Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecules with ALMA at Planet-forming Scales (MAPS). XVI. Characterizing the Impact of the Molecular Wind on the Evolution of the HD 163296 System

During the main phase of evolution of a protoplanetary disk, accretion regulates the inner-disk properties, such as the temperature and mass distribution, and in turn, the physical conditions associated with planet formation. The driving mechanism behind accretion remains uncertain; however, one promising mechanism is the removal of a fraction of angular momentum via a magnetohydrodynamic (MHD) disk wind launched from the inner tens of astronomical units of the disk. This paper utilizes CO isotopologue emission to study the unique molecular outflow originating from the HD 163296 protoplanetary disk obtained with the Atacama Large Millimeter/submillimeter Array. HD 163296 is one of the most well-studied Class II disks and is proposed to host multiple gas-giant planets. We robustly detect the large-scale rotating outflow in the 12CO J = 2 − 1 and the 13CO J = 2 − 1 and J = 1 − 0 transitions. We constrain the kinematics, the excitation temperature of the molecular gas, and the mass-loss rate. The high ratio of the rates of ejection to accretion (5–50), together with the rotation signatures of the flow, provides solid evidence for an MHD disk wind. We find that the angular momentum removal by the wind is sufficient to drive accretion though the inner region of the disk; therefore, accretion driven by turbulent viscosity is not required to explain HD 163296's accretion. The low temperature of the molecular wind and its overall kinematics suggest that the MHD disk wind could be perturbed and shocked by the previously observed high-velocity atomic jet. This paper is part of the MAPS special issue of the Astrophysical Journal Supplement.
Bootstrapping Bloch bands

Bootstrap methods, initially developed for solving statistical and quantum field theories, have recently been shown to capture the discrete spectrum of quantum mechanical problems, such as the single particle Schrödinger equation with an anharmonic potential. The core of bootstrap methods builds on exact recursion relations of arbitrary moments of some quantum operator and the use of an adequate set of positivity criteria. We extend this methodology to models with continuous Bloch band spectra, by considering a single quantum particle in a periodic cosine potential. We find that the band structure can be obtained accurately provided the bootstrap uses moments involving both position and momentum variables. We also introduce several new techniques that can apply generally to other bootstrap studies. First, we devise a trick to reduce by one unit the dimensionality of the search space for the variables parametrizing the bootstrap. Second, we employ statistical techniques to reconstruct the distribution probability allowing to compute observables that are analytic functions of the canonical variables. This method is used to extract the Bloch momentum, a quantity that is not readily available from the bootstrap recursion itself.
Irrational Behaviour and Stock Investment Decision. Does Gender Matter? Evidence from Sri Lanka

The research aims to examine the effect of the irrational behaviour of individual investors on stock investment decisions, specifically, anchoring, disposition effect, home bias, herding, overconfidence and risk perception. The research further investigates the moderating role of gender between irrational behaviour and stock investment decision. Finally, it reveals which irrational behaviour is most prevalent. A survey collected the primary data from 432 individual investors. The survey evidence shows that six irrational behaviours, anchoring, disposition effect, overconfidence and risk perception affect the investment decision of individual investors, and risk perception comes out to be the significant irrational behaviour on stock investment decision. The research further explores that gender has a significant moderation for anchoring, disposition effect, herding, overconfidence, risk perception, and stock investment decision. We recommend that if individuals are aware of the behavioural biases, it will help them for making the right stock investment decisions. The study is also relevant for financial advisors, stockbrokers and policymakers as it facilitates them in gaining a better understanding of their clients’ irrational behaviour. The present study gives a unique insight into the individual investors’ profile of gender corresponding to each main irrational behaviour on investment decision under consideration of stock investment.
Towards Better Model Understanding with Path-Sufficient Explanations

Feature based local attribution methods are amongst the most prevalent in explainable artificial intelligence (XAI) literature. Going beyond standard correlation, recently, methods have been proposed that highlight what should be minimally sufficient to justify the classification of an input (viz. pertinent positives). While minimal sufficiency is an attractive property, the resulting explanations are often too sparse for a human to understand and evaluate the local behavior of the model, thus making it difficult to judge its overall quality. To overcome these limitations, we propose a novel method called Path-Sufficient Explanations Method (PSEM) that outputs a sequence of sufficient explanations for a given input of strictly decreasing size (or value) -- from original input to a minimally sufficient explanation -- which can be thought to trace the local boundary of the model in a smooth manner, thus providing better intuition about the local model behavior for the specific input. We validate these claims, both qualitatively and quantitatively, with experiments that show the benefit of PSEM across all three modalities (image, tabular and text). A user study depicts the strength of the method in communicating the local behavior, where (many) users are able to correctly determine the prediction made by a model.
On the Correlation between the Noise and a Priori Error Vectors in Affine Projection Algorithms

This paper analyzes the correlation matrix between the a priori error and measurement noise vectors for affine projection algorithms (APA). This correlation stems from the dependence between the filter tap estimates and the noise samples, and has a strong influence on the mean square behavior of the algorithm. We show that the correlation matrix is upper triangular, and compute the diagonal elements in closed form, showing that they are independent of the input process statistics. Also, for white inputs we show that the matrix is fully diagonal. These results are valid in the transient and steady states of the algorithm considering a possibly variable step-size. Our only assumption is that the filter order is large compared to the projection order of APA and we make no assumptions on the input signal except for stationarity. Using these results, we perform a steady-state analysis of the algorithm for small step size and provide a new simple closed-form expression for mean-square error, which has comparable or better accuracy to many preexisting expressions, and is much simpler to compute. Finally, we also obtain expressions for the steady-state energy of the other components of the error vector.
Exploring Prompt-based Few-shot Learning for Grounded Dialog Generation

Dialog models can be greatly strengthened through grounding on various external information, but grounded dialog corpora are usually not naturally accessible. In this work, we focus on the few-shot learning for grounded dialog generation (GDG). We first propose a simple prompting method for GDG tasks, where different constructs of model input, such as the grounding source and the conversation context, are distinguished through continuous or discrete prompts. On three typical GDG tasks, we empirically demonstrate and analyze in-depth the effectiveness of our method. We then conduct extensive experiments to thoroughly investigate how our prompting method works with different pre-trained models. We show that prompted language models perform superiorly to conversational models, and further analyze various factors that influence the effects of prompting. Overall, our work introduces a prompt-based perspective to the few-shot learning for GDG tasks, and provides valuable findings and insights for future research.
ML Based Lineage in Databases

We track the lineage of tuples throughout their database lifetime. That is, we consider a scenario in which tuples (records) that are produced by a query may affect other tuple insertions into the DB, as part of a normal workflow. As time goes on, exact provenance explanations for such tuples become deeply nested, increasingly consuming space, and resulting in decreased clarity and readability. We present a novel approach for approximating lineage tracking, using a Machine Learning (ML) and Natural Language Processing (NLP) technique; namely, word embedding. The basic idea is summarizing (and approximating) the lineage of each tuple via a small set of constant-size vectors (the number of vectors per-tuple is a hyperparameter). Therefore, our solution does not suffer from space complexity blow-up over time, and it"naturally ranks"explanations to the existence of a tuple. We devise an alternative and improved lineage tracking mechanism, that of keeping track of and querying lineage at the column level; thereby, we manage to better distinguish between the provenance features and the textual characteristics of a tuple. We integrate our lineage computations into the PostgreSQL system via an extension (ProvSQL) and extensive experiments exhibit useful results in terms of accuracy against exact, semiring-based, justifications, especially for the column-based (CV) method which exhibits high precision and high per-level recall. In the experiments, we focus on tuples with \textit{multiple generations} of tuples in their lifelong lineage and analyze them in terms of direct and distant lineage.
Diffusion-Weighted MRI Findings of Caudate Nucleus and Putamen in Patients With Obsessive-Compulsive Disorder

Objective The purpose of this study was to establish the diffusion-weighted magnetic resonance imaging (DW-MRI) findings of the caudate nucleus and putamen in patients with obsessive-compulsive disorder (OCD) and to obtain new information on the etiopathogenesis of OCD, which is still unclear. Methods The study comprised 20 patients with OCDs and 20 healthy volunteers. In these cases, DW-MRI and diffusion-weighted echo-planar images (DW-EPI) at b600 and b1000 gradient values were taken and the measurements were made using the apparent diffusion coefficient (ADC) maps of each group at b600 and b1000 values from the caudate nucleus and putamen. Results When the DW-MRI examination in patients with OCD was compared with the control group, the mean ADC values in the caudate nucleus and putamen were not found to have statistically significantly changed. In addition, there were no significant differences regarding the right and left caudate nuclei and putamen ADC values at the b600 and b1000 in the patients with OCD or the control group. Conclusion There are still many unknowns about the neurobiology of OCD. When the DW-MRI examination of the patients with OCD was compared with the control group in our study, no significant difference was found between the ADC values of the caudate nucleus and putamen. Further studies are required for this present study on DW-MRI in patients with OCD to be meaningful.
The Role and Validity of Diagnostic Biomarkers in Late-Onset Neonatal Sepsis

Sepsis remains a leading cause of mortality in the neonatal population, and currently, there is still no consensus on an accurate biomarker that can aid prompt diagnosis. This review focuses on studies investigating biomarkers for late-onset neonatal sepsis specifically. We discuss the current evidence for traditionally used biomarkers and present recent developments on more novel markers. Suitable articles were selected from PubMed, Embase, Medline, Cochrane Handbook of Systematic Reviews, and ScienceDirect. Inclusion criteria were studies published from 2010 to 2020. Exclusion criteria were animal model-based studies. Keywords in search strategy were late-onset neonatal sepsis + biomarkers + diagnosis. Evidence is growing increasingly weak for commonly studied biomarkers such as C-reactive protein (CRP) and procalcitonin (PCT). Levels of markers such as Serum Amyloid A and Neutrophil CD64 increase more rapidly post-onset of infection compared to CRP. Moreover, this review found that the more novel biomarkers discussed such as presepsin and endocan may show superior and more promising potential as diagnostic markers. However, larger studies over multicenters are deemed essential to ascertain the ideal biomarker.
Non-invasive smooth muscle electromyography (SMEMG) as a novel monitoring technology of the gastrointestinal tract of awake, free-moving pigs—A pilot study

There are several mathematical models and measurements to determine the efficiency of the digestibility of different feedstuffs. However, there is lack of information regarding the direct methods or measurement techniques used to analyse the physical response of the different parts of the gastrointestinal tract (GIT) of growing pigs to different diets. Smooth muscle electromyography (SMEMG) is a non-invasive method for the measurement of gastrointestinal myoelectrical activity. In the present study, SMEMG methodology has been adapted from laboratory rats to pigs, and the effects of feedstuffs with control (CTR) or experimentally increased (EXP) amounts of fibre were investigated on gastrointestinal tract motility. Nine barrow pigs ((Danish Landrace × Danish Yorkshire) × Danish Duroc) were used (30 ± 3 kg), and their CTR and EXP feedstuffs contained 29 and 49 g/kg crude fibre (CF), respectively. Myoelectric activities of the stomach, ileum and caecum were detected in the awake pigs by a pair of electrodes. The recorded myoelectric signals were analysed with fast Fourier transformation (FFT), and the spectra were expressed in GIT section-specific cycles per minutes (cpm) values and the maximum power spectrum density (PsDmax). A significant increase (P < 0.001) was observed in the value of the PsDmax of the small intestine (20–25 cpm) as a consequence of the EXP diet. The PsDmax values of the stomach (3–5 cpm) and large intestine (1–3 cpm) did not show any significant change in pigs fed the EXP diet. As a direct and non-invasive method, SMEMG is suitable for the rapid evaluation of the effects of diets with different fibre contents on the GIT of non-anaesthetised, free-moving pigs.
Moderate plant–soil feedbacks have small effects on the biodiversity–productivity relationship: A field experiment

Abstract Plant–soil feedback (PSF) has gained attention as a mechanism promoting plant growth and coexistence. However, most PSF research has measured monoculture growth in greenhouse conditions. Translating PSFs into effects on plant growth in field communities remains an important frontier for PSF research. Using a 4‐year, factorial field experiment in Jena, Germany, we measured the growth of nine grassland species on soils conditioned by each of the target species (i.e., 72 PSFs). Plant community models were parameterized with or without these PSF effects, and model predictions were compared to plant biomass production in diversity–productivity experiments. Plants created soils that changed subsequent plant biomass by 40%. However, because they were both positive and negative, the average PSF effect was 14% less growth on “home” than on “away” soils. Nine‐species plant communities produced 29 to 37% more biomass for polycultures than for monocultures due primarily to selection effects. With or without PSF, plant community models predicted 28%–29% more biomass for polycultures than for monocultures, again due primarily to selection effects. Synthesis: Despite causing 40% changes in plant biomass, PSFs had little effect on model predictions of plant community biomass across a range of species richness. While somewhat surprising, a lack of a PSF effect was appropriate in this site because species richness effects in this study were caused by selection effects and not complementarity effects (PSFs are a complementarity mechanism). Our plant community models helped us describe several reasons that even large PSF may not affect plant productivity. Notably, we found that dominant species demonstrated small PSF, suggesting there may be selective pressure for plants to create neutral PSF. Broadly, testing PSFs in plant communities in field conditions provided a more realistic understanding of how PSFs affect plant growth in communities in the context of other species traits.
Propagation of smallness and spectral estimates

The purpose of this article is to show that the spectral projector estimates for Laplace operators can be deduced from Logunov-Malinnikova's Propagation of smallness estimates for harmonic functions [11, 10, 9]. The main point is to pass from the local estimates obtained in [4] (on a compact manifold) to global estimates. We also state classical consequences in terms of observability and control for heat equations, which are direct consequences of these spectral projector estimates.
Long time asymptotic for the Wadati-Konno-Ichikawa equation with finite density initial data

In this work, we investigate the Cauchy problem of the Wadati-Konno-Ichikawa (WKI) equation with ﬁnite density initial data. Employing the ¯ ∂ -generalization of Deift-Zhou nonlinear steepest descent method, we derive the long time asymptotic behavior of the solution q ( x, t ) in space-time soliton region. Based on the resulting asymptotic behavior, the asymptotic approximation of the WKI equation is characterized with the soliton term conﬁrmed by N ( I ) -soliton on discrete spectrum and the t − 12 leading order term on continuous spectrum with residual error up to O ( t − 34 ) . Our results also conﬁrm the soliton resolution conjecture for the WKI equation.
Spiking Neural Networks for Visual Place Recognition Via Weighted Neuronal Assignments

Spiking neural networks (SNNs) offer both compelling potential advantages, including energy efficiency and low latencies and challenges including the non-differentiable nature of event spikes. Much of the initial research in this area has converted deep neural networks to equivalent SNNs, but this conversion approach potentially negates some of the advantages of SNN-based approaches developed from scratch. One promising area for high-performance SNNs is template matching and image recognition. This research introduces the first high-performance SNN for the Visual Place Recognition (VPR) task: given a query image, the SNN has to find the closest match out of a list of reference images. At the core of this new system is a novel assignment scheme that implements a form of ambiguity-informed salience, by up-weighting single-place-encoding neurons and down-weighting “ambiguous” neurons that respond to multiple different reference places. In a range of experiments on the challenging Nordland, Oxford RobotCar, SPEDTest, Synthia, and St Lucia datasets, we show that our SNN achieves comparable VPR performance to state-of-the-art and classical techniques, and degrades gracefully in performance with an increasing number of reference places. Our results provide a significant milestone towards SNNs that can provide robust, energy-efficient, and low latency robot localization.
Evaluation of the wastewater generated in the purification of biodiesel

ABSTRACT This study quantified the pollutants in the wastewater (WW) of biodiesel. Density, number of acid, and viscosity were determined for biodiesel quality characteristics. The characterization of the effluents was performed according to the Mexican Environmental Norm. The viscosity was higher than the normalized limit (>5mm2/s). The WW had high concentrations of total suspended solids (TSS; >730 mg/l) and fat and oil (>1810 mg/l), the latter with significant differences (P > 0.05), and presented toxicity (>35000 mg/l), due to the presence of oil. Therefore these should not be discharged into the sewage networks or water bodies, considering the possible adverse effects in the environment.
Exact Coherent Structures and Phase Space Geometry of Preturbulent 2D Active Nematic Channel Flow.

Confined active nematics exhibit rich dynamical behavior, including spontaneous flows, periodic defect dynamics, and chaotic "active turbulence. " Here, we study these phenomena using the framework of exact coherent structures, which has been successful in characterizing the routes to high Reynolds number turbulence of passive fluids. Exact coherent structures are stationary, periodic, quasiperiodic, or traveling wave solutions of the hydrodynamic equations that, together with their invariant manifolds, serve as an organizing template of the dynamics. We compute the dominant exact coherent structures and connecting orbits in a preturbulent active nematic channel flow, which enables a fully nonlinear but highly reduced-order description in terms of a directed graph. Using this reduced representation, we compute instantaneous perturbations that switch the system between disparate spatiotemporal states occupying distant regions of the infinite-dimensional phase space. Our results lay the groundwork for a systematic means of understanding and controlling active nematic flows in the moderate- to high-activity regime.
The emerging roles of the gut microbiome in allogeneic hematopoietic stem cell transplantation

ABSTRACT Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is used for the treatment of hematologic cancers and disorders. However, graft-versus-host disease (GVHD) in which the donor immune cells attack the genetically-disparate recipient is a significant cause of morbidity. Acute GVHD is an inflammatory condition and the gastrointestinal system is a major organ affected but is also tied to beneficial graft-versus-tumor (GVT) effects. There is increasing interest on the role of the microbiome on immune function as well as on cancer progression and immunotherapy outcomes. However, there are still significant unanswered questions on the role the microbiome plays in GVHD progression or how to exploit the microbiome in GVHD prevention or treatment. In this review, concepts of HSCT with the focus on GVHD pathogenesis as well as issues in preclinical models used to study GVHD will be discussed with an emphasis on the impact of the microbiome. Factors affecting the microbiome and GVHD outcome such as obesity are also examined. The bridging of preclinical models and clinical outcomes in relation to the role of the microbiome will also be discussed along with possibilities for therapeutic exploitation.