Miracle-dz/UniFAR-FTUs huggingface dataset:
New optically identified supernova remnants in the Large Magellanic Cloud

We present a new optical sample of three Supernova Remnants and 16 Supernova Remnant (SNR) candidates in the Large Magellanic Cloud(LMC). These objects were originally selected using deep H$\alpha$, [SII] and [OIII] narrow-band imaging. Most of the newly found objects are located in less dense regions, near or around the edges of the LMC's main body. Together with previously suggested MCSNR J0541-6659, we confirm the SNR nature for two additional new objects: MCSNR J0522-6740 and MCSNRJ0542-7104. Spectroscopic follow-up observations for 12 of the LMC objects confirm high [SII]/H$\alpha$ a emission-line ratios ranging from 0.5 to 1.1. We consider the candidate J0509-6402 to be a special example of the remnant of a possible Type Ia Supernova which is situated some 2$^\circ$ ($\sim 1.75$kpc) north from the main body of the LMC. We also find that the SNR candidates in our sample are significantly larger in size than the currently known LMC SNRs by a factor of $\sim 2$. This could potentially imply that we are discovering a previously unknown but predicted, older class of large LMC SNRs that are only visible optically. Finally, we suggest that most of these LMC SNRs are residing in a very rarefied environment towards the end of their evolutionary span where they become less visible to radio and X-ray telescopes.
Designing Interpretable Approximations to Deep Reinforcement Learning with Soft Decision Trees

In an ever expanding set of research and application areas, deep neural networks (DNNs) set the bar for algorithm performance. However, depending upon additional constraints such as processing power and execution time limits, or requirements such as verifiable safety guarantees, it may not be feasible to actually use such high-performing DNNs in practice. Many techniques have been developed in recent years to compress or distill complex DNNs into smaller, faster or more understandable models and controllers. This work seeks to provide a quantitative framework with metrics to systematically evaluate the outcome of such conversion processes, and identify reduced models that not only preserve a desired performance level, but also, for example, succinctly explain the latent knowledge represented by a DNN. We illustrate the effectiveness of the proposed approach on the evaluation of decision tree variants in the context of benchmark reinforcement learning tasks.
Effective Decoder Masking for Transformer Based End-to-End Speech Recognition.

The attention-based encoder-decoder modeling paradigm has achieved promising results on a variety of speech processing tasks like automatic speech recognition (ASR), text-to-speech (TTS) and among others. This paradigm takes advantage of the generalization ability of neural networks to learn a direct mapping from an input sequence to an output sequence, without recourse to prior knowledge such as audio-text alignments or pronunciation lexicons. However, ASR models stemming from this paradigm are prone to overfitting, especially when the training data is limited. Inspired by SpecAugment and BERT-like masked language modeling, we propose in the paper a decoder masking based training approach for end-to-end (E2E) ASR models. During the training phase we randomly replace some portions of the decoder's historical text input with the symbol [mask], in order to encourage the decoder to robustly output a correct token even when parts of its decoding history are masked or corrupted. The proposed approach is instantiated with the top-of-the-line transformer-based E2E ASR model. Extensive experiments on the Librispeech960h and TedLium2 benchmark datasets demonstrate the superior performance of our approach in comparison to some existing strong E2E ASR systems.
Provably Efficient Online Agnostic Learning in Markov Games

We study online agnostic learning, a problem that arises in episodic multi-agent reinforcement learning where the actions of the opponents are unobservable. We show that in this challenging setting, achieving sublinear regret against the best response in hindsight is statistically hard. We then consider a weaker notion of regret, and present an algorithm that achieves after $K$ episodes a sublinear $\tilde{\mathcal{O}}(K^{3/4})$ regret. This is the first sublinear regret bound (to our knowledge) in the online agnostic setting. Importantly, our regret bound is independent of the size of the opponents' action spaces. As a result, even when the opponents' actions are fully observable, our regret bound improves upon existing analysis (e.g., (Xie et al., 2020)) by an exponential factor in the number of opponents.
Factors Involved in Pollen Germination Playing a Critical Role also in Allergic Sensitization

Pollen is a useful system for the studies of tip growth and cell wall deposition. For this reason, it was chosen as a promising model for studying how external stimuli are integrated within the cell and how several signalling molecules are interconnected. In particular, understanding how the overall shaping of the pollen tube tip and how several well-known factors are interconnected were the main goals of this study. As the proper growth of the pollen tube depends on an elaborate mechanism that integrates several molecular and cytological sub-processes its growth mechanism is controlled by several signaling molecules such as polyamines (PAs), which control different aspects of pollen tube germination. In pollen, the homeostasis of PAs is finely regulated and the perturbation of this balance has provided, over the years, interesting evidences about how PAs carry out some of their functions in the cell. For these reasons, PAs ware taken into consideration to be used as perturbing molecules, used to spread light in understanding not only the reciprocal interactions among factors, but also the timing of the events. Besides the great interest about pollen as a model of study, pollen is a relevant topic also because it is the main trigger of seasonal allergies. It often happens that the only “pollen count” does not completely mirror the potential allergenicity of the air. Here from, the necessity to integrate the forecasting of allergenic pollen in the atmosphere with the monitoring of airborne allergenic proteins. In these studies, the amount of airborne allergens were put in correlation not only with pollen counts but also with meteorological factors in order to obtain a broad panel of evidences about factors that may affect pollen and allergen dispersion. Finally, in vitro studies also focused on the characterization of allergens, deeply involved in pollen cell wall organization.
Machine learning of solvent effects on molecular spectra and reactions

Fast and accurate simulation of complex chemical systems in environments such as solutions is a long standing challenge in theoretical chemistry. In recent years, machine learning has extended the boundaries of quantum chemistry by providing highly accurate and efficient surrogate models of electronic structure theory, which previously have been out of reach for conventional approaches. Those models have long been restricted to closed molecular systems without accounting for environmental influences, such as external electric and magnetic fields or solvent effects. Here, we introduce the deep neural network FieldSchNet for modeling the interaction of molecules with arbitrary external fields. FieldSchNet offers access to a wealth of molecular response properties, enabling it to simulate a wide range of molecular spectra, such as infrared, Raman and nuclear magnetic resonance. Beyond that, it is able to describe implicit and explicit molecular environments, operating as a polarizable continuum model for solvation or in a quantum mechanics/molecular mechanics setup. We employ FieldSchNet to study the influence of solvent effects on molecular spectra and a Claisen rearrangement reaction. Based on these results, we use FieldSchNet to design an external environment capable of lowering the activation barrier of the rearrangement reaction significantly, demonstrating promising venues for inverse chemical design.
Resonant sub-GeV Dirac dark matter

We study the phenomenology and detection prospects of a sub-GeV Dirac dark matter candidate with resonantly enhanced annihilations via a dark photon mediator. The model evades cosmological constraints on light thermal particles in the early universe while simultaneously being in reach of current and upcoming terrestrial experiments. We conduct a global analysis of the parameter space, considering bounds from accelerator and direct detection experiments, as well as those arising from Big Bang Nucleosynthesis, the Cosmic Microwave Background and dark matter self-interactions. We also extend our discussion to the case of a dark matter subcomponent. We find that large regions of parameter space remain viable even for the case of a moderate resonant enhancement, and demonstrate the complementarity of different experimental strategies for further exploring this scenario.
Observations of wavefunction collapse and the retrospective application of the Born rule.

In this paper I present a thought experiment that gives different results depending on whether or not the wavefunction collapses. Since the wavefunction does not obey the Schrodinger equation during the collapse, conservation laws are violated. This is the reason why the results are different. Quantities that are conserved if the wavefunction does not collapse might change if it does. I also show that using the Born Rule to derive probabilities of states before a measurement given the state after it (rather than the other way round as it is usually used) leads to the conclusion that the memories that an observer has about making measurements of quantum systems have a significant probability of being false memories.
Predicting Themes within Complex Unstructured Texts: A Case Study on Safeguarding Reports

The task of text and sentence classification is associated with the need for large amounts of labelled training data. The acquisition of high volumes of labelled datasets can be expensive or unfeasible, especially for highly-specialised domains for which documents are hard to obtain. Research on the application of supervised classification based on small amounts of training data is limited. In this paper, we address the combination of state-of-the-art deep learning and classification methods and provide an insight into what combination of methods fit the needs of small, domain-specific, and terminologically-rich corpora. We focus on a real-world scenario related to a collection of safeguarding reports comprising learning experiences and reflections on tackling serious incidents involving children and vulnerable adults. The relatively small volume of available reports and their use of highly domain-specific terminology makes the application of automated approaches difficult. We focus on the problem of automatically identifying the main themes in a safeguarding report using supervised classification approaches. Our results show the potential of deep learning models to simulate subject-expert behaviour even for complex tasks with limited labelled data.
miR-128-induced LINE-1 restriction is dependent on down-regulation of hnRNPA1

The majority of the human genome is made of transposable elements, giving rise to interspaced repeats, including Long Interspersed Element-1s (LINE-1s or L1s). L1s are active human DNA parasites involved in genomic diversity and evolution, but can also contribute to genomic instability and diseases. L1s require host factors to complete their life cycles, whereas the host has evolved numerous mechanisms to restrict L1-induced mutagenesis. Restriction mechanisms in somatic cells include methylation of the L1 promoter, anti-viral factors and RNA-mediated processes such as small RNAs. microRNAs (miRNAs or miRs) are small non-coding RNAs that post-transcriptionally repress multiple target genes often found in the same cellular pathways. We have recently established that the interferon-inducible miR-128 function as a novel restriction factor inhibiting L1 mobilization in somatic cells. We have further demonstrated that miR-128 function through a dual mechanism; by directly targeting L1 RNA for degradation and indirectly by inhibiting a cellular co-factor which L1 is dependent on to transpose to new genomic locations (TNPO1). Here we add another piece to the puzzle of the enigmatic L1 life cycle. We show that miR-128 also inhibits another key cellular factor, hnRNPA1, by significantly reducing mRNA and protein levels through direct interaction with the coding sequence (CDS) of hnRNPA1 mRNA. Furthermore, we demonstrate that repression of hnRNPA1 using shRNA significantly decreases de novo L1 retrotransposition and that induced hnRNPA1 expression enhances L1 mobilization. Finally, we determine that hnRNPA1 is a functional target of miR-128 and that induced hnRNPA1 expression in miR-128-overexpressing cells can partly rescue the miR-128-induced repression of L1’s ability to transpose to different genomic locations. Thus, we have identified an additional mechanism by which miR-128 represses L1 retrotransposition and mediate genomic stability.
Elainella gen. nov.: a new tropical cyanobacterium characterized using a complex genomic approach

ABSTRACT Cyanobacteria represent an ancient, monophyletic lineage of bacteria with the ability to undertake oxygenic photosynthesis. Although they possess a relatively high degree of morphological variability compared with other prokaryotes and there is a wealth of molecular data, there are still significant gaps in our knowledge of cyanobacterial diversity, especially in tropical areas. Here, we present a novel, filamentous, tropical cyanobacterium, which could be classified as Pseudophormidium based on morphological criteria. A total evidence investigation employing ecological, morphological and genomic data, indicated that our strains form a new and ancient evolutionary lineage among cyanobacteria unrelated to Pseudophormidium. Based on this polyphasic assessment, our strains represent a novel, monospecific genus: Elainella. This new genus represents an example of phenotypic convergence, which seems to be a prevalent macroevolutionary pattern in cyanobacteria, a likely cause of the frequently cited polyphyly within a majority of genera.
DNA Microarray Data a Major Challenge

DNA microarray technologies now make it possible to measure the expression levels of thousands of genes simultaneously during cellular differentiation over time or different cell lines. A major challenge now is to understand and explore the patterns in this massive data sets. In this article, first we give a brief description of the array technology and examine, why it is considered to be so important in post genome sequencing era. Then we review various clustering methods that have been recently used to explore the gene expression data.
COVID-19 in Spain and India: Comparing Policy Implications by Analyzing Epidemiological and Social Media Data

The COVID-19 pandemic has forced public health experts to develop contingent policies to stem the spread of infection, including measures such as partial/complete lockdowns. The effectiveness of these policies has varied with geography, population distribution, and effectiveness in implementation. Consequently, some nations (e.g., Taiwan, Haiti) have been more successful than others (e.g., United States) in curbing the outbreak. A data-driven investigation into effective public health policies of a country would allow public health experts in other nations to decide future courses of action to control the outbreaks of disease and epidemics. We chose Spain and India to present our analysis on regions that were similar in terms of certain factors: (1) population density, (2) unemployment rate, (3) tourism, and (4) quality of living. We posit that citizen ideology obtainable from twitter conversations can provide insights into conformity to policy and suitably reflect on future case predictions. A milestone when the curves show the number of new cases diverging from each other is used to define a time period to extract policy-related tweets while the concepts from a causality network of policy-dependent sub-events are used to generate concept clouds. The number of new cases is predicted using sentiment scores in a regression model. We see that the new case predictions reflects twitter sentiment, meaningfully tied to a trigger sub-event that enables policy-related findings for Spain and India to be effectively compared.
Addressing Purchase-Impression Gap through a Sequential Re-ranker

Large scale eCommerce platforms such as eBay carry a wide variety of inventory and provide several buying choices to online shoppers. It is critical for eCommerce search engines to showcase in the top results the variety and selection of inventory available, specifically in the context of the various buying intents that may be associated with a search query. Search rankers are most commonly powered by learning-to-rank models which learn the preference between items during training. However, they score items independent of other items at runtime. Although the items placed at top of the results by such scoring functions may be independently optimal, they can be sub-optimal as a set. This may lead to a mismatch between the ideal distribution of items in the top results vs what is actually impressed. In this paper, we present methods to address the purchase-impression gap observed in top search results on eCommerce sites. We establish the ideal distribution of items based on historic shopping patterns. We then present a sequential reranker that methodically reranks top search results produced by a conventional pointwise scoring ranker. The reranker produces a reordered list by sequentially selecting candidates trading off between their independent relevance and potential to address the purchase-impression gap by utilizing specially constructed features that capture impression distribution of items already added to a reranked list. The sequential reranker enables addressing purchase impression gap with respect to multiple item aspects. Early version of the reranker showed promising lifts in conversion and engagement metrics at eBay. Based on experiments on randomly sampled validation datasets, we observe that the reranking methodology presented produces around 10% reduction in purchase-impression gap at an average for the top 20 results, while making improvements to conversion metrics.
Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference

The Bayesian paradigm has the potential to solve some of the core issues in modern deep learning, such as poor calibration, data inefficiency, and catastrophic forgetting. However, scaling Bayesian inference to the high-dimensional parameter spaces of deep neural networks requires restrictive approximations. In this paper, we propose performing inference over only a small subset of the model parameters while keeping all others as point estimates. This enables us to use expressive posterior approximations that would otherwise be intractable for the full model. In particular, we develop a practical and scalable Bayesian deep learning method that first trains a point estimate, and then infers a full covariance Gaussian posterior approximation over a subnetwork. We propose a subnetwork selection procedure which aims to optimally preserve posterior uncertainty. We empirically demonstrate the effectiveness of our approach compared to point-estimated networks and methods that use less expressive posterior approximations over the full network.
Integrated Nutrient Management for Improved Cauliflower Yield and Soil Health

ABSTRACT Nutrient management in Cauliflower (Brassica oleracea var. botrytis L.) cultivation is in part dependent on the microbial population in the rhizosphere. Fertilizer must be applied to support plant growth and development. Whether fertilizer usage in cauliflower can be reduced needs to be determined. Rhizospheric isolates of cauliflower, obtained from soil around the roots and from their roots, in the low and mid-hills agro-ecological zones of India were tested for their efficacy to support cauliflower productivity. The plant growth–promoting rhizobacteria SB5, SB8, SB10, and SB11 exhibited the best plant growth–promoting traits with antagonism against soilborne pathogens compared to the reference strain Bacillus pumilus (JN559852). Application of SB11 (Bacillus spp.) exhibited the most plant growth–promoting attributes in field trials at 75% N and P and increased yield by 33% over the uninoculated control at 100% N and P. The isolate SB11 exhibited P solubilization, siderophore production, indole acetic acid production, hydrocyanic acid (HCN) production, and antifungal activity that may be developed as a plant growth–promoting rhizobacteria (PGPR) to enhance crop productivity and sustain soil health while saving 25% usage of chemical fertilizers.
Pure culture, co-culture and whole ecosystem investigations of single anaerobes, partnerships and microbial communities in anaerobic digestion

Anaerobic Digestion (AD) comprises the microbiological breakdown of complex organic materials in the absence of oxygen to produce biogas, which can be used as a renewable fuel. However, there is a knowledge gap regarding the microbiology that underpins the AD process, especially the specific synergies and competitions, between the individual species involved. Growth and substrate consumption profiles of pure cultures, and co-cultures, of three model AD organisms, Methanosarcina barkeri, Acetobacterium woodii and Methanococcus maripaludis, were investigated to determine their interactions under H2-CO2 and acetate feeding at both moderate and low temperatures. A common medium was also used for two of the strains for this study. Temporal CH4 and volatile fatty acid (VFA) concentrations in assays were determined for all combinations at each growth phase. Results showed that the substrates and temperatures strongly influenced the growth rates of each strain and co-culture. M. barkeri grew fastest in H2-CO2 at 37°C (doubling time (dt): 12.44 hours) compared to under acetate (dt: 34.34 hours) or at 15°C (dt: 28.25 hours). A. woodii grew fastest in H2-CO2 at 35°C (dt: 17.13 hours) compared to at 15°C in H2-CO2 (dt: 27.90 hours) and M. maripaludis grew at an optimum of dt = 32.30 hours in the CP medium on its preferred substrate H2-CO2 as the main energy source, at 37°C. Cocultures of M. barkeri partnered with A. woodii and M. barkeri partnered with M. maripaludis demonstrated the impact of competitions on their pure culture counterparts regarding growth and metabolisms. Future studies will include analyses of individual strains and mixed-species consortia by genomics and transcriptomics, and metagenomics and metatranscriptomics, respectively.