Miracle-dz/UniFAR-FTUs huggingface dataset:
DROP-ON-DEMAND INKJET BIOPRINTING: A PRIMER ∗

Engineering complex biological structures for regenerative medicine, in vitro tissue analysis, and pharmaceutical testing require new fabrication techniques that can place specific cells in specific target locations. Conventional cell seeding methods cannot achieve this level of spatial resolution. Biofabrication is a rapidly advancing field that uses a variety of delivery mechanisms to achieve the spatial resolution necessary to place cells, biomaterials, and bioactive macromolecules in specific target locations. One new technique within this field is bioprinting, which uses drop-on-demand delivery mechanisms to fabricate biological structures. This review focuses on drop-on-demand inkjet bioprinting and provides a primer for researchers seeking to enter the field.
NINEPINS: Nuclei Instance Segmentation with Point Annotations

Deep learning-based methods are gaining traction in digital pathology, with an increasing number of publications and challenges that aim at easing the work of systematically and exhaustively analyzing tissue slides. These methods often achieve very high accuracies, at the cost of requiring large annotated datasets to train. This requirement is especially difficult to fulfill in the medical field, where expert knowledge is essential. In this paper we focus on nuclei segmentation, which generally requires experienced pathologists to annotate the nuclear areas in gigapixel histological images. We propose an algorithm for instance segmentation that uses pseudo-label segmentations generated automatically from point annotations, as a method to reduce the burden for pathologists. With the generated segmentation masks, the proposed method trains a modified version of HoVer-Net model to achieve instance segmentation. Experimental results show that the proposed method is robust to inaccuracies in point annotations and comparison with Hover-Net trained with fully annotated instance masks shows that a degradation in segmentation performance does not always imply a degradation in higher order tasks such as tissue classification.
Role of social and endocrine factors in alloparental behavior of prairie voles (Microtus ochrogaster)

Young, sexually naive prairie voles (Microtus ochrogaster), 21-60 days of age, of both sexes readily exhibit alloparental behavior toward pups without apparent hormonal or experiential priming. The goal of the present study was to quantify the incidence of spontaneously evoked alloparental behavior in young prairie voles and determine prior pup experience (i), gender-related (ii) and age-related (iii) characteristics, and hormonal (iv) and housing (v) conditions associated with alloparental behavior. Overall, 70% of all prairie voles between 21 and 60 days of age exhibited alloparental behavior regardless of hormonal condition or postweaning housing condition (single versus sib-group housing). Experience with pups prior to weaning was associated with a greater percentage of prairie voles exhibiting alloparental responding in comparison with prairie voles that had never been exposed to pups. Male prairie voles were more likely to be alloparental than were females, although most females (64%) exhibited allo.
Lightweight Cryptography for IoT: A State-of-the-Art

With the emergence of 5G, Internet of Things (IoT) has become a center of attraction for almost all industries due to its wide range of applications from various domains. The explosive growth of industrial control processes and the industrial IoT, imposes unprecedented vulnerability to cyber threats in critical infrastructure through the interconnected systems. This new security threats could be minimized by lightweight cryptography, a sub-branch of cryptography, especially derived for resource-constrained devices such as RFID tags, smart cards, wireless sensors, etc. More than four dozens of lightweight cryptography algorithms have been proposed, designed for specific application(s). These algorithms exhibit diverse hardware and software performances in different circumstances. This paper presents the performance comparison along with their reported cryptanalysis, mainly for lightweight block ciphers, and further shows new research directions to develop novel algorithms with right balance of cost, performance and security characteristics.
Mmp2 as a Regulatory Target of ßftz-f1 in Drosophila melanogaster Larval Fat Body Remodeling

Tissue remodeling is an important process by which cells dissociate and detach from one another. This phenomenon is involved in both healthy and disease driven functions in the human body including wound healing, and cancer metastasis. Tissue remodeling is carried out in part by Matrix Metalloproteinases (MMPs), proteases that cleave bonds in extracellular matrix (ECM) allowing cells to dissociate and detach from one another, enabling them to move to new locations. Due to the potentially dangerous actions of Mmps, their expression is tightly regulated. Drosophila melanogaster is a useful model organism for studying tissue remodeling and the regulation of Mmp expression through the examination of larval fat body remodeling. Drosophila larval fat body remodeling occurs during the transition from larva to adult fly. It is a tightly regulated process believed to involve the MMP Matrix Metalloproteinase 2 (MMP2). MMP2 is thought to cleave fat cell ECM proteins allowing cells to detach from each other in the process of remodeling. Previous research suggests that Mmp2 expression in Drosophila is regulated by a sFTZ-F1-mediated, 20-hydroxyecdysone (ecdysone) hormonal cascade (Bond et al. 2011). Both sftz-f1 and Mmp2 have been demonstrated as necessary and sufficient to induce Drosophila larval fat body remodeling (Bond et al. 2011). This study investigates the hypothesis of Mmp2 as a downstream regulatory target of sFTZ-F1 as a part of an ecdysone hormonal cascade in Drosophila larval fat body remodeling. Specifically, I hypothesize that sFTZ-F1 induces Mmp2 expression. Investigations were carried out through relative quantification of Mmp2 (as compared to a control gene) expression in wild type Drosophila fat body as compared to fat body in which sftz-f1 expression has been reduced. I expected reduced expression of Mmp2 in sftz-f1-reduced fat body as compared to wild type fat body. If Mmp2 expression is indeed induced by sftz-f1 expression, a reduction in sftz-f1 expression will result in a corresponding reduction in Mmp2 expression. Findings demonstrated a reduction in Mmp2 expression in sftz-f1-reduced fat body aged 10 hours after puparium formation (APF). These results are consistent with the hypothesis that Mmp2 expression is induced by sftz-f1 expression. Findings also demonstrated an increase in Mmp2 expression in sftz-f1-reuced fat body aged 8 and 12 hours APF. These results are counter to the hypothesis. Because only one trial has been done, these results are highly preliminary.
Approximate Bayesian Uncertainties on Deep Learning Dynamical Mass Estimates of Galaxy Clusters

We study methods for reconstructing Bayesian uncertainties on dynamical mass estimates of galaxy clusters using convolutional neural networks (CNNs). We discuss the statistical background of approximate Bayesian neural networks and demonstrate how variational inference techniques can be used to perform computationally tractable posterior estimation for a variety of deep neural architectures. We explore how various model designs and statistical assumptions impact prediction accuracy and uncertainty reconstruction in the context of cluster mass estimation. We measure the quality of our model posterior recovery using a mock cluster observation catalog derived from the MultiDark simulation and UniverseMachine catalog. We show that approximate Bayesian CNNs produce highly accurate dynamical cluster mass posteriors. These model posteriors are log-normal in cluster mass and recover 68% and 90% confidence intervals to within 1% of their measured value. We note how this rigorous modeling of dynamical mass posteriors is necessary for using cluster abundance measurements to constrain cosmological parameters.
Spectrum formation in X-ray pulsars at very low mass accretion rate: Monte Carlo approach

It has been recently discovered that the transition of X-ray pulsars to the low luminosity state ($L\lesssim 10^{35}\,{\rm erg\,s^{-1}}$) is accompanied by a dramatic spectral changes. Namely, the typical power-law-like spectrum with high energy cutoff transforms into a two-component structure with a possible cyclotron absorption feature on top of it. It was proposed that these spectral characteristics can be explained qualitatively by the emission of cyclotron photons in the atmosphere of the neutron star caused by collisional excitation of electrons to upper Landau levels and further comptonization of the photons by electron gas. The latter is expected to be overheated in a thin top layer of the atmosphere. In this paper, we perform Monte Carlo simulations of the radiative transfer in the atmosphere of an accreting neutron star while accounting for a resonant scattering of polarized X-ray photons by thermally distributed electrons. The spectral shape is shown to be strongly polarization-dependent in soft X-rays ($\lesssim 10\,{\rm keV}$) and near the cyclotron scattering feature. The results of our numerical simulations are tested against the observational data of X-ray pulsar A 0535+262 in the low luminosity state. We show that the spectral shape of the pulsar can be reproduced by the proposed theoretical model. The applications of the discovery to the observational studies of accreting neutron stars are discussed.
Preliminary list of Odonata from the Colombian Amazon, with descriptions of Inpabasis nigridorsum sp. nov. & Diaphlebia richteri sp. nov. (Coenagrionidae & Gomphidae)

The Colombian Amazon is one of the most biodiverse and unexplored regions in the world. Inventories and deeper research are needed for most of its biota, including for dragonflies. This work reports the results of a trip to the Amazon region in order to collect Odonata. It includes revision of CBUCES, CEUA and ICN entomological collections and a literature survey of Colombian Amazon Odonata. Two undescribed species in the genera Diaphlebia and Inpabasis were found. Five genera and 21 species are recorded for the first time in Colombia. Accounts for undescribed species, new records for the country, natural history notes and a discussed list of Colombian Amazon Odonata are provided. http://zoobank.org/urn:lsid:zoobank.org:pub:6E6FF0A0-28AC-454B-A36B-BCC6F120636B
Holography from Decoherence and Entanglement

We describe an explicit mechanism for the emergence of a dynamical holographic bulk from the structure of entanglement in a quantum state. We start with a generic system in complete isolation, assuming it has a classical limit involving coherent states. Then we entangle it with another system of that kind, and subject the pair to a decohering process. We make a number of broadly applicable and physically reasonable assumptions about this setup. First, we assume that the states selected by the decoherence (called pointer states) have the same local symmetries as the isolated systems, in a sense which is made precise. We also assume that the modular Hamiltonians of pointer states scale inversely with Planck's constant, so that the pointer states are highly entangled in the classical limit. Finally, we require the timescale of decoherence to scale in a certain way with Planck's constant, so that decoherence happens very frequently in the classical limit, but not too frequently. Given these assumptions, we demonstrate that the semiclassical evolution of the system is dominated by a certain dynamical generalisation of Uhlmann holonomy. We construct a coherent state path integral for this evolution, showing that the semiclassical fields evolve in a spacetime with one more dimension than the isolated case. The additional dimension is generated by modular flow.
Generative causal explanations of black-box classifiers

We develop a method for generating causal post-hoc explanations of black-box classifiers based on a learned low-dimensional representation of the data. The explanation is causal in the sense that changing learned latent factors produces a change in the classifier output statistics. To construct these explanations, we design a learning framework that leverages a generative model and information-theoretic measures of causal influence. Our objective function encourages both the generative model to faithfully represent the data distribution and the latent factors to have a large causal influence on the classifier output. Our method learns both global and local explanations, is compatible with any classifier that admits class probabilities and a gradient, and does not require labeled attributes or knowledge of causal structure. Using carefully controlled test cases, we provide intuition that illuminates the function of our causal objective. We then demonstrate the practical utility of our method on image recognition tasks.
Molecular characterisation of SGCE-associated myoclonus-dystonia and PINK1-associated Parkinson's disease

S 1. Grünewald A, Djarmati A, Lohmann-Hedrich K, Farrell K, Klein C. Phenotypes and genotypes in myoclonus-dystonia: Significance of deletions of the SGCE gene. Mov Disord 2007; 22 (Suppl. 16): A442. 2. Fung VSC, Mahant N, Sue CM, Grünewald A, Klein C. Autosomal dominant myoclonus dystonia: Unusual phenotype with prominent hypotonia/motor impersistence and positive celiac serology. Mov Disord 2007; 22 (Suppl.16): A143. 3. Grünewald A, Gegg ME, Taanman JW, Schapira AHV, Klein C. Mitochondriale Funktion und Morphologie bei PINK1-assoziiertem Parkinson-Syndrom. DGN 2008. 4. Grünewald A, Gegg ME, Taanman JW, Klein C, Schapira AHV. Differential effect of PINK1 nonsense and missense mutations on mitochondrial function and morphology. DGNG 2008. Accompanying booklet: P6. ORAL PRESENTATIONS 1. Grünewald A, Djarmati A, Lohmann-Hedrich K, Farrell K, Klein C. Phenotypes and genotypes in myoclonus-dystonia: Significance of deletions of the SGCE gene. 11th Annual Congress of the Movement Disorders Society: Highlights of Posters Sessions 1, Istanbul, Turkey, June 2007.
RESEARCH CONCERNING THE BIOLOGICAL CONTROL OF CODLING MOTH (CYDIA POMONELLA) USING THE ENTOMOPHAGOUS WASP TRICHOGRAMMA SP. IN THE CARANSEBES POMICULTURAL BASIN

The product Trichotim has as a principle the insect Trichogramma sp. Trichogramma is a wasp measuring only 0.5 mm. It lays its eggs in the pests' eggs, destroying them before they can damage the plants. Trichogramma is efficient in the destruction of 500 species of pests; the product developed based on this wasp has been homologated in Romania only for three such pests: cabbage moth (Mamestra brassicae), European grapevine moth (Lobesia botrana) and codling moth (Cydia pomonella). This wasp species, almost invisible, is extremely widespread. The main issue for researchers was how to capture it from the wild and then how to create the conditions necessary to make it multiply in an artificial environment. The product Trichotim has been tested for four years, an interval during which Trichogramma showed its efficacy in both laboratory cultures and in the cabbage gardens of some farmers in the Arad County to whom it was offered as a promotion. The costs are higher than in a treatment with chemical substances, but the cultures we obtained were less risky for our health. One gram contains about 50.000 eggs of Trichogramma, an amount sufficient to store the crop from 1 ha of cabbage, of grapevine or of apple trees. The product Trichotim is used in the control of pests of grains, maize, sunflower, rape, cabbage, peas, clover, alfalfa, grapevine, apple tree, plum tree, and ornamental plants.
Community‐based data integration of course and job data in support of personalized career‐education recommendations

How does your education impact your professional career? Ideally, the courses you take help you identify, get hired for, and perform the job you always wanted. However, not all courses provide skills that transfer to existing and future jobs; skill terms used in course descriptions might be different from those listed in job advertisements; and there might exist a considerable skill gap between what is taught in courses and what is needed for a job. In this study, we propose a novel method to integrate extensive course description and job advertisement data by leveraging heterogeneous data integration and community detection. The innovative heterogeneous graph approach along with identified skill communities enables cross‐domain information recommendation, for example, given an educational profile, job recommendations can be provided together with suggestions on education opportunities for re‐ and upskilling in support of lifelong learning. Note: This work was partially supported by the National Science Foundation under award 1,936,656. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF.
Supervised Understanding of Word Embeddings

Pre-trained word embeddings are widely used for transfer learning in natural language processing. The embeddings are continuous and distributed representations of the words that preserve their similarities in compact Euclidean spaces. However, the dimensions of these spaces do not provide any clear interpretation. In this study, we have obtained supervised projections in the form of the linear keyword-level classifiers on word embeddings. We have shown that the method creates interpretable projections of original embedding dimensions. Activations of the trained classifier nodes correspond to a subset of the words in the vocabulary. Thus, they behave similarly to the dictionary features while having the merit of continuous value output. Additionally, such dictionaries can be grown iteratively with multiple rounds by adding expert labels on top-scoring words to an initial collection of the keywords. Also, the same classifiers can be applied to aligned word embeddings in other languages to obtain corresponding dictionaries. In our experiments, we have shown that initializing higher-order networks with these classifier weights gives more accurate models for downstream NLP tasks. We further demonstrate the usefulness of supervised dimensions in revealing the polysemous nature of a keyword of interest by projecting it's embedding using learned classifiers in different sub-spaces.
Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization

In unmanned aerial vehicle (UAV) applications, the UAV's limited energy supply and storage have triggered the development of intelligent energy-conserving scheduling solutions. In this paper, we investigate energy minimization for UAV-aided communication networks by jointly optimizing data-transmission scheduling and UAV hovering time. The formulated problem is combinatorial and non-convex with bilinear constraints. To tackle the problem, firstly, we provide an optimal algorithm (OPT) and a golden section search heuristic algorithm (GSS-HEU). Both solutions are served as offline performance benchmarks which might not be suitable for online operations. Towards this end, from a deep reinforcement learning (DRL) perspective, we propose an actor-critic-based deep stochastic online scheduling (AC-DSOS) algorithm and develop a set of approaches to confine the action space. Compared to conventional RL/DRL, the novelty of AC-DSOS lies in handling two major issues, i.e., exponentially-increased action space and infeasible actions. Numerical results show that AC-DSOS is able to provide feasible solutions, and save around 25-30% energy compared to two conventional deep AC-DRL algorithms. Compared to the developed GSS-HEU, AC-DSOS consumes around 10% higher energy but reduces the computational time from second-level to millisecond-level.
CRUDE: Calibrating Regression Uncertainty Distributions Empirically

The importance of calibrated uncertainty estimates in machine learning is growing apparent across many fields such as autonomous vehicles, medicine, and weather and climate forecasting. While there is extensive literature on uncertainty calibration for classification, the classification findings do not always translate to regression. As a result, modern models for predicting uncertainty in regression settings typically produce uncalibrated and overconfident estimates. To address these gaps, we present a calibration method for regression settings that does not assume a particular uncertainty distribution over the error: Calibrating Regression Uncertainty Distributions Empirically (CRUDE). CRUDE makes the weaker assumption that error distributions have a constant arbitrary shape across the output space, shifted by predicted mean and scaled by predicted standard deviation. CRUDE requires no training of the calibration estimator, aside from a parameter to account for fixed bias in the predicted mean. Across an extensive set of regression tasks, CRUDE demonstrates consistently sharper, better calibrated, and more accurate uncertainty estimates than state-of-the-art techniques.
An Improved DOA Estimation Method for a Mixture of Circular and Non-Circular Signals Based on Sparse Arrays.

Sparse arrays have attracted a lot of interests recently for their capability of providing more degrees of freedom than traditional uniform linear arrays. For a mixture of circular and noncircular signals, most of the existing direction of arrival (DOA) estimation methods are based on various uniform arrays. Recently, a class of DOA estimation algorithms based on sparse arrays was developed for a mixture of circular and noncircular signals. To further improve its performance, in this work, a modified algorithm is presented, which can resolve the same number of signals, and simulation results are provided to verified its performance.
Corrigendum and Supplement to "Improve Language Modelling for Code Completion through Learning General Token Repetition of Source Code (with Optimized Memory)"

This paper is written because I receive several inquiry emails saying it is hard to achieve good results when applying token repetition learning techniques. If REP (proposed by me) or Pointer-Mixture (proposed by Jian Li) is directly applied to source code to decide all token repetitions, the model performance will decrease sharply. As we use pre-order traversal to traverse the Abstract Syntax Tree (AST) to generate token sequence, tokens corresponding to AST grammar are ignored when learning token repetition. For non-grammar tokens, there are many kinds: strings, chars, numbers and identifiers. For each kind of tokens, we try to learn its repetition pattern and find that only identifiers have the property of token repetition. For identifiers, there are also many kinds such as variables, package names, method names, simple types, qualified types or qualified names. Actually, some kinds of identifiers such as package names, method names, qualified names or qualified types are unlikely to be repeated. Thus, we ignore these kinds of identifiers that are unlikely to be repeated when learning token repetition. This step is crucial and this important implementation trick is not clearly presented in the paper because we think it is trivial and too many details may bother readers. We offer the GitHub address of our model in our conference paper and readers can check the description and implementation in that repository. Thus, in this paper, we supplement the important implementation optimization details for the already published papers.