Miracle-dz/UniFAR-FTUs huggingface dataset:
Retinal OCT disease classification with variational autoencoder regularization

According to the World Health Organization, 285 million people worldwide live with visual impairment. The most commonly used imaging technique for diagnosis in ophthalmology is optical coherence tomography (OCT). However, analysis of retinal OCT requires trained ophthalmologists and time, making a comprehensive early diagnosis unlikely. A recent study established a diagnostic tool based on convolutional neural networks (CNN), which was trained on a large database of retinal OCT images. The performance of the tool in classifying retinal conditions was on par to that of trained medical experts. However, the training of these networks is based on an enormous amount of labeled data, which is expensive and difficult to obtain. Therefore, this paper describes a method based on variational autoencoder regularization that improves classification performance when using a limited amount of labeled data. This work uses a two-path CNN model combining a classification network with an autoencoder (AE) for regularization. The key idea behind this is to prevent overfitting when using a limited training dataset size with small number of patients. Results show superior classification performance compared to a pre-trained and fully fine-tuned baseline ResNet-34. Clustering of the latent space in relation to the disease class is distinct. Neural networks for disease classification on OCTs can benefit from regularization using variational autoencoders when trained with limited amount of patient data. Especially in the medical imaging domain, data annotated by experts is expensive to obtain.
NM-Net: Mining Reliable Neighbors for Robust Feature Correspondences

Feature correspondence selection is pivotal to many feature-matching based tasks in computer vision. Searching spatially k-nearest neighbors is a common strategy for extracting local information in many previous works. However, there is no guarantee that the spatially k-nearest neighbors of correspondences are consistent because the spatial distribution of false correspondences is often irregular. To address this issue, we present a compatibility-specific mining method to search for consistent neighbors. Moreover, in order to extract and aggregate more reliable features from neighbors, we propose a hierarchical network named NM-Net with a series of graph convolutions that is insensitive to the order of correspondences. Our experimental results have shown the proposed method achieves the state-of-the-art performance on four datasets with various inlier ratios and varying numbers of feature consistencies.
4MOST Consortium Survey 5: eROSITA Galaxy Cluster Redshift Survey

Groups and clusters of galaxies are a current focus of astronomical research owing to their role in determining the environmental effects on galaxies and the constraints they provide to cosmology. The eROSITA X-ray telescope on board the Spectrum Roentgen Gamma observatory will be launched in 2019 and will have completed eight scans of the full sky when 4MOST starts operating. The experiment will detect groups and clusters of galaxies through X-ray emission from the hot intergalactic medium. The purpose of the 4MOST eROSITA Galaxy Cluster Redshift Survey is to provide spectroscopic redshifts of the optical counterparts to the X-ray emission from 40,000 groups and clusters of galaxies so as to perform dynamical estimates of the total mass and to measure the properties of the member galaxies. The survey aims to obtain precise redshift measurements of the photometrically identified brightest cluster galaxies at redshift $z > 0.7$. At lower redshifts ($z < 0.7$), the programme aims to sample over 15 member galaxies per cluster and enable dynamical mass measurements to calibrate the clusters for cosmological experiments. At $z < 0.2$, eROSITA will also detect X-ray emission from galaxy groups and filaments. 4MOST spectroscopic data from the survey will be used for optical identification of galaxy groups down to eROSITA's mass detection limits of $10^{13} M_\odot$, as well as the detection of the largest filaments for pioneering studies of their X-ray emission.
EE-AE: An Exclusivity Enhanced Unsupervised Feature Learning Approach

Unsupervised learning is becoming more and more important recently. As one of its key components, the autoencoder (AE) aims to learn a latent feature representation of data which is more robust and discriminative. However, most AE based methods only focus on the reconstruction within the encoder-decoder phase, which ignores the inherent relation of data, i.e., statistical and geometrical dependence, and easily causes overfitting. In order to deal with this issue, we propose an Exclusivity Enhanced (EE) unsupervised feature learning approach to improve the conventional AE. To the best of our knowledge, our research is the first to utilize such exclusivity concept to cooperate with feature extraction within AE. Moreover, in this paper we also make some improvements to the stacked AE structure especially for the connection of different layers from decoders, this could be regarded as a weight initialization trial. The experimental results show that our proposed approach can achieve remarkable performance compared with other related methods.
Looking for 
B→Xsℓ+ℓ−
 in a nonminimal universal extra dimensional model

Non-vanishing boundary localised terms significantly modify the mass spectrum and various interactions among the Kaluza-Klein excited states of 5-Dimensional Universal Extra Dimensional scenario. In this scenario we compute the contributions of Kaluza-Klein excitations of gauge bosons and third generation quarks for the decay process $B\rightarrow X_s\ell^+\ell^-$ incorporating next-to-leading order QCD corrections. We estimate branching ratio as well as Forward Backward asymmetry associated with this decay process. Considering the constraints from some other $b \to s$ observables and electroweak precision data we show that significant amount of parameter space of this scenario has been able to explain the observed experimental data for this decay process. From our analysis we put lower limit on the size of the extra dimension by comparing our theoretical prediction for branching ratio with the corresponding experimental data. Depending on the values of free parameters of the present scenario, lower limit on the inverse of the radius of compactification ($R^{-1}$) can be as high as $\geq 760$ GeV. Unfortunately, the Forward Backward asymmetry of this decay process would not provide any significant limit on $R^{-1}$ in the present model.
Person Re-identification with Bias-controlled Adversarial Training

Inspired by the effectiveness of adversarial training in the area of Generative Adversarial Networks we present a new approach for learning feature representations in person re-identification. We investigate different types of bias that typically occur in re-ID scenarios, i.e., pose, body part and camera view, and propose a general approach to address them. We introduce an adversarial strategy for controlling bias, named Bias-controlled Adversarial framework (BCA), with two complementary branches to reduce or to enhance bias-related features. The results and comparison to the state of the art on different benchmarks show that our framework is an effective strategy for person re-identification. The performance improvements are in both full and partial views of persons.
USIP: Unsupervised Stable Interest Point Detection From 3D Point Clouds

In this paper, we propose the USIP detector: an Unsupervised Stable Interest Point detector that can detect highly repeatable and accurately localized keypoints from 3D point clouds under arbitrary transformations without the need for any ground truth training data. Our USIP detector consists of a feature proposal network that learns stable keypoints from input 3D point clouds and their respective transformed pairs from randomly generated transformations. We provide degeneracy analysis and suggest solutions to prevent it. We encourage high repeatability and accurate localization of the keypoints with a probabilistic chamfer loss that minimizes the distances between the detected keypoints from the training point cloud pairs. Extensive experimental results of repeatability tests on several simulated and real-world 3D point cloud datasets from Lidar, RGB-D and CAD models show that our USIP detector significantly outperforms existing hand-crafted and deep learning-based 3D keypoint detectors. Our code is available at the project website. https://github.com/lijx10/USIP
Robust Subspace Recovery Layer for Unsupervised Anomaly Detection

We propose a neural network for unsupervised anomaly detection with a novel robust subspace recovery layer (RSR layer). This layer seeks to extract the underlying subspace from a latent representation of the given data and removes outliers that lie away from this subspace. It is used within an autoencoder. The encoder maps the data into a latent space, from which the RSR layer extracts the subspace. The decoder then smoothly maps back the underlying subspace to a "manifold" close to the original inliers. Inliers and outliers are distinguished according to the distances between the original and mapped positions (small for inliers and large for outliers). Extensive numerical experiments with both image and document datasets demonstrate state-of-the-art precision and recall.
Structured Minimally Supervised Learning for Neural Relation Extraction

We present an approach to minimally supervised relation extraction that combines the benefits of learned representations and structured learning, and accurately predicts sentence-level relation mentions given only proposition-level supervision from a KB. By explicitly reasoning about missing data during learning, our approach enables large-scale training of 1D convolutional neural networks while mitigating the issue of label noise inherent in distant supervision. Our approach achieves state-of-the-art results on minimally supervised sentential relation extraction, outperforming a number of baselines, including a competitive approach that uses the attention layer of a purely neural model.
Discrete Rotation Equivariance for Point Cloud Recognition

Despite the recent active research on processing point clouds with deep networks, few attention has been on the sensitivity of the networks to rotations. In this paper, we propose a deep learning architecture that achieves discrete $SO(2)/ SO(3)$ rotation equivariance for point cloud recognition. Specifically, the rotation of an input point cloud with elements of a rotation group is similar to shuffling the feature vectors generated by our approach. The equivariance is easily reduced to invariance by eliminating the permutation with operations such as maximum or average. Our method can be directly applied to any existing point cloud based networks, resulting in significant improvements in their performance for rotated inputs. We show state-of-the-art results in the classification tasks with various datasets under both SO(2) and SO(3) rotations. In addition, we further analyze the necessary conditions of applying our approach to PointNet [1] based networks.
Environmental dependence of the correlations between stoichiometric and fatty acid‐based indicators of phytoplankton nutritional quality

Marine phytoplankton is simultaneously affected by multiple environmental drivers. To‐date integrative assessments of multiple combined effects are rare on the relationship between elemental stoichiometry and biochemicals in marine phytoplankton. We investigated responses of stoichiometric (N:C and P:C ratios) and fatty acid‐based (polyunsaturated fatty acid, PUFA) indicators of nutritional quality to three N:P supply ratios (10:1, 24:1, and 63:1 mol mol−1), three temperatures (12, 18, and 24°C) and two pCO2 levels (560 and 2400 μatm) in the marine phytoplankters Rhodomonas sp. and Phaeodactylum tricornutum. Overall, warming and nutrient deficiency showed dramatic effects, but increased pCO2 had modest effects on the two indicators of nutritional quality. Specifically, warming showed strong positive effects on N:C and P:C ratios in Rhodomonas sp. but negative effects on PUFAs in both species. The low N‐ and low P‐media led to low contents of both nutrients but high contents of PUFAs in the biomass of Rhodomonas sp. , while the response of P. tricornutum was more complex: N:C ratios were lowest at the intermediate N:P supply but P:C ratios responded negatively to P deficiency and positively to N deficiency. Large variations in the two indicators of nutritional quality can be attributed to species‐specific physiological optima and interactions between the three manipulated variables. Our results suggest that stoichiometric and FA‐based indicators of nutritional quality may change differentially in response to warming and nutrient deficiency in marine phytoplankton, highlighting the relevance of simultaneous considerations of the two indicators of nutritional quality, when assessing food web dynamics under future ocean scenarios.
Invertible functions on nonarchimedean symmetric spaces

Let $u$ be a nowhere vanishing holomorphic function on the Drinfeld space $\Omega^{r}$ of dimension $r-1$, where $r \geq 2$. The logarithm $\log_{q}\lvert u \rvert$ of its absolute value may be regarded as an affine function on the attached Bruhat-Tits building $\mathcal{BT}^{r}$. Generalizing a construction of van der Put in case $r=2$, we relate the group $\mathcal{O}(\Omega^{r})^{*}$ of such $u$ with the group $\mathbf{H}(\mathcal{BT}^{r}, \mathbb{Z})$ of integer-valued harmonic 1-cochains on $\mathcal{BT}^{r}$. This also gives rise to a natural $\mathbb{Z}$-structure on the first ($\ell$-adic or de Rham) cohomology of $\Omega^{r}$.
Multi-Scale Time-Frequency Attention for Rare Sound Event Detection

Most attention-based methods only concentrate along the time axis, which is insufficient for Acoustic Event Detection (AED). Meanwhile, previous methods for AED rarely considered that target events possess distinct temporal and frequential scales. In this work, we propose a Multi-Scale Time-Frequency Attention (MTFA) module for AED. MTFA gathers information at multiple resolutions to generate a time-frequency attention mask which tells the model where to focus along both time and frequency axis. With MTFA, the model could capture the characteristics of target events with different scales. We demonstrate the proposed method on Task 2 of Detection and Classification of Acoustic Scenes and Events (DCASE) 2017 Challenge. Our method achieves competitive results on both development dataset and evaluation dataset.
Modeling Drug-Disease Relations with Linguistic and Knowledge Graph Constraints

FDA drug labels are rich sources of information about drugs and drug-disease relations, but their complexity makes them challenging texts to analyze in isolation. To overcome this, we situate these labels in two health knowledge graphs: one built from precise structured information about drugs and diseases, and another built entirely from a database of clinical narrative texts using simple heuristic methods. We show that Probabilistic Soft Logic models defined over these graphs are superior to text-only and relation-only variants, and that the clinical narratives graph delivers exceptional results with little manual effort. Finally, we release a new dataset of drug labels with annotations for five distinct drug-disease relations.
Probing the nature of central objects in extreme-mass-ratio inspirals with gravitational waves

We extend the work of Ryan on mapping the spacetime of the central object of an extreme mass-ratio inspiral (EMRI) by using gravitational waves (GWs) emitted by the system, which may be observed in future missions such as LISA. Whether the central object is a black hole or not can be probed by observing the phasing of these waves, which carry information about its mass and spin multipole moments. We go beyond the phase terms found by Ryan, which were obtained in the quadrupolar approximation of the point-particle limit, and derive terms up to the fifth post-Newtonian (PN) order. Since corrections due to horizon absorption (i.e., if the central object is a black hole) and tidal heating appear by that order, at 2.5PN and 5PN, respectively, we include them here. Corrections due to the motion of the central object, which was addressed only partially by Ryan, are included as well. Additionally, we obtain the contribution of the higher order radiative multipole moments. For the tidal interaction, our results have been derived in the approximation of the Newtonian tidal field. Therefore, in the potential for tidal field only the contribution due to the mass of the central object has been included as well. Using these results we argue that it might be possible for LISA to probe if the central object in an EMRI has a horizon or not. We also discuss how our results can be used to test the No-hair theorem from the inspiral phase of such systems.
Application of Cluster Variation and Path Probability Methods to the Tetragonal–Cubic Phase Transition in ZrO2

Cluster variation method (CVM) and path probability method (PPM) have generally been employed to study replacive phase transitions in alloy systems. Recently, displacive phase transitions have been explored within the realm of replacive phase transition in the CVM theoretical framework by viewing displaced atoms as different atomic species, i.e., by converting a freedom of atomic displacement to a configurational freedom. The same methodology is applied to the PPM calculations in this work, and the kinetics of displacive phase transition from tetragonal to cubic phases in ZrO2 are investigated as well as their equilibrium states.
Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction

Conditional text-to-image generation is an active area of research, with many possible applications. Existing research has primarily focused on generating a single image from available conditioning information in one step. One practical extension beyond one-step generation is a system that generates an image iteratively, conditioned on ongoing linguistic input or feedback. This is significantly more challenging than one-step generation tasks, as such a system must understand the contents of its generated images with respect to the feedback history, the current feedback, as well as the interactions among concepts present in the feedback history. In this work, we present a recurrent image generation model which takes into account both the generated output up to the current step as well as all past instructions for generation. We show that our model is able to generate the background, add new objects, and apply simple transformations to existing objects. We believe our approach is an important step toward interactive generation. Code and data is available at: https://www.microsoft. com/en-us/research/project/generative-neural-visual-artist-geneva/.
Dog activity classification with movement sensor placed on the collar

Dog owners are highly motivated in understanding behavior and physiology of their pets and monitoring their wellbeing. Monitoring with a commercially available activity trackers reveals levels of daily activity and rest but recognizing the behavior of the dog would provide additional information, especially when the dog is not under supervision. In this study, a performance of a 3D accelerometer movement sensor placed on the dog collar was evaluated in classifying seven activities during semi-controlled test situation with 24 dogs. Various features were extracted from the acceleration time series signals. The performance of two classifiers was evaluated with two feature scenarios: using all computed features and the ones given by forward selection algorithm. The highest overall classification accuracy for the seven behaviors was 76%. The results are promising pro improving classification of specific behaviors by relatively simple algorithms.
High-resolution Observations of the Molecular Clouds Associated with the Huge H ii Region CTB 102

We report the first high-resolution (subarcminute) large-scale mapping 12CO and 13CO observations of the molecular clouds associated with the giant outer Galaxy H ii region CTB 102 (KR 1). These observations were made using a newly commissioned receiver system on the 13.7 m radio telescope at the Taeduk Radio Astronomy Observatory. Our observations show that the molecular clouds have a spatial extent of 60 × 35 pc and a total mass of 104.8–105.0 M⊙. Infrared data from the Wide-field Infrared Survey Explorer and Two Micron All Sky Survey were used to identify and classify the young stellar object (YSO) population associated with ongoing star formation activity within the molecular clouds. We directly detect 18 class I/class II YSOs and six transition disk objects. Moving away from the H ii region, there is an age/class gradient consistent with sequential star formation. The infrared and molecular-line data were combined to estimate the star formation efficiency (SFE) of the entire cloud as well as the SFE for various subregions of the cloud. We find that the overall SFE is between ∼5% and 10%, consistent with previous observations of giant molecular clouds. One of the subregions, region 1a, is a clear outlier, with a SFE of 17%–35% on a 5 pc spatial scale. This high SFE is more typical for much smaller (subparsec scale) star-forming cores, and we think region 1a is likely an embedded massive protocluster.
Possibility of a Coordinated Signaling Scheme in the Galaxy and SETI Experiments

We discuss a Galaxy-wide coordinated signaling scheme with which a SETI observer needs to examine a tiny fraction of the sky. The target sky direction is determined as a function of time, based on high-precision measurements of a progenitor of a conspicuous astronomical event such as a coalescence of a double neutron star binary. In various respects, such a coordinated scheme would be advantageous for both transmitters and receivers, and might be widely prevailing as a tacit adjustment. For this scheme, the planned space gravitational-wave detector LISA and its follow-on missions have the potential to narrow down the target sky area by a factor of 103–4, and could have a large impact on future SETI experiments.
Genetic diversity of the non-native crab Rhithropanopeus harrisii (Brachyura: Panopeidae) in the Polish coastal waters − an example of patchy genetic diversity at a small geographic scale

Abstract The American panopeid crab species Rhithropanopeus harrisii (Gould, 1841) is listed as an non-native species in European waters. In Poland, it occurred in the 1950s at two sites at the Baltic Sea coast, the Dead Vistula River (DVR) and the Vistula Lagoon (VL). Almost 50 years later, two additional populations were identified in the Gulf of Gdańsk (GG) and its inner part, Puck Bay (PB). In the present study, we sequenced and analyzed part of the mitochondrial cytochrome oxidase subunit I gene of the four Polish populations of R. harrisii in order to assess their genetic diversity and connectivity. The analyzed sequences of a length of 989 base pairs revealed eight different haplotypes. The highest number of haplotypes (n=6) was observed in the population from GG, whereas the lowest (n=3) in VL. The most common haplotype was recorded in 37% of the analyzed individuals. Pairwise ΦST values were mostly non-significant, with the exception of the comparison between DVR and VL (ΦST = 0.267; P < 0.05) and between PB and VL (ΦST = 0.194; P < 0.05), indicating a restricted gene flow or different sources of colonization.