Miracle-dz/UniFAR-FTUs huggingface dataset:
Hierarchical Bayesian Analysis using Monte Carlo Integration: Computing Posterior Distributions when

A Bayesian approach allows the statistician to compute the posterior probability for each model in a set of possible models and therefore to retain consideration of several or many models throughout the analysis rather than to restrict attention to just one 'best' model. This paper illustrates the use of Monte Carlo integration in hierarchical Bayesian analysis when there are many possible models of different dimensions. A hierarchical approach can facilitate the choice of a prior distribution as well as the Monte Carlo computation. The methodology is illustrated by an example in multiple logistic regression involving 256 possible models.
Push-Out Bond Strength and Dentinal Penetration of a Novel Herbal-Based Pulp Capping Agent: An In vitro Study

Background: Pulp capping agents should have proper sealing effect to enhance dental pulp tissue healing. Aim: The purpose of this study was to compare the effectiveness of grape seed extract (GSE) and Mineral trioxide aggregate (MTA) to penetrate to dentin and their push-out bond strength at two time intervals (1 and 3 months) when used as pulp capping agents either singly or combined to each other. Materials and Methods: This study was conducted on 120 human single-rooted anterior teeth. Sixty dentin discs were randomly divided into three groups (n=20) based on the material used; MTA, GSE, and a combination of MTA and GSE. A universal testing machine was used to determine the push-out bond strength for one and three months. At the same time intervals, extra 60 teeth with the same groups were utilized to quantify the degree of capping material penetration within the dentinal tubules using scanning electron microscopy (SEM). ANOVA with multiple comparison Post hoc test was used to evaluate the data where the p value was < 0.05. Results: MTA had the highest push-out bond strength and penetration depth measurement into dentinal tubules at one month, followed by MTA combined with GSE, while GSE had the lowest push-out bond strength and penetration depth measurement. Nevertheless, GSE had the greatest values in both tests at 3 months, followed by MTA, while MTA coupled with GSE had the lowest value in both tests. Conclusion: Push out bond strength and dentinal penetration depth were improved with time except for the MTA group testing its dentinal penetration depth. GSE shows good push out bond strength and dentinal penetration depth.
Logistics Demand Forecast of Fresh Food E-Commerce Based on Bi-LSTM Model

Fresh products have the characteristics of perishable, small batch and high frequency. Therefore, for fresh food e-commerce enterprises, market demand forecasting is particularly important. This paper takes the sales data of a fresh food e-commerce enterprise as the logistics demand, analyzes the influence of time and meteorological factors on the demand, extracts the charac-teristic factors with greater influence, and proposes a logistics demand forecast scheme of fresh food e-commerce based on the Bi-LSTM model. The scheme is compared with other schemes based on the BP neural network and LSTM neural network models. The experimental results show that the Bi-LSTM model has good prediction performance on the problem of logistics demand prediction. This facilitates further research on some supply chain issues, such as business decision-making, inventory control, and logistics capacity planning.
Age-related metabolic and neurodegenerative changes in SAMP8 mice

The most important risk factor for the development of sporadic Alzheimer’s disease (AD) is ageing. Senescence accelerated mouse prone 8 (SAMP8) is a model of sporadic AD, with senescence accelerated resistant mouse (SAMR1) as a control. In this study, we aimed to determine the onset of senescence-induced neurodegeneration and the related potential therapeutic window using behavioral experiments, immunohistochemistry and western blotting in SAMP8 and SAMR1 mice at 3, 6 and 9 months of age. The Y-maze revealed significantly impaired working spatial memory of SAMP8 mice from the 6th month. With ageing, increasing plasma concentrations of proinflammatory cytokines in SAMP8 mice were detected as well as significantly increased astrocytosis in the cortex and microgliosis in the brainstem. Moreover, from the 3rd month, SAMP8 mice displayed a decreased number of neurons and neurogenesis in the hippocampus. From the 6th month, increased pathological phosphorylation of tau protein at Thr231 and Ser214 was observed in the hippocampi of SAMP8 mice. In conclusion, changes specific for neurodegenerative processes were observed between the 3rd and 6th month of age in SAMP8 mice; thus, potential neuroprotective interventions could be applied between these ages.
Ramanujan's Master theorem for the hypergeometric Fourier transform on root systems

Ramanujan's Master theorem states that, under suitable conditions, the Mellin transform of an alternating power series provides an interpolation formula for the coefficients of this series. Ramanujan applied this theorem to compute several definite integrals and power series, which explains why it is referred to as the "Master Theorem". In this paper we prove an analogue of Ramanujan's Master theorem for the hypergeometric Fourier transform on root systems. This theorem generalizes to arbitrary positive multiplicity functions the results previously proven by the same authors for the spherical Fourier transform on semisimple Riemannian symmetric spaces.
An End-to-End Macaque Voiceprint Verification Method Based on Channel Fusion Mechanism

Primates are facing a serious survival crisis. Tracking the range of animal activities and population changes is of great significance for efficient animal protection. Primates are highly alert and inaccessible to humans so that it is difficult to track animals through direct observation, DNA fingerprinting, or marking methods. Primate recognition based on animal calls has the advantages of wide monitoring range, low equipment cost, and good concealment. In this work, we propose an effective macaque speech feature extraction structure, and innovatively propose a feature fusion mechanism to effectively obtain the feature representation of each call. Furthermore, we construct a public open source macaque voiceprint verification dataset. The experimental results show that the proposed method is superior to the existing state-of-the-art human voiceprint verification algorithms with different call durations. The equal error rate (EER) of our macaque voiceprint verification algorithm reaches 6.19%.
Dimension in T.T.T Structures

In this paper we consider two types of dimension that can be defined for products of one-dimensional topologically totally transcendental (t.t.t) structures. The first is topological and considers the interior of projections of the set onto lower dimensional products. The second one is based on algebraic dependence. We show that these definitions are equivalent for \omega -saturated one-dimensional t.t.t structures. We also prove that sets which are dense in products of these structures are comeager.
Oral Fluorescein Angiography with Ultra-Wide-Field Scanning Laser Ophthalmoscopy in Pediatric Patients Precis: Oral Fluorescein Angiography in Children

Aims: To investigate the success rate of oral fluorescein angiography (oral FA) in children with ultrawide scanning laser ophthalmoscopy (SLO) system and whether it can provide images of sufficient quality compared with intravenous FA (IVFA). Methods: In this comparative case series study, a series of 40 consecutive pediatric patients of the age of 3–18 with retinal vascular diseases, in whom FA was needed for the diagnosis or treatment, were enrolled in this study. IVFA and oral FA were performed within one week and images were obtained with the SLO system. The image quality was scored blindly and compared based on: (I) visualization of the branch retinal vessel, (II) the foveal avascular zone (FAZ), and (III) clinically important findings, such as the presence of microaneurysms, neovascularization, leakage, or significant nonperfusion. All these were scored using a three-point scale. Results: In preschoolers (three to six years), all 19 children complete oral FA (100%), while only 7 (36.84%) complete IVFA (p < 0.0001). With the SLO system, the branch retinal vessels were well visualized both in oral and IV FA (all images were two scores). The visualization of FAZ was similar between oral and IV FA (p = 0.8972). The clinically important findings were well visualized in both groups (p > 0.9999). The overall image quality was similar between the two groups (p = 0.2500). Conclusion: Oral FA is more acceptable to preschoolers than IVFA owing to the needle-free procedure. With the SLO system, oral FA provided high-quality angiograms similar to IVFA. Oral FA is an effective alternative to IVFA and may be considered the first option for FA in pediatric patients, especially in preschoolers.
Localization of Passively Guided Capsule Endoscopes—A Review

Traditional endoscopy is conducted stationary in the hospital and requires sedation of the patient. Contrary, capsule endoscopy (CE) requires no sedation, and the patients are allowed to leave the hospital for their daily life during the procedure that lasts for several hours. Commercially available capsule endoscopes are passively moved through the gastrointestinal tract (GIT) by peristalsis. For diagnosis and intervention planning, the traveled distance and orientation of the capsules for each video frame would be highly relevant. Nevertheless, commercial capsule endoscope systems do not offer precise localization of the capsules. In the past years, many different scientific localization approaches have been proposed. These methods can be divided into video-, radio frequency-(RF), and magnetic field-based methods. In this review, a requirement profile for a localization method suitable for a commercial capsule endoscopy system is defined. The defined requirements of a localization system for capsule endoscopy are high accuracy, robustness during the daily life of a patient, and integrability with commercial capsule endoscopy systems. Afterward, promising approaches are reviewed and discussed concerning these requirements. It was revealed that the magnetic field- and video-based methods are the most promising methods, and these methods would complement each other very well. Therefore, a hybrid magnetic-field-/video-based method is suggested, which has, to the best of our knowledge, not been proposed so far.
End-to-End Audio-Visual Neural Speaker Diarization

In this paper, we propose a novel end-to-end neural-network-based audio-visual speaker diarization method. Unlike most existing audio-visual methods, our audio-visual model takes audio features (e.g., FBANKs), multi-speaker lip regions of interest (ROIs), and multi-speaker i-vector embbedings as multi-modal inputs. And a set of binary classiﬁcation output layers produces activities of each speaker. With the ﬁnely designed end-to-end structure, the proposed method can explicitly handle the overlapping speech and distinguish between speech and non-speech accurately with multi-modal information. I-vectors are the key point to solve the alignment problem caused by visual modality error (e.g., occlusions, off-screen speakers or un-reliable detection). Besides, our audio-visual model is robust to the absence of visual modality, where the diarization performance degrades signiﬁcantly using the visual-only model. Evaluated on the datasets of the ﬁrst multi-model information based speech processing (MISP) challenge, the proposed method achieved diarization error rates (DERs) of 10.1%/9.5% on development/eval set with reference voice activity detection (VAD) information, while audio-only and video-only system yielded DERs of 27.9%/29.0% and 14.6%/13.1% respectively.
Attentive Training: A New Training Framework for Talker-independent Speaker Extraction

Listening in a multitalker scenario, we typically attend to a single talker through auditory selective attention. Inspired by human selective attention, we propose attentive training: a new training framework for talker-independent speaker extraction with an intrinsic selection mechanism. In the real world, multiple talkers very unlikely start speaking at the same time. Based on this observation, we train a deep neural network to create a representation for the first speaker and utilize it to extract or track that speaker from a multitalker noisy mixture. Experimental results demonstrate the superiority of attentive training over widely used permutation invariant training for talker-independent speaker extraction, especially in mismatched conditions in terms of the number of speakers, speaker interaction patterns, and the amount of speaker overlaps.
Don't Let Perfect Be the Enemy of Good: A Proof of Concept for a Custom National Data Repository of Quality Measures for Free and Charitable Clinics

Purpose: Free and charitable clinics (FCCs), nonprofits that utilize volunteer licensed health care professionals to provide health services at no cost or a small fee to low-income uninsured patients who are disproportionately from underrepresented communities, have been part of the safety net for over a century. Approximately 1400 known FCCs serve two million patients annually. Despite their longevity and sizable number, evidence regarding the quality of care in FCCs is lacking. We report new evidence generated by a national initiative, the Roadmap to Health Equity. Established in 2017, this consortium is co-led by two national organizations serving FCCs and an academic institution. It has involved more than 150 FCC stakeholders with the shared goal of improving the quality of care and reducing inequities. The centerpiece is a custom national data repository of 15 validated clinical quality measures and patient-level characteristics. Methods: Fifty FCCs pilot tested the data repository. Clinics submitted patient-level data on two blood pressure (BP) measures and at least one additional measure. Descriptive statistics were stratified by sex, race, ethnicity, and language. Results: In 2021, 33 pilot FCCs from 21 states reported data across 13 of the 15 clinical measures, representing 34,359 unique patients. For example, on average, 60% of patients had controlled BP, but Black patients had lower rates of BP control than Hispanic and White patients (55.9% vs. 62.1% and 63.0%, respectively). Conclusion: Our findings demonstrate a proof of concept. By standardizing quality measures alongside patient characteristics, clinics can become aware of racial/ethnic inequalities in health outcomes. This information can motivate clinics to investigate the causes and implement solutions. In an environment where outcome data from FCCs are scarce, the new national data repository lays the foundation for routine stratified reporting of a range of quality outcomes for an important safety net for the uninsured.
Text-to-speech synthesis using spectral modeling based on non-negative autoencoder

This paper proposes a statistical parametric speech synthesis system that uses non-negative autoencoder (NAE) for spectral modeling. NAE is a model that extends non-negative matrix factorization (NMF) as neural networks. In the proposed method, we employ latent variables in NAE as acoustic features. Reconstruction of spectral information and estimation of latent variables are simultaneously trained. The non-negativity of latent variables in NAE is expected to contribute to dimensionality reduction such that the fine structure of the spectral envelopes is preserved. Experimental results demonstrates the effectiveness of the proposed framework. We also study multi-speaker modeling where each of NAEs corresponds to each single speaker. In addition, a neural source-filter (NSF) model was applied to the waveform generation. When a neural vocoder is trained with natural acoustic features and tested with synthesized features, quality degradation occurs due to the mismatch between training and test data. In order to mitigate the mismatch, this system uses features obtained by reconstructing nat-ural speech using NAE for training. Experimental results show that reconstructed features are similar to synthesized features, and as a result, the quality of the synthesized speech is improved.
Classify Symmetry-Protected Topological Orders for Holographic Conformal Field Theories

In the context of AdS/CFT correspondence, we propose how to define the (symmetry-protected) topological orders for the holographic conformal field theories (CFTs) via the non-normalizable modes of the anti-de Sitter (AdS) bulk fields. To be specific, we consider the sector of the CFTs which is dual to the multi-flavor massive free fermions in AdS space. We demonstrate that the classification of the topological edge modes on the AdS boundary can be done by classifying the symmetry-protected topological orders of the same fermion system in Minkowski space. We then apply Kitaev's K-theory analysis to classifying the symmetry-protected topological orders as done for usual topological insulators/superconductors. The results are translated into the ones for the holographic CFTs. Our proposal goes beyond the usual classification scheme of topological orders for the gapped systems.
Clinical Inertia in the Management of Type 2 Diabetes Mellitus: A Systematic Review

This review seeks to establish, through the recent available literature, the prevalence of therapeutic intensification delay and its sequences in poorly controlled Type 2 Diabetes Mellitus (T2DM) patients. The strategy identified studies exploring the clinical inertia and its associated factors in the treatment of patients with T2DM. A total of 25 studies meeting the pre-established quality criteria were included in this review. These studies were conducted between 2004 and 2021 and represented 575,067 patients diagnosed with T2DM. Trusted electronic bibliographic databases, including Medline, Embase, and the Cochrane Central Register of Controlled Trials, were used to collect studies by utilizing a comprehensive set of search terms to identify Medical Subject Headings (MeSH) terms. Most o the studies included in this review showed clinical inertia rates over 50% of T2DM patients. In the USA, clinical inertia ranged from 35.4% to 85.8%. In the UK, clinical inertia ranged from 22.1% to 69.1%. In Spain, clinical inertia ranged from 18.1% to 60%. In Canada, Brazil, and Thailand, clinical inertia was reported as 65.8%, 68%, and 68.4%, respectively. The highest clinical inertia was reported in the USA (85.8%). A significant number of patients with T2DM suffered from poor glycemic control for quite a long time before treatment intensification with oral antidiabetic drugs (OADs) or insulin. Barriers to treatment intensification exist at the provider, patient, and system levels. There are deficiencies pointed out by this review at specialized centers in terms of clinical inertia in the management of T2DM including in developed countries. This review shows that the earlier intensification in the T2DM treatment is appropriate to address issues around therapeutic inertia.
Increased Respiratory Drive after Prolonged Isoflurane Sedation: A Retrospective Cohort Study

Low-dose isoflurane stimulates spontaneous breathing. We, therefore, tested the hypothesis that isoflurane compared to propofol sedation for at least 48 h is associated with increased respiratory drive in intensive care patients after sedation stop. All patients in our intensive care unit receiving at least 48 h of isoflurane or propofol sedation in 2019 were included. The primary outcome was increased respiratory drive over 72 h after sedation stop, defined as an arterial carbon dioxide pressure below 35 mmHg and a base excess more than −2 mmol/L. Secondary outcomes were acid–base balance and ventilatory parameters. We analyzed 64 patients, 23 patients sedated with isoflurane and 41 patients sedated with propofol. Patients sedated with isoflurane were about three times as likely to show increased respiratory drive after sedation stop than those sedated with propofol: adjusted risk ratio [95% confidence interval]: 2.9 [1.3, 6.5], p = 0.010. After sedation stop, tidal volumes were significantly greater and arterial carbon dioxide partial pressures were significantly lower, while respiratory rates did not differ in isoflurane versus propofol-sedated patients. In conclusion, prolonged isoflurane use in intensive care patients is associated with increased respiratory drive after sedation stop. Beneficial effects of isoflurane sedation on respiratory drive may, thus, extend beyond the actual period of sedation.
Child labor and unfree labor: Evidence from the palm oil sector in Sabah (East Malaysia)

Abstract By focusing on child labor in oil palm production in Sabah (East Malaysia), this study contributes to the ongoing debate about unfree labor from three perspectives. First, the study highlights the importance of measuring the incidence of child labor from an individual basis and subsequently advances discourse on what is acceptable and unacceptable in the context of children’s involvement in oil palm production. Second, it advances children’s voices that reflect their perceptions, everyday realities, and social expectations behind their active participation in oil palm production. Third, it highlights the reoccurrence of unfree labor relations and conditions as a result of employment transition among working children within the agricultural sector and from agricultural to the fishing sector. Here we argue that, although not all children’s participation in oil palm production constitutes child labor, the presence of unfree and unacceptable labor relations and conditions suggests strong indicators of child labor. This study, however, cautions that the blanket use of child labor may not only ignore local dynamics and societal expectations but also hinder effective policy intervention and business actions.
Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humans

We are currently unable to specify human goals and societal values in a way that reliably directs AI behavior. Law-making and legal interpretation form a computational engine that converts opaque human values into legible directives. "Law Informs Code"is the research agenda embedding legal knowledge and reasoning in AI. Similar to how parties to a legal contract cannot foresee every potential contingency of their future relationship, and legislators cannot predict all the circumstances under which their proposed bills will be applied, we cannot ex ante specify rules that provably direct good AI behavior. Legal theory and practice have developed arrays of tools to address these specification problems. For instance, legal standards allow humans to develop shared understandings and adapt them to novel situations. In contrast to more prosaic uses of the law (e.g., as a deterrent of bad behavior through the threat of sanction), leveraged as an expression of how humans communicate their goals, and what society values, Law Informs Code. We describe how data generated by legal processes (methods of law-making, statutory interpretation, contract drafting, applications of legal standards, legal reasoning, etc.) can facilitate the robust specification of inherently vague human goals. This increases human-AI alignment and the local usefulness of AI. Toward society-AI alignment, we present a framework for understanding law as the applied philosophy of multi-agent alignment. Although law is partly a reflection of historically contingent political power - and thus not a perfect aggregation of citizen preferences - if properly parsed, its distillation offers the most legitimate computational comprehension of societal values available. If law eventually informs powerful AI, engaging in the deliberative political process to improve law takes on even more meaning.