Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk of Dementia in Patients with Depression or Parkinson's Disease: A Retrospective Cohort Study

Background This study aimed to clarify whether Parkinson's disease (PD) and depression were independent risk factors or with synergic effects in dementia. Methods Newly diagnosed PD (n = 1213) patients and control subjects (n = 4852) were selected from the Taiwan National Health Insurance Research Database from January 2001 through December 2008. Follow-up ended in 2011 with an outcome of dementia occurring or not. This cohort was divided into controls with or without depression, PD only, and PD with depression. The incident rate of dementia and hazard ratio (HR) using Cox's regression analysis were calculated for each group. Results When compared with controls without depression as HR 1.00, the adjusted HR for dementia was 3.29 (p < 0.001) in the PD only group, 2.77 (p < 0.001) in the PD with depression group, and 1.55 (p=0.024) in the depression only group. The incident rate of dementia was 29.2 (per 1000 person-years) in the PD only group and 13.2 in the PD with depression group. The effect of PD on dementia in the depression group produced a HR of 0.97 (p=0.905). Conclusions Parkinson's disease served as a risk factor for dementia. By comparison, depression was not a risk factor for dementia in PD patients, although it did act as a risk factor for dementia.
Thresholds of Abnormality Perception in Facial Esthetics among Laypersons and Dental Professionals: Profile Esthetics

Aim To find thresholds at which laypersons and dental professionals from Jordanian population perceive abnormalities in sagittal positioning of upper and lower jaws as a major determinant to facial profile esthetics. Materials and Methods Using photo editing software, a baseline profile image of a young male was manipulated on a 2 mm incremental basis to move each of the upper and lower jaws backward and forward relative to true vertical line (TVL) at which four variables of maxillary and mandibular retrusion and protrusion were researched. A total of 120 participants divided equally into four groups of laypersons, general dental practitioners (GDPs), orthodontists, and oral and maxillofacial surgeons (OMFSs) rated the images using an analog scale of 100 mm long. The image that showed the first statistical difference compared to the baseline was considered as a threshold of abnormality. Results Laypersons, GDPs, and OMFSs perceived the abnormality in the maxillary retrusion at −5 mm to TVL, while orthodontists defined that at −3 mm. All dental professionals perceived the abnormality in the maxillary protrusion at +1 mm to TVL while the layperson group at +3 mm. A threshold of −7 mm mandibular retrusion to TVL was abnormally perceived by all groups. All dental professionals realized the abnormality in the mandibular protrusion at 0 mm to TVL while the laypersons at +2 mm. Conclusion These thresholds regarding profile esthetics may contribute to the process of establishing proper orthodontic treatment planning that suits the highest facial esthetic standards.
A comment on no-force conditions for black holes and branes

In the context of the weak gravity conjecture the notion of quasi-extremality for black holes and branes was recently defined as the property of having either vanishing horizon size or surface gravity. It was derived that such objects obey a no-force condition. In this short note I present a simplified derivation that is essentially present in the formalism of timelike reduction pioneered by Breitenlohner, Gibbons and Maison. This formalism also provides the natural definition of quasi-extremality for gravitational instantons (and wormholes) sourced by axion fluxes and strengthens the argument that macroscopic axion wormholes do not contribute in the path integral since they are self-repulsive in a Euclidean sense.
Rapid changes in Atlantic grey seal milk from birth to desertion

True seals have the shortest lactation periods of any group of placental mammal. Most are capital breeders that undergo short, intense lactations, during which they fast while transferring substantial proportions of their body reserves to their pups, which they then abruptly desert. Milk was collected from Atlantic grey seals (Halichoerus grypus) periodically from birth until near desertion. Milk protein profiles matured within 24 hours or less, indicating the most rapid transition from colostrum to mature phase lactation yet observed. There was an unexpected persistence of immunoglobulin G almost until weaning, potentially indicating prolonged trans-intestinal transfer of IgG. Among components of innate immune protection were found fucosyllactose and siallylactose that are thought to impede colonisation by pathogens and encourage an appropriate gut microbiome. These oligosaccharides decreased from early lactation to almost undetectable levels by weaning. Taurine levels were initially high, then fell, possibly indicative of taurine dependency in seals, and progressive depletion of maternal reserves. Metabolites that could signal changes in the mother’s metabolism of fats, such as nicotinamide and derivatives, rose from virtual absence, and acetylcarnitines fell. It is therefore possible that indicators of maternal metabolic strain exist that signal the imminence of desertion.
A Dataset and a Convolutional Model for Iconography Classification in Paintings

Iconography in art is the discipline that studies the visual content of artworks to determine their motifs and themes and to characterize the way these are represented. It is a subject of active research for a variety of purposes, including the interpretation of meaning, the investigation of the origin and diffusion in time and space of representations, and the study of influences across artists and artworks. With the proliferation of digital archives of art images, the possibility arises of applying Computer Vision techniques to the analysis of art images at an unprecedented scale, which may support iconography research and education. In this article, we introduce a novel paintings dataset for iconography classification and present the quantitative and qualitative results of applying a Convolutional Neural Network (CNN) classifier to the recognition of the iconography of artworks. The proposed classifier achieves good performances (71.17% Precision, 70.89% Recall, 70.25% F1-Score, and 72.73% Average Precision) in the task of identifying saints in Christian religious paintings, a task made difficult by the presence of classes with very similar visual features. Qualitative analysis of the results shows that the CNN focuses on the traditional iconic motifs that characterize the representation of each saint and exploits such hints to attain correct identification. The ultimate goal of our work is to enable the automatic extraction, decomposition, and comparison of iconography elements to support iconographic studies and automatic artwork annotation.
In Search of Robust Measures of Generalization

One of the principal scientific challenges in deep learning is explaining generalization, i.e., why the particular way the community now trains networks to achieve small training error also leads to small error on held-out data from the same population. It is widely appreciated that some worst-case theories -- such as those based on the VC dimension of the class of predictors induced by modern neural network architectures -- are unable to explain empirical performance. A large volume of work aims to close this gap, primarily by developing bounds on generalization error, optimization error, and excess risk. When evaluated empirically, however, most of these bounds are numerically vacuous. Focusing on generalization bounds, this work addresses the question of how to evaluate such bounds empirically. Jiang et al. (2020) recently described a large-scale empirical study aimed at uncovering potential causal relationships between bounds/measures and generalization. Building on their study, we highlight where their proposed methods can obscure failures and successes of generalization measures in explaining generalization. We argue that generalization measures should instead be evaluated within the framework of distributional robustness.
Geodesic networks in Liouville quantum gravity surfaces

Recent work has shown that for $\gamma \in (0,2)$, a Liouville quantum gravity (LQG) surface can be endowed with a canonical metric. We prove several results concerning geodesics for this metric. In particular, we completely classify the possible networks of geodesics from a typical point on the surface to an arbitrary point on the surface, as well as the types of networks of geodesics between pairs of points on the surface which are dense. This latter result is the $\gamma$-LQG analog of the classification of geodesic networks in the Brownian map due to Angel, Kolesnik, and Miermont (2017). We also show that there is a deterministic $m\in\mathbb N$ such that almost surely any two points are joined by at most $m$ distinct LQG geodesics.
CRYPTOGRU: Low Latency Privacy-Preserving Text Analysis With GRU

Homomorphic encryption (HE) and garbled circuit (GC) provide the protection for users’ privacy. However, simply mixing the HE and GC in RNN models suffer from long inference latency due to slow activation functions. In this paper, we present a novel hybrid structure of HE and GC gated recurrent unit (GRU) network, , for low-latency secure inferences. replaces computationally expensive GC-based tanh with fast GC-based ReLU, and then quantizes sigmoid and ReLU to smaller bit-length to accelerate activations in a GRU. We evaluate with multiple GRU models trained on 4 public datasets. Experimental results show achieves top-notch accuracy and improves the secure inference latency by up to 138\times over one of the state-of-the-art secure networks on the Penn Treebank dataset.
The Nature and Origins of Sub‐Neptune Size Planets

Planets intermediate in size between the Earth and Neptune, and orbiting closer to their host stars than Mercury does the Sun, are the most common type of planet revealed by exoplanet surveys over the last quarter century. Results from NASA's Kepler mission have revealed a bimodality in the radius distribution of these objects, with a relative underabundance of planets between 1.5 and 2.0 R⊕ . This bimodality suggests that sub‐Neptunes are mostly rocky planets that were born with primary atmospheres a few percent by mass accreted from the protoplanetary nebula. Planets above the radius gap were able to retain their atmospheres (“gas‐rich super‐Earths”), while planets below the radius gap lost their atmospheres and are stripped cores (“true super‐Earths”). The mechanism that drives atmospheric loss for these planets remains an outstanding question, with photoevaporation and core‐powered mass loss being the prime candidates. As with the mass‐loss mechanism, there are two contenders for the origins of the solids in sub‐Neptune planets: the migration model involves the growth and migration of embryos from beyond the ice line, while the drift model involves inward‐drifting pebbles that coagulate to form planets close‐in. Atmospheric studies have the potential to break degeneracies in interior structure models and place additional constraints on the origins of these planets. However, most atmospheric characterization efforts have been confounded by aerosols. Observations with upcoming facilities are expected to finally reveal the atmospheric compositions of these worlds, which are arguably the first fundamentally new type of planetary object identified from the study of exoplanets.
Learning to Sort Image Sequences via Accumulated Temporal Differences

Consider a set of n images of a scene with dynamic objects captured with a static or a handheld camera. Let the temporal order in which these images are captured be unknown. There can be n! possibilities for the temporal order in which these images could have been captured. In this work, we tackle the problem of temporally sequencing the unordered set of images of a dynamic scene captured with a hand-held camera. We propose a convolutional block which captures the spatial information through 2D convolution kernel and captures the temporal information by utilizing the differences present among the feature maps extracted from the input images. We evaluate the performance of the proposed approach on the dataset extracted from a standard action recognition dataset, UCF101. We show that the proposed approach outperforms the state-of-the-art methods by a significant margin. We show that the network generalizes well by evaluating it on a dataset extracted from the DAVIS dataset, a dataset meant for video object segmentation, when the same network was trained with a dataset extracted from UCF101, a dataset meant for action recognition.
Constraining accretion efficiency in massive binary stars with LIGO –Virgo black holes

The growing sample of LIGO–Virgo black holes (BHs) opens new perspectives for the study of massive binary evolution. Here, we study the impact of mass accretion efficiency and common envelope on the properties of binary BH (BBH) mergers, by means of population synthesis simulations. We model mass accretion efficiency with the parameter fMT ∈ [0.05, 1], which represents the fraction of mass lost from the donor which is effectively accreted by the companion. Lower values of fMT result in lower BBH merger rate densities and produce mass spectra skewed towards lower BH masses. Our hierarchical Bayesian analysis, applied to BBH mergers in the first and second gravitational-wave transient catalogue, yields zero support for values of fMT ≲ 0.6, with a lower boundary of the 99 per cent credible intervals equal to fMT = 0.59. This result holds for all the values of the common-envelope efficiency parameter we considered in this study αCE ∈ [1, 10]. This confirms that gravitational-wave data can be used to put constraints on several uncertain binary evolution processes.
Disentangling Action Sequences: Discovering Correlated Samples

Disentanglement is a highly desirable property of representation due to its similarity with human's understanding and reasoning. This improves interpretability, enables the performance of down-stream tasks, and enables controllable generative models. However, this domain is challenged by the abstract notion and incomplete theories to support unsupervised disentanglement learning. We demonstrate the data itself, such as the orientation of images, plays a crucial role in disentanglement and instead of the factors, and the disentangled representations align the latent variables with the action sequences. We further introduce the concept of disentangling action sequences which facilitates the description of the behaviours of the existing disentangling approaches. An analogy for this process is to discover the commonality between the things and categorizing them. Furthermore, we analyze the inductive biases on the data and find that the latent information thresholds are correlated with the significance of the actions. For the supervised and unsupervised settings, we respectively introduce two methods to measure the thresholds. We further propose a novel framework, fractional variational autoencoder (FVAE), to disentangle the action sequences with different significance step-by-step. Experimental results on dSprites and 3D Chairs show that FVAE improves the stability of disentanglement.
Graph Neural Network for Large-Scale Network Localization

Graph neural networks (GNNs) are popular to use for classifying structured data in the context of machine learning. But surprisingly, they are rarely applied to regression problems. In this work, we adopt GNN for a classic but challenging nonlinear regression problem, namely the network localization. Our main findings are in order. First, GNN is potentially the best solution to large-scale network localization in terms of accuracy, robustness and computational time. Second, proper thresholding of the communication range is essential to its superior performance. Simulation results corroborate that the proposed GNN based method outperforms all state-of-the-art benchmarks by far. Such inspiring results are theoretically justified in terms of data aggregation, non-line-of-sight (NLOS) noise removal and low-pass filtering effect, all affected by the threshold for neighbor selection. Code is available at https://github.com/Yanzongzi/GNN-For-localization.
HIPK2 sustains inflammatory cytokine production by promoting endoplasmic reticulum stress in macrophages

Uncontrolled inflammatory cytokine production by macrophages contributes to numerous conditions, including infection, endotoxemia and sepsis. A previous study proposed that endoplasmic reticulum (ER) stress acts as an essential process in inflammatory cytokine production by macrophages. The present study used a mouse sepsis model and in vitro macrophages to demonstrate that homeodomain-interacting protein kinase 2 (HIPK2) sustained cytokine production in an ER stress-dependent manner. HIPK2 expression was upregulated in the early phase of lipopolysaccharide stimulation. HIPK2 knockdown attenuated IL-6 and TNF-α production, and p65 phosphorylation in macrophages. Furthermore, the attenuated cytokine production was abolished by the ER stress agonist tunicamycin. The activation of ER stress increased the levels of IL-6 and TNF-α, and the phosphorylation of p65, in macrophages following knockdown of HIPK2. Furthermore, HIPK2 inhibition attenuated the production of IL-6 and TNF-α in vitro and in vivo. Therefore, HIPK2 sustained inflammatory cytokine production by promoting ER stress in macrophages. Targeting HIPK2 may be a potential strategy for the management of uncontrolled inflammation in clinical settings.
Error Bounds of Imitating Policies and Environments for Reinforcement Learning

In sequential decision-making, imitation learning (IL) trains a policy efficiently by mimicking expert demonstrations. Various imitation methods were proposed and empirically evaluated, meanwhile, their theoretical understandings need further studies, among which the compounding error in long-horizon decisions is a major issue. In this paper, we first analyze the value gap between the expert policy and imitated policies by two imitation methods, behavioral cloning (BC) and generative adversarial imitation. The results support that generative adversarial imitation can reduce the compounding error compared to BC. Furthermore, we establish the lower bounds of IL under two settings, suggesting the significance of environment interactions in IL. By considering the environment transition model as a dual agent, IL can also be used to learn the environment model. Therefore, based on the bounds of imitating policies, we further analyze the performance of imitating environments. The results show that environment models can be more effectively imitated by generative adversarial imitation than BC. Particularly, we obtain a policy evaluation error that is linear with the effective planning horizon w.r. t. the model bias, suggesting a novel application of adversarial imitation for model-based reinforcement learning (MBRL). We hope these results could inspire future advances in IL and MBRL.
Association of Estrogen Receptor 1 and Tumor Necrosis Factor α Polymorphisms with Temporomandibular Joint Anterior Disc Displacement without Reduction

Objectives The aim of this study was to investigate the role of ESR1 rs1643821 and TNF-α rs1800629 as potential genetic factors regulating anterior disc displacement without reduction-mediated inflammatory pathway. Background The temporomandibular joint is a complex synovial joint that allows mandibular movement in three directions. Although temporomandibular disorders are widespread, limited data is available on the biochemical characteristics of the displaced disc and quality of the surrounding soft tissue. Changes in degenerative tissue provoke disc displacement which involves secretion of inflammatory markers and sequential conversion of fibroblast-like cells into chondrocyte-like cells. Due to the high occurrence in female adolescents, the potential role of sex hormones in temporomandibular joint disorders has been speculated. Furthermore, anterior disc displacement without reduction severely affects the quality of life. Methods 124 Caucasian patients with a history of at least one anterior disc displacement without reduction within 3 months were enrolled. Anterior disc displacement without reduction was diagnosed based on clinical examination, diagnostic criteria (DC)/TMD, and cone-beam computed tomography/magnetic resonance imaging (CBCT/MRI). The control group consisted of 126 patients with no temporomandibular joint disorders. Genotyping of two single nucleotide polymorphisms, estrogen receptor 1 (ESR1) rs1643821, and tumor necrosis factor α (TNF-α) rs1800629 was performed. Results ESR1 rs1643821 showed significant P values (using chi-square analysis) revealing the difference in anterior disc displacement without reduction frequencies while TNF-α rs1800629 polymorphism was found to be statistically insignificant when compared to the control group. Furthermore, patients with a genotype of ESR1 rs1643821 showed a decreased probability (OR = 0.412) against anterior disc displacement without reduction when compared to the GG genotype (OR = 1). Conclusion ESR1 rs1643821 with A allele frequency was lower in patients with anterior disc displacement without reduction compared to the control group. Thus, the rs1643821 variant is significantly associated with susceptibility to the anterior disc displacement without a reduction in European Caucasians. Conversely, TNF-α rs1800629 was a statistically insignificant factor against anterior disc displacement without reduction when compared to the control group.
Millicharged cosmic rays and low recoil detectors

We consider the production of a "fast flux" of hypothetical millicharged particles (mCPs) in the interstellar medium (ISM). We consider two possible sources induced by cosmic rays: (a) $pp\rightarrow$(meson)$\rightarrow$(mCP) which adds to atmospheric production of mCPs, and (b) cosmic-ray up-scattering on a millicharged component of dark matter. We notice that the galactic magnetic fields retain mCPs for a long time, leading to an enhancement of the fast flux by many orders of magnitude. In both scenarios, we calculate the expected signal for direct dark matter detection aimed at electron recoil. We observe that in Scenario (a) neutrino detectors (ArgoNeuT and Super-Kamiokande) still provide superior sensitivity compared to dark matter detectors (XENON1T). However, in scenarios with a boosted dark matter component, the dark matter detectors perform better, given the enhancement of the upscattered flux at low velocities. Given the uncertainties, both in the flux generation model and in the actual atomic physics leading to electron recoil, it is still possible that the XENON1T-reported excess may come from a fast mCP flux, which will be decisively tested with future experiments.
The Primal-Dual method for Learning Augmented Algorithms

The extension of classical online algorithms when provided with predictions is a new and active research area. In this paper, we extend the primal-dual method for online algorithms in order to incorporate predictions that advise the online algorithm about the next action to take. We use this framework to obtain novel algorithms for a variety of online covering problems. We compare our algorithms to the cost of the true and predicted offline optimal solutions and show that these algorithms outperform any online algorithm when the prediction is accurate while maintaining good guarantees when the prediction is misleading.