Miracle-dz/UniFAR-FTUs huggingface dataset:
Equilibrium and non-equilibrium regimes in the learning of restricted Boltzmann machines

Training restricted Boltzmann machines (RBMs) have been challenging for a long time due to the difficulty of precisely computing the log-likelihood gradient. Over the past few decades, many works have proposed more or less successful training recipes but without studying the crucial quantity of the problem: the mixing time, i.e. the number of Monte Carlo iterations needed to sample new configurations from a model. In this work, we show that this mixing time plays a crucial role in the dynamics and stability of the trained model, and that RBMs operate in two well-defined regimes, namely equilibrium and out-of-equilibrium, depending on the interplay between this mixing time of the model and the number of steps, k, used to approximate the gradient. We further show empirically that this mixing time increases with the learning, which often implies a transition from one regime to another as soon as k becomes smaller than this time. In particular, we show that using the popular k (persistent) contrastive divergence approaches, with k small, the dynamics of the learned model are extremely slow and often dominated by strong out-of-equilibrium effects. On the contrary, RBMs trained in equilibrium display faster dynamics, and a smooth convergence to dataset-like configurations during the sampling. Finally, we discuss how to exploit in practice both regimes depending on the task one aims to fulfill: (i) short k can be used to generate convincing samples in short learning times, (ii) large k (or increasingly large) is needed to learn the correct equilibrium distribution of the RBM. Finally, the existence of these two operational regimes seems to be a general property of energy based models trained via likelihood maximization.
TEM Analyses of Unusual Presolar Silicon Carbide: Insights into the Range of Circumstellar Dust Condensation Conditions

Presolar silicon carbide (SiC) grains in meteoritic samples can help constrain circumstellar condensation processes and conditions in C-rich stars and core-collapse supernovae (CCSNe). This study presents our findings on eight presolar SiC grains from asymptotic giant branch (AGB) stars (four mainstream and one Y grain) and CCSNe (three X grains), chosen on the basis of μ-Raman spectral features that were indicative of their having unusual non-3C polytypes and/or high degrees of crystal disorder. Analytical transmission electron microscopy (TEM), which provides elemental compositional and structural information, shows evidence for complex histories for the grains. Our TEM results confirm the presence of non-3C,2H crystal domains. Minor-element heterogeneities and/or subgrains were observed in all grains analyzed for their compositions. The C/O ratios inferred for the parent stars varied from 0.98 to ≥1.03. Our data show that SiC condensation can occur under a wide range of conditions, in which environmental factors other than temperature (e.g., pressure, gas composition, heterogeneous nucleation on precondensed phases) play a significant role. Based on previous μ-Raman studies, ∼10% of SiC grains may have infrared (IR) spectral features that are influenced by crystal defects, porosity, and/or subgrains. Future sub-diffraction-limited IR measurements of complex SiC grains might shed further light on the relative contributions of each of these features to the shape and position of the characteristic IR 11 μm SiC feature and thus improve the interpretation of IR spectra of AGB stars like those that produced the presolar SiC grains.
The piranha problem: Large effects swimming in a small pond

In some scientific fields, it is common to have certain variables of interest that are of particular importance and for which there are many studies indicating a relationship with different explanatory variables. In such cases, particularly those where no relationships are known among the explanatory variables, it is worth asking under what conditions it is possible for all such claimed effects to exist simultaneously. This paper addresses this question by reviewing some theorems from multivariate analysis showing that, unless the explanatory variables also have sizable dependencies with each other, it is impossible to have many such large effects. We discuss implications for the replication crisis in social science.
Alleviating the Knowledge-Language Inconsistency: A Study for Deep Commonsense Knowledge

Knowledge facts are typically represented by relational triples, while we observe that some commonsense facts are represented by triples whose forms are inconsistent with the corresponding language expressions. For commonsense mining tasks, this inconsistency raises a challenge for the prevailing methods using pre-trained language models that learn the expression of language. However, there are few studies which focus on this inconsistency issue. To fill this empty, in this paper, we term the commonsense knowledge whose triple form is heavily inconsistent with the language expression as deep commonsense knowledge and first conduct extensive exploratory experiments to study deep commonsense knowledge. We show that deep commonsense knowledge occupies a significant part of commonsense knowledge, while the conventional methods based on pre-trained language models fail to capture it effectively. We further propose a novel method to mine the deep commonsense knowledge from raw text that is exactly language expression, alleviating the reliance of conventional methods on the triple representation form. Experiments demonstrate that our proposed method substantially improves the performance in mining deep commonsense knowledge.
Pull Request Decisions Explained: An Empirical Overview

Context: The pull-based development model is widely used in open source projects, leading to the emergence of trends in distributed software development. One aspect that has garnered significant attention concerning pull request decisions is the identification of explanatory factors. Objective: This study builds on a decade of research on pull request decisions and provides further insights. We empirically investigate how factors influence pull request decisions and the scenarios that change the influence of such factors. Method: We identify factors influencing pull request decisions on GitHub through a systematic literature review and infer them by mining archival data. We collect a total of 3,347,937 pull requests with 95 features from 11,230 diverse projects on GitHub. Using these data, we explore the relations among the factors and build mixed effects logistic regression models to empirically explain pull request decisions. Results: Our study shows that a small number of factors explain pull request decisions, with that concerning whether the integrator is the same as or different from the submitter being the most important factor. We also note that the influence of factors on pull request decisions change with a change in context; e.g., the area hotness of pull request is important only in the early stage of project development, however it becomes unimportant for pull request decisions as projects become mature.
On the Special Identities of Gelfand-Dorfman Algebras

In this paper, we prove that the class of all special Gelfand--Dorfman algebras (GD-algebras) is closed with respect to homomorphisms and thus forms a variety. We also prove that every 2-dimensional GD-algebra is special. For the latter, we give a technical method to find all special identities of GD-algebras and compute the degree 6 component of the Gr\"obner basis for the shuffle operad constructed on the symmetric operad governing the class of GD-algebras.
Training With Data Dependent Dynamic Learning Rates

Recently many first and second order variants of SGD have been proposed to facilitate training of Deep Neural Networks (DNNs). A common limitation of these works stem from the fact that they use the same learning rate across all instances present in the dataset. This setting is widely adopted under the assumption that loss functions for each instance are similar in nature, and hence, a common learning rate can be used. In this work, we relax this assumption and propose an optimization framework which accounts for difference in loss function characteristics across instances. More specifically, our optimizer learns a dynamic learning rate for each instance present in the dataset. Learning a dynamic learning rate for each instance allows our optimization framework to focus on different modes of training data during optimization. When applied to an image classification task, across different CNN architectures, learning dynamic learning rates leads to consistent gains over standard optimizers. When applied to a dataset containing corrupt instances, our framework reduces the learning rates on noisy instances, and improves over the state-of-the-art. Finally, we show that our optimization framework can be used for personalization of a machine learning model towards a known targeted data distribution.
Implications of PREX-2 on the electric dipole polarizability of neutron rich nuclei

Methods: Covariant energy density functionals informed by the properties of finite nuclei are used to compute the electric dipole response of Ca, Ni, Sn, and Pb. The set of functionals used in this work are consistent with experimental data, yet are flexible enough in that they span a wide range of values of R skin. Results: It is found that theoretical predictions of the electric dipole polarizability that are consistent with the PREX-2 measurement systematically overestimate the corresponding values extracted from the direct measurements of the distribution of electric dipole strength. Conclusions: The neutron skin thickness of Pb extracted from parity violating electron scattering and the electric dipole polarizability measured in photoabsorption experiments are two of the cleanest experimental tools used to constrain the symmetry energy around nuclear saturation density. However, the recent value of R skin that suggests a fairly stiff symmetry energy stands in stark contrast to the conclusions derived from the electric dipole polarizability. At present, we offer no solution to this dilemma.
Knowledge Inheritance for Pre-trained Language Models

Recent explorations of large-scale pre-trained language models (PLMs) have revealed the power of PLMs with huge amounts of parameters, setting off a wave of training ever-larger PLMs. However, it requires tremendous computational resources to train a large-scale PLM, which may be practically unaffordable. In addition, existing large-scale PLMs are mainly trained from scratch individually, ignoring that many well-trained PLMs are available. To this end, we explore the question how could existing PLMs benefit training large-scale PLMs in future. Specifically, we introduce a pre-training framework named “knowledge inheritance” (KI) and explore how could knowledge distillation serve as auxiliary supervision during pre-training to efficiently learn larger PLMs. Experimental results demonstrate the superiority of KI in training efficiency. We also conduct empirical analyses to explore the effects of teacher PLMs’ pre-training settings, including model architecture, pre-training data, etc. Finally, we show that KI could be applied to domain adaptation and knowledge transfer.
Open-world Machine Learning: Applications, Challenges, and Opportunities

Traditional machine learning, mainly supervised learning, follows the assumptions of closed-world learning, i.e., for each testing class, a training class is available. However, such machine learning models fail to identify the classes, which were not available during training time. These classes can be referred to as unseen classes. Open-world Machine Learning (OWML) is a novel technique, which deals with unseen classes. Although OWML is around for a few years and many significant research works have been carried out in this domain, there is no comprehensive survey of the characteristics, applications, and impact of OWML on the major research areas. In this article, we aimed to capture the different dimensions of OWML with respect to other traditional machine learning models. We have thoroughly analyzed the existing literature and provided a novel taxonomy of OWML considering its two major application domains: Computer Vision and Natural Language Processing. We listed the available software packages and open datasets in OWML for future researchers. Finally, the article concludes with a set of research gaps, open challenges, and future directions.
Essential oil composition of bark and leaves of Cinammoum verum Bertch. & Presl from Mizoram, North East India

Abstract Stem bark and leaf essential oil of Cinammoum verum Bertch. & Presl in Mizoram India was extracted and analyzed for composition. On hydro distillation, 0.87% and 2.13% (w/w) essential oil was yielded from leaves and bark, respectively. Overall, twenty compounds were identified in bark and twenty-one in leaf oil using GC-FID and GC–MS analysis. The major components of bark essential oil were methyl cinnamate (81.87%), linalool (3.90%) and αpinene (2.41%) whereas; linalool (67.60%), methyl cinnamate (17.32%) and αpinene (2.74%) were major components of leaf volatile oil. On comparison of our results with earlier studies on various species of Cinnamon as well as C. verum, marked variations exist not only on identity of compounds but also in their percent share in the composition of essential oil. Our observation indicates possibility of Cinnamic aldehyde and eugenol derivatives rich chemotype (Mizoram) of C. verum. Besides, factors which may affect oil composition are also discussed.
DISTRIBUTION AND PREDICTION ON HEAVY METALS POLLUTION LEVEL (Pb, Cd, Cu, Zn, and Ni) IN SEDIMENT IN BANGKA ISLAND WATERS USING LOAD POLLUTION INDEX AND GEOACCUMULATION INDEX

Bangka Island is rich in natural resources particularly tin minerals. The increasing of tin mining has elevated various wastes such as tailings, oil, and fuel coming from the sand scraper tin boat. These wastes containing toxic heavy metals may harmful to marine organism. Measurement of Pb, Cd, Cu, Zn, and Ni were carried out in September 2010. The purpose of this research was to predict the pollution degree of Pb, Cd, Cu, Zn, and Ni in sediment using two different methods namely geoaccumulation index (I_geo) and pollution load index (PLI). The samples of sediments were collected at 20 stations using Gravity Core. The content of heavy metals in all samples was determined using Atomic Absorption Spectrophotometer with a mixture of air and acetylene flame. The results showed that there was a different of prediction on sediment pollution level between Load Pollution Index with Geoaccumulation Index. According to Load Pollution Index, sediments in this waters were not polluted by Pb, Cd, Cu, Zn, and Ni (PLI<1). Based on Geoaccumulation Index, sediment were also not polluted by Pb, Cu, Zn, and Ni (Igeo<0). While for Cd, sediments divided into three categories, namely not polluted (Igeo<0), light polluted (0<Igeo<1), and medium pollued (1<Igeo<2). The concentration of the heavy metals still lower than that sediment quality guideline values. Keywords: Bangka Island, heavy metals, geoaccumulation index, pollution load index.
SGLT2 inhibitors and the cardiac Na+/H+ exchanger-1: the plot thickens

British Heart Foundation Centre of Research Excellence, King’s College London, UK; Burdon Sanderson Cardiac Science Centre, Department of Anatomy, Physiology and Genetics, University of Oxford, Oxford, UK; School of Biomedical Engineering and Imaging Sciences, King’s College London, UK; Institute of Cardiovascular & Medical Sciences, University of Glasgow, UK; and Institute for Cardiovascular Sciences, University of Birmingham, UK
Serum lipocalin‐2 and carotid artery intima–media thickness in relation to obesity in eugonadal males over forty with venogenic erectile dysfunction

Obesity is a risk factor for erectile dysfunction and atherosclerosis. Lipocalin‐2 is an adipocytokine with proinflammatory properties involved in several disorders with metabolic alterations. Our aim was to study the relation of serum lipocalin‐2 and carotid artery intima–media thickness (CIMT) to obesity in erectile dysfunction. Serum lipocalin‐2 and CIMT were measured in 25 obese and 25 nonobese eugonadal patients over forty with venogenic erectile dysfunction and 25 healthy controls. Their relation to different patient‐ and disease‐related parameters was studied. Results revealed lipocalin‐2 to be significantly higher in obese compared with nonobese patients and with controls, and in nonobese patients compared with controls. CIMT was lower in controls compared with both obese and nonobese patients. In obese and nonobese patients, lipocalin‐2 was positively correlated with disease duration, body mass index, waist circumference and end‐diastolic velocity. Lipocalin‐2 was negatively correlated with the short form of the international index of erectile function scores in both groups. In conclusion, the elevated lipocalin‐2 in obese and to a lesser extent in nonobese patients and its association with disease severity points to its potential value as a diagnostic marker and a possible therapeutic target that could ameliorate the metabolic derangement associated with erectile dysfunction.
Pressure‐controlled intermittent coronary sinus occlusion improves the vasodilatory microvascular capacity and reduces myocardial injury in patients with STEMI

Preliminary data suggest that pressure‐controlled intermittent coronary sinus occlusion (PICSO) might reduce the infarct size (IS) in patients with anterior ST‐elevation myocardial infarction (STEMI). However, the applicability of this therapy to patients with inferior STEMI and its exact mechanism of action is uncertain.
Increased C-Peptide Immunoreactivity in Insulin Autoimmune Syndrome (Hirata Disease) Due to High Molecular Weight Proinsulin

Abstract Background Determination of C-peptide is important in the investigation of unexplained hyperinsulinemic hypoglycemia because a high C-peptide concentration usually indicates endogenous insulin hypersecretion. Insulin autoimmune syndrome (IAS) denotes hyperinsulinemic hypoglycemia due to insulin-binding antibodies that prolong insulin half-life. C-peptide clearance is considered to be unaffected, and although a marked C-peptide immunoreactivity in hypoglycemic samples has been reported, it has been suspected to be artifactual. High-resolution mass spectrometry enables examination of the basis of C-peptide-immunoreactivity in IAS. Methods Precipitation of plasma with polyethylene glycol was followed by C-peptide immunoassay. Plasma peptides extracted by solvent precipitation were characterized by nano-LC–MS/MS and analyzed using an untargeted data-dependent method. Peptides related to proinsulin, in amino acid sequence, were identified using proprietary bioinformatics software and confirmed by repeat LC–MS/MS analysis. Gel filtration chromatography coupled to LC–MS/MS was used to identify proinsulin-related peptides present in IAS immunocomplexes. Results were compared with those from C-peptide immunoassay. Results Polyethylene glycol precipitation of IAS plasma, but not control plasma, depleted C-peptide immunoreactivity consistent with immunoglobulin-bound C-peptide immunoreactivity. LC–MS/MS detected proinsulin and des 31,32 proinsulin at higher abundance in IAS plasma compared with control plasma. Analysis by gel filtration chromatography coupled to LC–MS/MS demonstrated proinsulin and des 31,32 proinsulin, but no C-peptide, in plasma immunocomplexes. Conclusions Antibody binding can enrich proinsulin and des 31,32 proinsulin in IAS immunocomplexes. Proinsulin cross-reactivity in some C-peptide immunoassays can lead to artifactually increased C-peptide results.
Solar photovoltaic as a means to sustainable energy consumption in Malaysia: the role of knowledge and price value

ABSTRACT Despite various initiatives from the government, merely two per cent of Malaysia’s electricity is produced by renewable energy sources including solar panels. Currently, little is known about the predictors for households’ behavioral intention of utilizing solar photovoltaic systems in Malaysia. Thus, this study aims to investigate the role of knowledge and price value in affecting the adoption of solar system among Malaysian households, based on the Unified theory of acceptance and the use of technology. Results based on a quantitative survey indicated that knowledge affects effort expectancy, social influence, and facilitating conditions positively. Meanwhile, the price value had a direct effect on performance expectancy, effort expectancy, social influence, and facilitating conditions. Additionally, performance expectancy, effort expectancy, social influence, and facilitating conditions were all positively related to behavioral intention. This study provides valuable references for Malaysian government to promote the use of solar energy.
On gradient estimates for heat kernels

Abstract We study pointwise and Lp gradient estimates of the heat kernels of both the scalar Laplacian, as well as the Hodge Laplacian on k-forms, on manifolds that may have some amount of negative Ricci curvature, provided it is not too negative (in an integral sense) at infinity. Such heat kernel estimates have already been obtained by the author, together with Coulhon and Sikora, provided certain L 2-cohomology spaces are trivial. This is however a strong topological assumption, and it is desirable to weaken it. The main point of the current work is to investigate what happens when these L 2-cohomology spaces are non-trivial. We find that the answer depends on some Lq integrability properties of L 2-harmonic forms.
Smart platform experiment cycle (SPEC): a process to design, analyze, and validate digital platforms

Abstract Digital platform business models are disrupting traditional business processes and reveal a new way of creating value. Current validation processes for business models are designed to assess pipeline business models. They cannot grasp the logic of digital platforms, which increasingly integrate Artificial Intelligence (AI) to ensure success. This study developed a new validation process for early market validation of digital platform business models by following the Design Science Research methodology. The designed process, the Smart Platform Experiment Cycle (SPEC), is created by combining the Four-Step Iterative Cycle of business experiments, the Customer Development Process, and the Build-Measure-Learn feedback loop of the Lean Startup approach and enriching it with the knowledge of digital platforms. It consists of five iterative steps showing the startup how to design their platform business model and corresponding experiments and how to run, measure, analyze, and learn from the outcomes and results. To assess its efficacy, applicability, and validity, SPEC was applied in the German startup GassiAlarm, a service marketplace business model. The application of SPEC revealed shortcomings in the pricing strategy and highlighted to what extent their current business model would be successful. SPEC reduces the risk of building a product or service the market deems redundant and gives insights into its success rate. More applications of the SPEC are needed to validate its robustness further and to extend it to other types of digital platform business models for improved generalization.