Miracle-dz/UniFAR-FTUs huggingface dataset:
Cosmological Evolution of the Formation Rate of Short Gamma-Ray Bursts with and without Extended Emission

Originating from neutron star–neutron star or neutron star–black hole mergers, short gamma-ray bursts (SGRBs) are the first electromagnetic emitters associated with gravitational waves (GWs). This association makes the determination of SGRB formation rate (FR) a critical issue. We determine the true SGRB FR and its relation to the cosmic star formation rate (SFR). This can help in determining the expected GW rate involving small mass mergers. We present nonparametric methods for the determination of the evolutions of the luminosity function (LF) and the FR using SGRBs observed by Swift, without any assumptions. These are powerful tools for small samples, such as our sample of 68 SGRBs. We combine SGRBs with and without extended emission (SEE), assuming that both descend from the same progenitor. To overcome the incompleteness introduced by redshift measurements we use the Kolmogorov–Smirnov (KS) test to find flux thresholds yielding a sample of sources with a redshift drawn from the parent sample including all sources. Using two subsamples of SGRBs with flux limits of 4.57 × 10−7 and 2.15 × 10−7 erg cm−2 s−1 with respective KS p = (1, 0.9), we find a 3σ evidence for luminosity evolution (LE), a broken power-law LF with significant steepening at L ∼ 1050 erg s−1, and an FR evolution that decreases monotonically with redshift (independent of LE and the thresholds). Thus, SGRBs may have been more luminous in the past with an FR delayed relative to the SFR as expected in the merger scenario.
Societal Biases in Retrieved Contents: Measurement Framework and Adversarial Mitigation of BERT Rankers

Societal biases resonate in the retrieved contents of information retrieval (IR) systems, resulting in reinforcing existing stereotypes. Approaching this issue requires established measures of fairness in respect to the representation of various social groups in retrieval results, as well as methods to mitigate such biases, particularly in the light of the advances in deep ranking models. In this work, we first provide a novel framework to measure the fairness in the retrieved text contents of ranking models. Introducing a ranker-agnostic measurement, the framework also enables the disentanglement of the effect on fairness of collection from that of rankers. To mitigate these biases, we propose AdvBert, a ranking model achieved by adapting adversarial bias mitigation for IR, which jointly learns to predict relevance and remove protected attributes. We conduct experiments on two passage retrieval collections (MSMARCO Passage Re-ranking and TREC Deep Learning 2019 Passage Re-ranking), which we extend by fairness annotations of a selected subset of queries regarding gender attributes. Our results on the MSMARCO benchmark show that, (1) all ranking models are less fair in comparison with ranker-agnostic baselines, and (2) the fairness of Bert rankers significantly improves when using the proposed AdvBert models. Lastly, we investigate the trade-off between fairness and utility, showing that we can maintain the significant improvements in fairness without any significant loss in utility.
Zero-Shot Detection via Vision and Language Knowledge Distillation

Zero-shot image classiﬁcation has made promising progress by training the aligned image and text encoders. The goal of this work is to advance zero-shot object detection, which aims to detect novel objects without bounding box nor mask annotations. We propose ViLD , a training method via Vi sion and L anguage knowledge D istillation. We distill the knowledge from a pre-trained zero-shot image classiﬁcation model ( e.g ., CLIP [33]) into a two-stage detector ( e.g ., Mask R-CNN [17]). Our method aligns the region embeddings in the detector to the text and image embeddings inferred by the pre-trained model. We use the text embeddings as the detection classiﬁer, obtained by feeding category names into the pre-trained text encoder. We then minimize the distance between the region embeddings and image embeddings, obtained by feeding region proposals into the pre-trained image encoder. During inference, we include text embeddings of novel categories into the detection classiﬁer for zero-shot detection. We benchmark the performance on LVIS dataset [15] by holding out all rare categories as novel categories. ViLD obtains 16.1 mask AP r with a Mask R-CNN (ResNet-50 FPN) for zero-shot detection, outperforming the supervised counterpart by 3.8. The model can directly transfer to other datasets, achieving 72.2 AP 50 , 36.6 AP and 11.8 AP on PASCAL VOC, COCO and Objects365, respectively.
Interplay between polarization and quantum correlations of confined polaritons

We consider the problem of a coherently driven polariton box cavity in the low driving regime, accounting for the polarization degree of freedom. The different interaction strengths between coand cross-circularly polarized polaritons and a realistic linear-polarization splitting allows one to model the system as two coupled nonlinear resonators with both selfand cross-Kerr-like nonlinearities, thus making our results potentially relevant for other experimental platforms. Within an effective wave-function approach, we obtain analytical expressions for the steady-state polarization-resolved polariton populations and second-order correlation functions, which agree very well with our numerical results obtained from a Lindblad master equation. Notably, we highlight that depending on the excitation polarization (circular or linear), both the unconventional (interference-mediated) and conventional (mediated by nonlinearities) antibunchings can be investigated in a single cavity. Moreover, using our results, we argue that recent experiments on confined fiber-cavity polaritons are likely to have probed a regime where the dominant interaction is between cross-polarized polaritons, which is characteristic of the polariton Feshbach resonance. We furthermore investigate the regime close to resonance using a two-channel model, and we show that systems with large biexciton binding energies, such as atomically thin semiconductors, are promising platforms for realizing strong polariton antibunching.
IDMT-Traffic: An Open Benchmark Dataset for Acoustic Traffic Monitoring Research

In many urban areas, traffic load and noise pollution are constantly increasing. Automated systems for traffic monitoring are promising countermeasures, which allow to systematically quantify and predict local traffic flow in order to to support municipal traffic planning decisions. In this paper, we present a novel open benchmark dataset, containing 15,706 2-second long stereo audio clips, which were extracted from 4718 vehicle passing events captured with both high-quality sE8 and medium-quality MEMS microphones. This dataset is well suited to evaluate the use-case of deploying audio classification algorithms to embedded sensor devices with restricted microphone quality and hardware processing power. In addition, this paper provides a detailed review of recent acoustic traffic monitoring (ATM) algorithms as well as the results of two benchmark experiments on vehicle type classification and direction of movement estimation using four state-of-the-art convolutional neural network architectures.
Point Cloud Learning with Transformer

Remarkable performance from Transformer networks in Natural Language Processing promote the development of these models in dealing with computer vision tasks such as image recognition and segmentation. In this paper, we introduce a novel framework, called Multi-level Multi-scale Point Transformer (MLMSPT) that works directly on the irregular point clouds for representation learning. Speciﬁcally, a point pyramid transformer is investi-gated to model features with diverse resolutions or scales we deﬁned, followed by a multi-level transformer module to aggregate contextual information from different levels of each scale and enhance their interactions. While a multi-scale transformer module is designed to capture the dependencies among representations across different scales. Extensive evaluation on public benchmark datasets demonstrate the effectiveness and the competitive performance of our methods on 3D shape classiﬁcation, segmentation tasks.
Meta-evaluation of Conversational Search Evaluation Metrics

Conversational search systems, such as Google assistant and Microsoft Cortana, enable users to interact with search systems in multiple rounds through natural language dialogues. Evaluating such systems is very challenging, given that any natural language responses could be generated, and users commonly interact for multiple semantically coherent rounds to accomplish a search task. Although prior studies proposed many evaluation metrics, the extent of how those measures effectively capture user preference remain to be investigated. In this article, we systematically meta-evaluate a variety of conversational search metrics. We specifically study three perspectives on those metrics: (1) reliability: the ability to detect “actual” performance differences as opposed to those observed by chance; (2) fidelity: the ability to agree with ultimate user preference; and (3) intuitiveness: the ability to capture any property deemed important: adequacy, informativeness, and fluency in the context of conversational search. By conducting experiments on two test collections, we find that the performance of different metrics vary significantly across different scenarios, whereas consistent with prior studies, existing metrics only achieve weak correlation with ultimate user preference and satisfaction. METEOR is, comparatively speaking, the best existing single-turn metric considering all three perspectives. We also demonstrate that adapted session-based evaluation metrics can be used to measure multi-turn conversational search, achieving moderate concordance with user satisfaction. To our knowledge, our work establishes the most comprehensive meta-evaluation for conversational search to date.
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods. Indeed, many high-dimensional learning tasks previously thought to be beyond reach -- such as computer vision, playing Go, or protein folding -- are in fact feasible with appropriate computational scale. Remarkably, the essence of deep learning is built from two simple algorithmic principles: first, the notion of representation or feature learning, whereby adapted, often hierarchical, features capture the appropriate notion of regularity for each task, and second, learning by local gradient-descent type methods, typically implemented as backpropagation. While learning generic functions in high dimensions is a cursed estimation problem, most tasks of interest are not generic, and come with essential pre-defined regularities arising from the underlying low-dimensionality and structure of the physical world. This text is concerned with exposing these regularities through unified geometric principles that can be applied throughout a wide spectrum of applications. Such a 'geometric unification' endeavour, in the spirit of Felix Klein's Erlangen Program, serves a dual purpose: on one hand, it provides a common mathematical framework to study the most successful neural network architectures, such as CNNs, RNNs, GNNs, and Transformers. On the other hand, it gives a constructive procedure to incorporate prior physical knowledge into neural architectures and provide principled way to build future architectures yet to be invented.
Aromatic Profile Variation of Essential Oil from Dried Makwhaen Fruit and Related Species

The aim of this research is to evaluate the relationship between genotype, phenotype, and chemical profiles of essential oil obtained from available Zanthoxylum spp. Three specimens of makhwaen (MK) distributed in Northern Thailand were genetically and morphologically compared with other Zanthoxylum spices, known locally as huajiao (HJ) and makwoung (MKO), respectively. HJ was taxonomically confirmed as Z. armatum while MKO and MK were identified as Z. rhetsa and Z. myriacanthum. Genetic sequencing distributed these species into three groups accordingly to their confirmed species. Essential oil of the dried fruits from these samples was extracted and analyzed for their chemical and physical properties. Cluster analysis of their volatile compositions separated MKO and MK apart from HJ with L-limonene, terpinen-4-ol, β-phellandrene, and β-philandrene. By using odor attributes, the essential oil of MKO and MK were closely related possessing fruity, woody, and citrus aromas, while the HJ was distinctive. Overall, the phenotypic characteristic can be used to elucidate the species among makhwaen fruits of different sources. The volatile profiling was nonetheless dependent on the genotypes but makwoung and makhwaen showed similar profiles.
Curcumin Inhibits Lysophosphatidic Acid Mediated MCP-1 Expression via Blocking ROCK Signalling

Curcumin is a natural compound that has been widely used as a food additive and medicine in Asian countries. Over several decades, diverse biological effects of curcumin have been elucidated, such as anti-inflammatory and anti-oxidative activities. Monocyte chemoattractant protein-1 (MCP-1) is a key inflammatory marker during the development of atherosclerosis, and curcumin blocks MCP-1 expression stimulated by various ligands. Hence, we studied the action of curcumin on lysophosphatidic acid (LPA) mediated MCP-1 expression and explored the specific underlying mechanisms. In human vascular smooth muscle cells, LPA induces Rho-associated protein kinase (ROCK) dependent transforming growth factor receptor (TGFBR1) transactivation, leading to glycosaminoglycan chain elongation. We found that LPA also signals via the TGFBR1 transactivation pathway to regulate MCP-1 expression. Curcumin blocks LPA mediated TGFBR1 transactivation and subsequent MCP-1 expression by blocking the ROCK signalling. In the vasculature, ROCK signalling regulates smooth muscle cell contraction, inflammatory cell recruitment, endothelial dysfunction and vascular remodelling. Therefore, curcumin as a ROCK signalling inhibitor has the potential to prevent atherogenesis via multiple ways.
In situ fabrication of (Sr,La)FeO4 with CoFe alloy nanoparticles as an independent catalyst layer for direct methane-based solid oxide fuel cells with a nickel cermet anode

An independent catalyst layer is applied to develop a highly effective way to reduce coking when operating in methane based fuels, in which the catalyst layer is separated from a Ni cermet anode. In this way, Ni cermet anode conductivity is not influenced, and cell cracking due to the thermal–mechanical stress from the mismatched thermal expansion coefficients (TECs) between the catalyst and anode materials, the temperature gradients within the anode caused by the highly endothermic reforming reaction of methane, and the large internal strain during the reduction process is also avoided. La0.6Sr0. 4Co0.2Fe0. 8O3−δ (LSCF), which is co-pressed with an Al2O3 substrate into a double-layered slice with a mesoporous structure, functions as an independent catalyst layer of the Ni-based anode. Under SOFC operating conditions, a K2NiF4-type oxide (Sr,La)FeO4 with homogeneously dispersed CoFe alloy nanoparticles is formed, which shows good catalytic activity for methane partial oxidation with 88% conversion at 950 °C in a mixture of CH4 and O2 (1 : 1). A conventional cell with the state-of-art Ni cermet anode (NiO–8% Y stabilized ZrO2 (YSZ)/YSZ/La0.8Sr0. 2MnO3–YSZ) is constructed and the electrochemical performance of cells with and without the independent catalyst layer is tested. In wet methane, the voltage of the conventional cell without the catalyst layer declines rapidly from 0.7 V to 0.1 V within 20 min at 333 mA cm−2 and 800 °C. In contrast, the voltage of the modified cell with an independent catalyst layer stabilizes at 0.79 V with negligible degradation within 116 h. In wet coal bed methane (CBM), the voltage of the modified cell with an independent catalyst layer exhibits a slow decrease from 0.69 V to 0.66 V within 12 h. The stable power output of the cell with an independent catalyst layer under a constant current load in methane indicates excellent coking resistance. The microstructure and surface composition of the catalyst layer and anode are further analyzed by SEM and EDX after the stability test.
The Gut Microbiota Affects Corticosterone Production in the Murine Small Intestine

Glucocorticoids (GCs) are hormones that are released in response to stressors and exhibit many activities, including immunomodulatory and anti-inflammatory activities. They are primarily synthesized in the adrenal gland but are also produced in peripheral tissues via regeneration of adrenal 11-oxo metabolites or by de novo synthesis from cholesterol. The present study investigated the influence of the microbiota on de novo steroidogenesis and regeneration of corticosterone in the intestine of germ-free (GF) and specific pathogen-free mice challenged with a physical stressor (anti-CD3 antibody i.p. injection). In the small intestine, acute immune stress resulted in increased mRNA levels of the proinflammatory cytokines IL1β, IL6 and Tnfα and genes involved in de novo steroidogenesis (Stard3 and Cyp11a1), as well as in regeneration of active GCs from their 11-oxo metabolites (Hsd11b1). GF mice showed a generally reduced transcriptional response to immune stress, which was accompanied by decreased intestinal corticosterone production and reduced expression of the GC-sensitive marker Fkbp5. In contrast, the interaction between stress and the microbiota was not detected at the level of plasma corticosterone or the transcriptional response of adrenal steroidogenic enzymes. The results indicate a differential immune stress-induced intestinal response to proinflammatory stimuli and local corticosterone production driven by the gut microbiota.
Testis Development and Differentiation in Amphibians

Sex is determined genetically in amphibians; however, little is known about the sex chromosomes, testis-determining genes, and the genes involved in testis differentiation in this class. Certain inherent characteristics of the species of this group, like the homomorphic sex chromosomes, the high diversity of the sex-determining mechanisms, or the existence of polyploids, may hinder the design of experiments when studying how the gonads can differentiate. Even so, other features, like their external development or the possibility of inducing sex reversal by external treatments, can be helpful. This review summarizes the current knowledge on amphibian sex determination, gonadal development, and testis differentiation. The analysis of this information, compared with the information available for other vertebrate groups, allows us to identify the evolutionarily conserved and divergent pathways involved in testis differentiation. Overall, the data confirm the previous observations in other vertebrates—the morphology of the adult testis is similar across different groups; however, the male-determining signal and the genetic networks involved in testis differentiation are not evolutionarily conserved.
Mild and Moderate Extraction Methods to Assess Potentially Available Soil Organic Nitrogen

The use of chemical methods to assess the soil organic nitrogen (N) potentially available to plants is not a common practice in Brazil. However, associated with others, this tool might improve efficiency in the use of waste and nitrogen fertilizers. In our study, chemical methods were tested to assess potentially available soil N in samples of 17 representative soils of the western plateau of the state of Sao Paulo (10 Oxisols and 7 Ultisols). Available soil N was extracted from the collected soil samples using moderate (ISNT-Illinois Soil Nitrogen Test) and mild (hot water and heated solutions of 2 mol L-1 KCl and 0.01 mol L-1 CaCl2) extraction methods. The levels of potentially available N obtained from these chemical methods were correlated with dry matter (DM) and N uptake (Nup) by corn plants grown in pots in a greenhouse experiment carried out with the same 17 soil samples. The ISNT method showed the highest available N extraction capacity, whereas hot water showed the lowest capacity, followed closely by the hot 0.01 mol L-1 CaCl2 solution. Despite the differences among the quantities of available N extracted, the methods correlated with each other and with DM and Nup, but the values from the ISNT method showed the lowest correlation with plant variables (rDM = 0.67** and rNup = 0.81**). Procedures of extraction with water or 0.01 mol L-1 CaCl2 heated for 16 h, and 2 mol L-1 KCl heated for 4 h, resulted in similar correlation values (r) with plant DM and Nup. Thus, water (rDM = 0.77** and rNup = 0.90**) and 0.01 mol L-1 CaCl2 (rDM = 0.82** and rNup = 0.93**) heated for 16 h can be recommended as the best options for N extraction.considering the possibility for predicting N availability, lower generation of waste, and lower cost of analysis.
A Homozygous Dab1−/− Is a Potential Novel Cause of Autosomal Recessive Congenital Anomalies of the Mice Kidney and Urinary Tract

This study aimed to explore morphology changes in the kidneys of Dab1−/− (yotari) mice, as well as expression patterns of reelin, NOTCH2, LC3B, and cleaved caspase3 (CASP3) proteins, as potential determinants of normal kidney formation and function. We assumed that Dab1 functional inactivation may cause disorder in a wide spectrum of congenital anomalies of the kidney and urinary tract (CAKUT). Animals were sacrificed at postnatal days P4, P11, and P14. Paraffin-embedded kidney tissues were sectioned and analyzed by immunohistochemistry using specific antibodies. Kidney specimens were examined by bright-field, fluorescence, and electron microscopy. Data were analyzed by two-way ANOVA and t-tests. We noticed that yotari kidneys were smaller in size with a reduced diameter of nephron segments and thinner cortex. TEM microphotographs revealed foot process effacement in the glomeruli (G) of yotari mice, whereas aberrations in the structure of proximal convoluted tubules (PCT) and distal convoluted tubules (DCT) were not observed. A significant increase in reelin expression, NOTCH2, LC3B and cleaved CASP3 proteins was observed in the glomeruli of yotari mice. Renal hypoplasia in conjunction with foot process effacement and elevation in the expression of examined proteins in the glomeruli revealed CAKUT phenotype and loss of functional kidney tissue of yotari.
Energy Allocation for LoRaWAN Nodes with Multi-Source Energy Harvesting

Many connected devices are expected to be deployed during the next few years. Energy harvesting appears to be a good solution to power these devices but is not a reliable power source due to the time-varying nature of most energy sources. It is possible to harvest energy from multiple energy sources to tackle this problem, thus increasing the amount and the consistency of harvested energy. Additionally, a power management system can be implemented to compute how much energy can be consumed and to allocate this energy to multiple tasks, thus adapting the device quality of service to its energy capabilities. The goal is to maximize the amount of measured and transmitted data while avoiding power failures as much as possible. For this purpose, an industrial sensor node platform was extended with a multi-source energy-harvesting circuit and programmed with a novel energy-allocation system for multi-task devices. In this paper, a multi-source energy-harvesting LoRaWAN node is proposed and optimal energy allocation is proposed when the node runs different sensing tasks. The presented hardware platform was built with off-the-shelf components, and the proposed power management system was implemented on this platform. An experimental validation on a real LoRaWAN network shows that a gain of 51% transmitted messages and 62% executed sensing tasks can be achieved with the multi-source energy-harvesting and power-management system, compared to a single-source system.