Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable Bayesian Functional Connectivity Inference for Multi-Electrode Array Recordings

Multi-electrode arrays (MEAs) can record extracellular action potentials (also known as 'spikes') from hundreds or thousands of neurons simultaneously. Inference of a functional network from a spike train is a fundamental and formidable computational task in neuroscience. With the advancement of MEA technology, it has become increasingly crucial to develop statistical tools for analyzing multiple neuronal activity as a network. In this paper, we propose a scalable Bayesian framework for inference of functional networks from MEA data. Our framework makes use of the hierarchical structure of networks of neurons. We split the large scale recordings into smaller local networks for network inference, which not only eases the computational burden from Bayesian sampling but also provides useful insights on regional connections in organoids and brains. We speed up the expensive Bayesian sampling process by using parallel computing. Experiments on both synthetic datasets and large-scale real-world MEA recordings show the effectiveness and efficiency of the scalable Bayesian framework. Inference of networks from controlled experiments exposing neural cultures to cadmium presents distinguishable results and further confirms the utility of our framework.
Octopus: Privacy-Preserving Collaborative Evaluation of Loan Stacking

With the rise of online lenders, the loan stacking problem has become a significant issue in the financial industry. One of the key steps in the fight against it is the querying of the loan history of a borrower from peer lenders. This is especially important in markets without a trusted credit bureau. To protect participants privacy and business interests, we want to hide borrower identities and lenders data from the loan originator, while simultaneously verifying that the borrower authorizes the query. In this paper, we propose Octopus, a distributed system to execute the query while meeting all the above security requirements. Theoretically, Octopus is sound. Practically, it integrates multiple optimizations to reduce communication and computation overhead. Evaluation shows that Octopus can run on 800 geographically distributed servers and can perform a query within about 0.5 seconds on average.
A phylogeny for side-necked turtles Pleurodira) based on mitochondrial gene sequence variation (Chelonia: and nuclear

Aspects of the phylogeny of pleurodiran turtles are contentious, particularly within the Chelidae. Morphological analyses group the long-necked Australasian Chelodina and the longnecked South American Chelus and Hydromedusa into a single clade, suggesting a common derived origin of the long neck and associated habits that predated the separation of Australia from South America. In contrast, published analyses of IZSrRNA and cytochrome b sequences suggest that the long-necked Chelodina are more closely related to the short-necked Australasian genera than to either Chelus or Hydromedusa. This paper adds partial sequences of 16s rRNA and CO1 mitochondria1 genes and partial sequences of the nuclear oncogene c-mos to test a range of previous hypotheses on the phylogenetic relationships among chelid turtles. In total, 1382 nucleotides were available for each of 25 taxa after elimination of ambiguously aligned regions. These taxa included representatives of all the genera of the turtle families Chelidae and Pelomedusidae, the three sub-genera of Phrynops, and recognized sub-generic groups of Elseya and Chelodina. Of the four genes examined, 12s ? -MA was the most informative, followed by c-mos with 16s rRNA and COI the least informative. The molecular data support the currently accepted arrangement for pelomedusid genera, that is, a sister relationship between the African Pelusios and Pelomedusa and a clade comprising the South American Peltoceflhalus and Podocnemis with the Madagascan Eryrnnochelys. However, there is also support for Elymnochebs and Podocnemis as sister taxa to the exclusion of Peltocephalus (bootstrap values of 69-80%) which is at odds with the most commonly accepted arrangement. The South American chelids are monophyletic (76-82%). This clade includes the long-necked Chelus and Hydromedusa, but excludes the Australasian long-necked Chelodina. Furthermore, the South American long-necked chelids are not themselves monophyletic, with 98-100% bootstrap values for the node supporting Chelus and the remaining South American chelids to the exclusion of Hydromedusa. Hence, the hypothesis of a monophyletic grouping of the longnecked genera of South America and Australasia is not supported by the molecular data. Although reciprocal monophyly of the South American and Australasian chelid faunas was the most likely and the most parsimonious arrangement in all but one analysis, bootstrap support for the monophyly of the Australasian chelids was low (52-66%). The South American chelids, Chelodina and the short-necked Australasian chelids form an unresolved trichotomy. The genera Phrynops and Elsga are paraphyletic, leading to a recommendation
Exemplar based Generation and Data Augmentation using Exemplar VAEs

This paper combines the advantages of parametric and non-parametric, exemplar based generative models using variational inference, yielding a new generative model called Exemplar VAE. This is a variant of VAE with a non-parametric Parzen window prior in the latent space. To sample from it, one first draws a random exemplar from training data, then stochastically transforms the exemplar into a latent code and a new observation. We propose Retrieval Augmented Training (RAT) that uses approximate nearest neighbor search in the latent space to speed up training based on a novel lower bound on log marginal likelihood. To enhance generalization, model parameters are learned using exemplar leave-one-out and subsampling. Experiments demonstrate the effectiveness of Exemplar VAEs on density estimation and representation learning. Further, generative data augmentation using Exemplar VAEs on permutation invariant MNIST and Fashion MNIST reduces classification error from 1.23 to 0.69 and 8.56 to 8.16.
Abstractive and mixed summarization for long-single documents

The lack of diversity in the datasets available for automatic summarization of documents has meant that the vast majority of neural models for automatic summarization have been trained with news articles. These datasets are relatively small, with an average size of about 600 words, and the models trained with such data sets see their performance limited to short documents. In order to surmount this problem, this paper uses scientific papers as the dataset on which different models are trained. These models have been chosen based on their performance on the CNN/Daily Mail data set, so that the highest ranked model of each architectural variant is selected. In this work, six different models are compared, two with an RNN architecture, one with a CNN architecture, two with a Transformer architecture and one with a Transformer architecture combined with reinforcement learning. The results from this work show that those models that use a hierarchical encoder to model the structure of the document has a better performance than the rest.
Aligning Partially Overlapping Point Sets: an Inner Approximation Algorithm

Aligning partially overlapping point sets where there is no prior information about the value of the transformation is a challenging problem in computer vision. To achieve this goal, we first reduce the objective of the robust point matching algorithm to a function of a low dimensional variable. The resulting function, however, is only concave over a finite region including the feasible region. To cope with this issue, we employ the inner approximation optimization algorithm which only operates within the region where the objective function is concave. Our algorithm does not need regularization on transformation, and thus can handle the situation where there is no prior information about the values of the transformations. Our method is also $\epsilon-$globally optimal and thus is guaranteed to be robust. Moreover, its most computationally expensive subroutine is a linear assignment problem which can be efficiently solved. Experimental results demonstrate the better robustness of the proposed method over state-of-the-art algorithms. Our method is also efficient when the number of transformation parameters is small.
Polyembryony in teleostean fishes: double monstrosities and triplets

The scientific literature pertaining to both naturally occurring and experimentally induced polyembryony in teleosts is surveyed for fish culturists and fisheries naturalists. The survey, covering more than 100 references, provides a chronological historical introduction (17-20th centuries) and a discussion of the causes underlying axial duplication in bony fishes.
Psychotic Features Abated with Vitamin D Treatment in a Patient with Major Depressive Disorder

Introduction Vitamin D deficiency is common in psychiatric populations, and vitamin D has been used as an add-on medication in major depressive disorder. Case Report. Here, we present the case of a 49-year-old man diagnosed with major depressive disorder, severe, with psychotic features, who was treated with antidepressants and underwent multiple antipsychotic trials. The patient was found to have low serum levels of vitamin D. During treatment with vitamin D, serum levels normalized and the patient's psychotic symptoms abated. Conclusion Ensuring adequate vitamin D levels could be a method of focus for the augmentation or treatment of psychotic features in major depressive disorder.
Interventional cardiology in Poland in 2019. Summary report of the Association of Cardiovascular Interventions of the Polish Cardiac Society (AISN PTK) and Jagiellonian University Medical College*

Corresponding author: Zbigniew Siudak MD, PhD, Collegium Medicum, Jan Kochanowski University, 19 IX Wieków Kielc St, 25-516 Kielce, Poland, e-mail: zbigniew.siudak@ujk.edu. pl Received: 10.05. 2020, accepted: 11.05. 2020. Interventional cardiology in Poland in 2019. Summary report of the Association of Cardiovascular Interventions of the Polish Cardiac Society (AISN PTK) and Jagiellonian University Medical College*
DART: Open-Domain Structured Data Record to Text Generation

We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures. To this end, we propose a procedure of extracting semantic triples from tables that encodes their structures by exploiting the semantic dependencies among table headers and the table title. Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and spoken dialogue systems by utilizing techniques including tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing. We present systematic evaluation on DART as well as new state-of-the-art results on WebNLG 2017 to show that DART (1) poses new challenges to existing data-to-text datasets and (2) facilitates out-of-domain generalization. Our data and code can be found at https://github.com/Yale-LILY/dart.
Faster Randomized Primal-Dual Algorithms For Nonsmooth Composite Convex Minimization

We develop two novel randomized primal-dual algorithms to solve nonsmooth composite convex optimization problems. The first algorithm is fully randomized, i.e., it has parallel randomized updates on both primal and dual variables, while the second one is a semi-randomized scheme, which only has one randomized update on the primal (or dual) variable while using the full update for the other. Both algorithms achieve the best-known $\mathcal{O}(1/k)$ or $\mathcal{O}(1/k^2)$ convergence rates in expectation under either only convexity or strong convexity, respectively, where $k$ is the iteration counter. These rates can be obtained for both the primal and dual problems. With new parameter update rules, our algorithms can be boosted up to $\underline{o}(1/(k\sqrt{\log{k}}))$ or $\underline{o}(1/(k^2\sqrt{\log{k}}))$-rates in expectation, respectively (see definitions below). To the best of our knowledge, this is the first time such faster convergence rates are shown for randomized primal-dual methods. Finally, we verify our theoretical results via two numerical examples and compare them with state-of-the-arts.
Autoécologie des chênaies de Quercus robur L. en Galice (Espagne)

Une caracterisation parametrique des biotopes occupes actuellement par des formations de « carballo » (Quercus robur L.) est presentee pour la Galice (Espagne). Pour ceci, on a inventorie un total de 39 parcelles et on a elabore 28 parametres ecologiques caracteristiques du biotope, avec 14 autres parametres qui caracterisent de facon dendrometrique et sylvicole les chenaies inventoriees. Cette information a permis de definir l'habitat physiographique, climatique et edaphique, central et marginal, de Quercus robur en Galice, ainsi que d'etablir la caracterisation de la situation actuelle de ses massifs d'un point de vue dendrometrique et sylvicole. Les chenaies galiciennes presentent une heterogeneite elevee aussi bien dans les biotopes ou elles se situent que dans l'utilisation et la gestion auxquelles elles ont ete soumises. La variation altitudinale est superieure a 1200 m, ceci se traduit par une difference de temperature moyenne de 7,3 °C. Les precipitations moyennes annuelles sont de 137],9 mm, la moyenne estivale atteint 164 mm, en consequence, l'aridite estivale est tres faible. Les sols, en majorite des cambisols, sont fortement acides, avec une composition texturale a a dominante sableuse, ceci est du a la nature des materiaux geologiques (granites et schistes) desquels ils proviennent.
Estimation of Ground Contacts from Human Gait by a Wearable Inertial Measurement Unit using machine learning

Robotics system for rehabilitation of movement disorders and motion assistance are gaining increased intention. In this scenario estimation of ground contact is an active area of research in robotics and healthcare. This article addresses the estimation and classification of right and left foot during the healthy human gait based on the IMU sensor data of chest and lower back. For this purpose we have collected an IMU data of 48 subjects by using two smartphones at chest and lower back of the human body and one smart watch at right ankle of the body. To show the robustness of our approach data was collected at six different surfaces (road tiles carpet grass concrete and soil). The recorded data of lower back and chest sensor was segmented into single steps on the basis of right ankle sensor data, then we computed a total of 408 features from time frequency and wavelet domain of each segmented step. For classification task we have trained two machine learning classifiers SVM and RF with 10 fold cross validation method. We performed classification experiments at individual surfaces, hard surfaces, soft surfaces and all surfaces, highest accuracy was achieved at individual surfaces with accuracy index of 98.88%. Furthermore, classification rate at hard soft and all surface are 95.60%, 94.38% and 95.05% respectively. The results shows that estimation of ground contact form normal human walk at different surfaces can be performed with high accuracy using 6D data of angular velocities and accelerations from chest and lower back location of the body.
Multi-Sensor Next-Best-View Planning as Matroid-Constrained Submodular Maximization

3D scene models are useful in robotics for tasks such as path planning, object manipulation, and structural inspection. We consider the problem of creating a 3D model using depth images captured by a team of multiple robots. Each robot selects a viewpoint and captures a depth image from it, and the images are fused to update the scene model. The process is repeated until a scene model of desired quality is obtained. Next-best-view planning uses the current scene model to select the next viewpoints. The objective is to select viewpoints so that the images captured using them improve the quality of the scene model the most. In this letter, we address next-best-view planning for multiple depth cameras. We propose a utility function that scores sets of viewpoints and avoids overlap between multiple sensors. We show that multi-sensor next-best-view planning with this utility function is an instance of submodular maximization under a matroid constraint. This allows the planning problem to be solved by a polynomial-time greedy algorithm that yields a solution within a constant factor from the optimal. We evaluate the performance of our planning algorithm in simulated experiments with up to 8 sensors, and in real-world experiments using two robot arms equipped with depth cameras.
Automatic semantic segmentation for prediction of tuberculosis using lens-free microscopy images

Tuberculosis (TB), caused by a germ called Mycobacterium tuberculosis, is one of the most serious public health problems in Peru and the world. The development of this project seeks to facilitate and automate the diagnosis of tuberculosis by the MODS method and using lens-free microscopy, due they are easier to calibrate and easier to use (by untrained personnel) in comparison with lens microscopy. Thus, we employ a U-Net network in our collected dataset to perform the automatic segmentation of the TB cords in order to predict tuberculosis. Our initial results show promising evidence for automatic segmentation of TB cords.
Nutrient induced fluorescence transients (NIFTs) provide a rapid measure of P and C (co-)limitation in a green alga

Nutrient Induced Fluorescence Transients (NIFTs) have been shown to be a possible way of testing for the limiting nutrient in algal populations. In this study we tested the hypothesis that NIFTs can be used to detect a (co-)limitation for inorganic phosphorus (Pi) and CO2 in the green alga Chlamydomonas acidophila and that the magnitude of the NIFTs can be related to cellular P:C ratios. We show a co-limitation response for Pi and CO2 via traditional nutrient enrichment experiments in natural phytoplankton populations dominated by C. acidophila. We measured NIFT responses after a Pi- or a CO2-spike in C. acidophila batch cultures at various stages of Pi and inorganic C limitation. Significant NIFTs were observed in response to spikes in both nutrients. The NIFT response to a Pi-spike showed a strong negative correlation with cellular P:C ratio that was pronounced below 3 mmol P: mol C (equivalent to 0.2 pg P cell–1). Both cellular P and C content influenced the extent of the Pi-NIFT response. The NIFT response to a CO2-spike correlated to low CO2 culturing conditions and also had a negative correlation with cellular P content. A secondary response within the Pi-NIFT response was related to the CO2 concentration and potentially reflected co-limitation. In conclusion, NIFTs provided a quick and reliable method to detect the growth-limiting nutrient in an extremophile green alga, under Pi-, CO2- and Pi/CO2 (co-)limited growth conditions.
Bidirectional Model-based Policy Optimization

Model-based reinforcement learning approaches leverage a forward dynamics model to support planning and decision making, which, however, may fail catastrophically if the model is inaccurate. Although there are several existing methods dedicated to combating the model error, the potential of the single forward model is still limited. In this paper, we propose to additionally construct a backward dynamics model to reduce the reliance on accuracy in forward model predictions. We develop a novel method, called Bidirectional Model-based Policy Optimization (BMPO) to utilize both the forward model and backward model to generate short branched rollouts for policy optimization. Furthermore, we theoretically derive a tighter bound of return discrepancy, which shows the superiority of BMPO against the one using merely the forward model. Extensive experiments demonstrate that BMPO outperforms state-of-the-art model-based methods in terms of sample efficiency and asymptotic performance.
Online Regularization for High-Dimensional Dynamic Pricing Algorithms

We propose a novel \textit{online regularization} scheme for revenue-maximization in high-dimensional dynamic pricing algorithms. The online regularization scheme equips the proposed optimistic online regularized maximum likelihood pricing (\texttt{OORMLP}) algorithm with three major advantages: encode market noise knowledge into pricing process optimism; empower online statistical learning with always-validity over all decision points; envelop prediction error process with time-uniform non-asymptotic oracle inequalities. This type of non-asymptotic inference results allows us to design safer and more robust dynamic pricing algorithms in practice. In theory, the proposed \texttt{OORMLP} algorithm exploits the sparsity structure of high-dimensional models and obtains a logarithmic regret in a decision horizon. These theoretical advances are made possible by proposing an optimistic online LASSO procedure that resolves dynamic pricing problems at the \textit{process} level, based on a novel use of non-asymptotic martingale concentration. In experiments, we evaluate \texttt{OORMLP} in different synthetic pricing problem settings and observe that \texttt{OORMLP} performs better than \texttt{RMLP} proposed in \cite{javanmard2019dynamic}.
The nonavian theropod quadrate I: standardized terminology and overview of the anatomy, function and ontogeny

The quadrate of reptiles and most other tetrapods plays an important morphofunctional role by allowing the articulation of the mandible with the cranium. In Theropoda, the morphology of the quadrate is particularly complex and varies importantly among different clades of non-avian theropods, therefore conferring it a strong taxonomic potential. Inconsistencies in the notation and terminology used in discussions of the theropod quadrate anatomy have been noticed, namely a number of no less than eight different terms being sometimes given to the same structure. A standardized list of terms and notations for each quadrate anatomical entity is proposed here, with the goal of facilitating future descriptions of this important cranial bone. In addition, an overview of the literature on the quadrate function, pneumaticity and ontogeny in non-avian theropods is also presented, along with the inferences that could be made from this research. Specifically, the quadrate of the large majority of non-avian theropods is akinetic but the diagonally oriented intercondylar sulcus of the mandibular articulation allowed both rami of the mandible to move laterally when opening the mouth in many of theropods. Pneumaticity of the quadrate is also present in most tetanuran clades and the pneumatic chamber—invaded by the quadrate diverticulum of the mandibular arch pneumatic system—was connected to one or several pneumatic foramina on the medial, lateral, posterior, anterior or ventral sides of the quadrate. Absence of a quadrate foramen and a poor delimitation of mandibular condyles seem to be ontogenetic features of some tetanurans.