely affected by common issues in traditional single-object Re-ID problems, such as viewpoint and human pose variations, but also suffers from changes in group layout and group membership. In this paper, we propose a novel concept of group granularity by characterizing a group image by multigrained objects: individual people and subgroups of two and three people within a group. To achieve robust group Re-ID, we first introduce multigrained representations which can be extracted via the development of two separate schemes, that is, one with handcrafted descriptors and another with deep neural networks. The proposed representation seeks to characterize both appearance and spatial relations of multigrained objects, and is further equipped with importance weights which capture variations in intragroup dynamics. Optimal group-wise matching is facilitated by a multiorder matching process which, in turn, dynamically updates the importance weights in iterative fashion. We evaluated three multicamera group datasets containing complex scenarios and large dynamics, with experimental results demonstrating the effectiveness of our approach.
Vasoactive intestinal polypeptide‐like immunoreactive nerves to the human eye

Abstract Using immunohistochemical techniques, vasoactive intestinal polypeptide (VIP) is visualized in nerves distributed to the human eye. Immunoreactive nerve Fibers occur about limbal blood vessels and within the trabecular meshwork. In the iris, free‐running stromal nerves are the most common, but nerves to both dilator and sphincter muscles are present as well. Immunoreactive nerves are seen within the ciliary mucle and occasionally within a ciliary process. Innervation to choroidal blood vessels constitutes a prominent feature; innervation to more anterior uveal blood vessels is seen only irregularly. Immunoreactive to more anterior uveal blood vessels is seen only irregularly. Immunoreactive nerves are apposed to melanocytes throughout the uvea. The present findings extend prior reports in the human eye, indicating a potential role for VIP in ocular physiology. Additional neuroanatomical, biochemical and physiological studies are necessary to define fully the ocular function of VIP and to determine ultimely whether VIP has clinical and pharmacological implications.
Comparison-Based Framework for Psychophysics: Lab versus Crowdsourcing

Traditionally, psychophysical experiments are conducted by repeated measurements on a few well-trained participants under well-controlled conditions, often resulting in, if done properly, high quality data. In recent years, however, crowdsourcing platforms are becoming increasingly popular means of data collection, measuring many participants at the potential cost of obtaining data of worse quality. In this paper we study whether the use of comparison-based (ordinal) data, combined with machine learning algorithms, can boost the reliability of crowdsourcing studies for psychophysics, such that they can achieve performance close to a lab experiment. To this end, we compare three setups: simulations, a psychophysics lab experiment, and the same experiment on Amazon Mechanical Turk. All these experiments are conducted in a comparison-based setting where participants have to answer triplet questions of the form "is object x closer to y or to z? ". We then use machine learning to solve the triplet prediction problem: given a subset of triplet questions with corresponding answers, we predict the answer to the remaining questions. Considering the limitations and noise on MTurk, we find that the accuracy of triplet prediction is surprisingly close---but not equal---to our lab study.
Abnormal achromatic and chromatic contrast sensitivity in neurofibromatosis type 1.

PURPOSE Neurofibromatosis type 1 (NF1) is a monogenic disorder with the majority of patients presenting subtle to moderate cognitive impairments. Visuospatial deficits are considered to be one of the hallmark characteristics of their cognitive profile. However, low-level visual processing has not been previously investigated. Our aim was to study contrast perception in these patients to assess the function of early visual areas. METHODS Contrast sensitivity was tested in 19 children and adolescents with NF1 and 33 control children and adolescents and 12 adults with NF1 and 24 control adults. The tasks used probed two achromatic spatiotemporal frequency channels and chromatic red-green and blue-yellow pathways. RESULTS Individuals with NF1 showed significant contrast sensitivity deficits for the achromatic higher spatial frequency channel [F(₁,₈₃) = 36.1, P < 0.001] and for the achromatic low spatial high temporal (magnocellular) frequency channel [F(₁,₇₂) = 8.0, P < 0.01]. Furthermore, individuals with NF1 presented a significant deficit in chromatic red-green (parvocellular) contrast sensitivity (P < 0.01) but not in blue-yellow (koniocelular) sensitivity. The decrease in achromatic sensitivity for higher spatial frequency was observed throughout the visual field, in both central and peripheral locations. In contrast, central contrast sensitivity for the magnocellular-biased condition was relatively preserved and only peripheral sensitivity was affected. Interestingly, the same pattern of deficits was found in both age groups tested. CONCLUSIONS These findings showed that contrast sensitivity is impaired in patients with NF1, associating for the first time abnormal low-level vision to the cognitive profile of this disorder.
A Chandra Study: Are Dwarf Carbon Stars Spun Up and Rejuvenated by Mass Transfer?

Carbon stars (with C/O > 1) were long assumed to all be giants, because only asymptotic giant branch (AGB) stars dredge up significant carbon into their atmospheres. The case is nearly ironclad now that the formerly mysterious dwarf carbon (dC) stars are actually far more common than C giants and have accreted carbon-rich material from a former AGB companion, yielding a white dwarf (WD) and a dC star that has gained both significant mass and angular momentum. Some such dC systems have undergone a planetary nebula phase, and some may evolve to become CH, CEMP, or Ba giants. Recent studies indicate that most dCs are likely from older, metal-poor kinematic populations. Given the well-known anticorrelation of age and activity, dCs would not be expected to show significant X-ray emission related to coronal activity. However, accretion spin-up might be expected to rejuvenate magnetic dynamos in these post-mass-transfer binary systems. We describe our Chandra pilot study of six dCs selected from the SDSS for Hα emission and/or a hot WD companion, to test whether their X-ray emission strength and spectral properties are consistent with a rejuvenated dynamo. We detect all six dCs in the sample, which have X-ray luminosities ranging from log –29.7, preliminary evidence that dCs may be active at a level consistent with stars that have short rotation periods of several days or less. More definitive results require a sample of typical dCs with deeper X-ray observations to better constrain their plasma temperatures.
Effect of thyroid hormone on plasma apolipoproteins and ApoA‐ and ApoB‐containing lipoprotein particles

Apolipoprotein and apoB‐ and apoA‐containing lipoprotein particle concentrations were determined in 10 athyreotic patients 4 weeks after withdrawal of Synthroid replacement therapy [T4, 0.96 ± 0.66 μg mL−1; thyroid‐stimulating hormone (TSH), 62.7 ± 22.8 μIU mL−1] and again 4 weeks after reinstitution of treatment.
Continuous magnetic phase transition in artificial square ice

At a continuous phase transition, different systems can show the same behavior and are said to belong to the same universality class. Combining synchrotron x-ray scattering and numerical simulations, we establish that artificial square ice, a magnetic metamaterial consisting of a two-dimensional array of nanomagnets, admits of such a transition. The authors show that the transition belongs to the two-dimensional Ising universality class, extending the applicability of the fundamental concepts of continuous phase transitions to mesoscopic magnetic systems.
Cosmological dark matter in a conformal model

We study the collider, astrophysical and cosmological constraints on the dark matter sector of a conformal model within the framework of both the freeze out as well as the freeze in mechanism. The model has a dark sector with strong self interactions. This sector couples weakly with the Standard Model (SM) particles via a scalar messenger. The lightest dark sector particle is a pion-like fermion anti-fermion bound state. We find that the model successfully satisfies the constraints coming from the Higgs decay to the visible as well as the invisible sector. We have used the results of the dark matter direct detection experiments, such as, XENON1T in order to impose bounds on the parameters of the model. The model satisfies the indirect detection constraints of gamma ray from the galactic center and Dwarf spheroidal galaxies. We also determine the parameter range for which it satisfies the astrophysical constraints on the dark matter self coupling.
Real-Time Faulted Line Localization and PMU Placement in Power Systems Through Convolutional Neural Networks

Diverse fault types, fast reclosures, and complicated transient states after a fault event make real-time fault location in power grids challenging. Existing localization techniques in this area rely on simplistic assumptions, such as static loads, or require much higher sampling rates or total measurement availability. This paper proposes a faulted line localization method based on a convolutional neural network (CNN) classifier using bus voltages. Unlike prior data-driven methods, the proposed classifier is based on features with physical interpretations that improve the robustness of the location performance. The accuracy of our CNN-based localization tool is demonstrably superior to other machine learning classifiers in the literature. To further improve the location performance, a joint phasor measurement units placement strategy are proposed and validated against other methods. A significant aspect of our methodology is that under very low observability (${7\%}$ of buses), the algorithm is still able to localize the faulted line to a small neighborhood with high probability. The performance of our scheme is validated through simulations of faults of various types in the IEEE 39-bus and 68-bus power systems under varying uncertain conditions, system observability, and measurement quality.
Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creatives

Accurately predicting conversions in advertisements is generally a challenging task, because such conversions do not occur frequently. In this paper, we propose a new framework to support creating high-performing ad creatives, including the accurate prediction of ad creative text conversions before delivering to the consumer. The proposed framework includes three key ideas: multi-task learning, conditional attention, and attention highlighting. Multi-task learning is an idea for improving the prediction accuracy of conversion, which predicts clicks and conversions simultaneously, to solve the difficulty of data imbalance. Furthermore, conditional attention focuses attention of each ad creative with the consideration of its genre and target gender, thus improving conversion prediction accuracy. Attention highlighting visualizes important words and/or phrases based on conditional attention. We evaluated the proposed framework with actual delivery history data (14,000 creatives displayed more than a certain number of times from Gunosy Inc.), and confirmed that these ideas improve the prediction performance of conversions, and visualize noteworthy words according to the creatives' attributes.
An Experimental and Theoretical Insights into the Colossal Permittivity Mechanism of (Li, Nd) Co-doped ZnO Ceramics

In this work, we report the combined effect of donor (Nd) and acceptor (Li) co-doping at the Zn-site of ZnO ceramics on structural, microstructural and dielectric properties. Combining experimental observations with DFT based theoretical study, we have shown that before experimental fabrication DFT based first principles study can be used as a good indication to have prior qualitative assessment of a dielectric medium. For implementing this objective various Li and Nd co-doped ZnO ceramics have been synthesized through the conventional solid-state reaction route. Quantitative XRD analysis reveals the formation of wurtzite hexagonal structured ZnO having space group P63mc. Meanwhile, FESEM micrographs confirm the formation of randomly aligned non-uniform grains in size and shape. We show that the average grain size distribution and density of the studied compositions are two tuning factors to control the dielectric properties of these compounds. Though the value of dielectric constant is decreased with the increase in doping content, the optimum composition Nd0.005Li0. 005Zn0.99O exhibits slightly lower dielectric constant (2066 at 1 KHz) than pristine ZnO but relatively very low dielectric loss (0.20 at 1 KHz) at room temperature than pure ZnO ceramics sintered at 1623 K. For understanding the dielectric relaxation mechanism in the studied ceramics, complex impedance spectra analysis have also been performed and discussed thoroughly. This study provides a new insight for further development of colossal permittivity (CP) ceramics and extends the current understanding of CP mechanism in ceramic materials.
Small molecule schweinfurthins selectively inhibit cancer cell proliferation and mTOR/AKT signaling by interfering with trans-Golgi-network trafficking

Natural compound schweinfurthins are of considerable interest for novel therapy development because of their selective anti-proliferative activity against human cancer cells. We previously reported the isolation of highly active schweinfurthins E-H, and in the present study, mechanisms of the potent and selective anti-proliferation were investigated. We found that schweinfurthins preferentially inhibited the proliferation of PTEN deficient cancer cells by indirect inhibition of AKT phosphorylation. Mechanistically, schweinfurthins and their analogs arrested trans-Golgi-network trafficking, an intracellular vesicular trafficking system, resulting in the induction of endoplasmic reticulum stress and the suppression of both lipid raft-mediated PI3K activation and mTOR/RheB complex formation, which collectively led to an effective inhibition of mTOR/AKT signaling. The trans-Golgi-network traffic arresting effect of schweinfurthins was associated with their in vitro binding activity to oxysterol-binding proteins that are known to regulate intracellular vesicular trafficking. Moreover, schweinfurthins were found to be highly toxic toward PTEN-deficient B cell lymphoma cells, and displayed 2 orders of magnitude lower activity toward normal human peripheral blood mononuclear cells and primary fibroblasts in vitro. These results revealed a previously unrecognized role of schweinfurthins in regulating trans-Golgi-network trafficking, and linked mechanistically this cellular effect with mTOR/AKT signaling and with cancer cell survival and growth. Our findings suggest the schweinfurthin class of compounds as a novel approach to modulate oncogenic mTOR/AKT signaling for cancer treatment.
Texture Fields: Learning Texture Representations in Function Space

In recent years, substantial progress has been achieved in learning-based reconstruction of 3D objects. At the same time, generative models were proposed that can generate highly realistic images. However, despite this success in these closely related tasks, texture reconstruction of 3D objects has received little attention from the research community and state-of-the-art methods are either limited to comparably low resolution or constrained experimental setups. A major reason for these limitations is that common representations of texture are inefficient or hard to interface for modern deep learning techniques. In this paper, we propose Texture Fields, a novel texture representation which is based on regressing a continuous 3D function parameterized with a neural network. Our approach circumvents limiting factors like shape discretization and parameterization, as the proposed texture representation is independent of the shape representation of the 3D object. We show that Texture Fields are able to represent high frequency texture and naturally blend with modern deep learning techniques. Experimentally, we find that Texture Fields compare favorably to state-of-the-art methods for conditional texture reconstruction of 3D objects and enable learning of probabilistic generative models for texturing unseen 3D models. We believe that Texture Fields will become an important building block for the next generation of generative 3D models.
Identification of the VirB4-VirB8-VirB5-VirB2 Pilus Assembly Sequence of Type IV Secretion Systems*

Type IV secretion systems mediate the translocation of virulence factors (proteins and/or DNA) from Gram-negative bacteria into eukaryotic cells. A complex of 11 conserved proteins (VirB1-VirB11) spans the inner and the outer membrane and assembles extracellular T-pili in Agrobacterium tumefaciens. Here we report a sequence of protein interactions required for the formation of complexes between VirB2 and VirB5, which precedes their incorporation into pili. The NTPase Walker A active site of the inner membrane protein VirB4 is required for virulence, but an active site VirB4 variant stabilized VirB3 and VirB8 and enabled T-pilus formation. Analysis of VirB protein complexes extracted from the membranes with mild detergent revealed that VirB2-VirB5 complex formation depended on VirB4, which identified a novel T-pilus assembly step. Bicistron expression demonstrated direct interaction of VirB4 with VirB8, and analyses with purified proteins showed that VirB5 bound to VirB8 and VirB10. VirB4 therefore localizes at the basis of a trans-envelope interaction sequence, and by stabilization of VirB8 it mediates the incorporation of VirB5 and VirB2 into extracellular pili.
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs

Multi-hop reading comprehension (RC) across documents poses new challenge over single-document RC because it requires reasoning over multiple documents to reach the final answer. In this paper, we propose a new model to tackle the multi-hop RC problem. We introduce a heterogeneous graph with different types of nodes and edges, which is named as Heterogeneous Document-Entity (HDE) graph. The advantage of HDE graph is that it contains different granularity levels of information including candidates, documents and entities in specific document contexts. Our proposed model can do reasoning over the HDE graph with nodes representation initialized with co-attention and self-attention based context encoders. We employ Graph Neural Networks (GNN) based message passing algorithms to accumulate evidences on the proposed HDE graph. Evaluated on the blind test set of the Qangaroo WikiHop data set, our HDE graph based single model delivers competitive result, and the ensemble model achieves the state-of-the-art performance.
Learning novel object parts model for object categorization

We present a new method to learn the model based on object parts extraction and grammar which can be applied to classification and recognition. Our approach is invariant to the scale and rotation of the objects. We use Structural Context feature to detect object parts. It is done comparing SC histograms of the model and image. We extract oriented triplets from centers of detected parts. We define grammar for these parts using normalize distances and angles between them. We propose and compare two alternative implementations using different classifiers: Hidden Markov Model with mixture of Gaussian outputs (MHMM) and Adaptive Neuro-Fuzzy Inference system (ANFIS) to learn this grammar and estimate parameters of the model for each object class. The proposed method is computationally efficient and it is invariant to scale and rotation. Experimental results demonstrate the privileged performance of the proposed approach against other methods.
Whole exome sequencing reveals de novo pathogenic variants in KAT6A as a cause of a neurodevelopmental disorder

Neurodevelopmental disorders (NDD) are common, with 1–3% of general population being affected, but the etiology is unknown in most individuals. Clinical whole‐exome sequencing (WES) has proven to be a powerful tool for the identification of pathogenic variants leading to Mendelian disorders, among which NDD represent a significant percentage. Performing WES with a trio‐approach has proven to be extremely effective in identifying de novo pathogenic variants as a common cause of NDD. Here we report six unrelated individuals with a common phenotype consisting of NDD with severe speech delay, hypotonia, and facial dysmorphism. These patients underwent WES with a trio approach and de novo heterozygous predicted pathogenic novel variants in the KAT6A gene were identified. The KAT6A gene encodes a histone acetyltransfrease protein and it has long been known for its structural involvement in acute myeloid leukemia; however, it has not previously been associated with any congenital disorder. In animal models the KAT6A ortholog is involved in transcriptional regulation during development. Given the similar findings in animal models and our patient's phenotypes, we hypothesize that KAT6A could play a role in development of the brain, face, and heart in humans. © 2016 Wiley Periodicals, Inc.