Miracle-dz/UniFAR-FTUs huggingface dataset:
A Universal Method for Solar Filament Detection from H-alpha Observations using Semi-supervised Deep Learning

Filaments are omnipresent features in the solar atmosphere. Their location, properties and time evolution can provide information about changes in solar activity and assist the operational space weather forecast. Therefore, filaments have to be identified in full disk images and their properties extracted from these images. Manual extraction is tedious and takes much time; extraction with morphological image processing tools produces a large number of false-positive detections. Automatic object detection, segmentation, and extraction in a reliable manner allows to process more data in a shorter time. The Chromospheric Telescope (ChroTel), Tenerife, Spain, the Global Oscillation Network Group (GONG), and the Kanzelh\"ohe Observatory (KSO), Austria, provide regular full-disk observations of the Sun in the core of the chromospheric H-alpha absorption line. We present a deep learning method that provides reliable extractions of filaments from H-alpha filtergrams. First, we train the object detection algorithm YOLOv5 with labeled filament data of ChroTel. We use the trained model to obtain bounding-boxes from the full GONG archive. In a second step, we apply a semi-supervised training approach, where we use the bounding boxes of filaments, to learn a pixel-wise classification of filaments with u-net. Here, we make use of the increased data set size to avoid overfitting of spurious artifacts from the generated training masks. Filaments are predicted with an accuracy of 92%. With the resulting filament segmentations, physical parameters such as the area or tilt angle can be easily determined and studied. This we demonstrate in one example, where we determine the rush-to-the pole for Solar Cycle 24 from the segmented GONG images. In a last step, we apply the filament detection to H-alpha observations from KSO which demonstrates the general applicability of our method to H-alpha filtergrams.
Fixed-Lag Smoothing for Bayes Optimal Knowledge Exploitation in Target Tracking

In this work, we are interested in the improvements attainable when multiscan processing of external knowledge is performed over a moving time window. We propose a novel algorithm that enforces the state constraints by using a Fixed-Lag Smoothing procedure within the prediction step of the Bayesian recursion. For proving the improvements, we utilize differential entropy as a measure of uncertainty and show that the approach guarantees a lower or equal posterior differential entropy than classical single-step constrained filtering. Simulation results using examples for single-target tracking are presented to verify that a Sequential Monte Carlo implementation of the proposed algorithm guarantees an improved tracking accuracy.
Target Fishing: A Single-Label or Multi-Label Problem?

According to Cobanoglu et al and Murphy, it is now widely acknowledged that the single target paradigm (one protein or target, one disease, one drug) that has been the dominant premise in drug development in the recent past is untenable. More often than not, a drug-like compound (ligand) can be promiscuous - that is, it can interact with more than one target protein. In recent years, in in silico target prediction methods the promiscuity issue has been approached computationally in different ways. In this study we confine attention to the so-called ligand-based target prediction machine learning approaches, commonly referred to as target-fishing. With a few exceptions, the target-fishing approaches that are currently ubiquitous in cheminformatics literature can be essentially viewed as single-label multi-classification schemes; these approaches inherently bank on the single target paradigm assumption that a ligand can home in on one specific target. In order to address the ligand promiscuity issue, one might be able to cast target-fishing as a multi-label multi-class classification problem. For illustrative and comparison purposes, single-label and multi-label Naive Bayes classification models (denoted here by SMM and MMM, respectively) for target-fishing were implemented. The models were constructed and tested on 65,587 compounds and 308 targets retrieved from the ChEMBL17 database. SMM and MMM performed differently: for 16,344 test compounds, the MMM model returned recall and precision values of 0.8058 and 0.6622, respectively; the corresponding recall and precision values yielded by the SMM model were 0.7805 and 0.7596, respectively. However, at a significance level of 0.05 and one degree of freedom McNemar test performed on the target prediction results returned by SMM and MMM for the 16,344 test ligands gave a chi-squared value of 15.656, in favour of the MMM approach.
A GPI processing phospholipase A2, PGAP6, modulates Nodal signaling in embryos by shedding CRIPTO

Lee et al. show that PGAP6 is a glycosylphosphatidylinositol (GPI)-specific phospholipase A2 expressed on the cell surface. PGAP6 selectively acts on a GPI anchor of CRIPTO, but not its close homologue CRYPTIC, and modulates Nodal signaling during embryonic development.
Polymorphisms of Serotonin Receptor 2A and 2C Genes and COMT in Relation to Obesity and Type 2 Diabetes

Background Candidate genes of psychological importance include 5HT2A, 5HT2C, and COMT, implicated in the serotonin, noradrenaline and dopamine pathways, which also may be involved in regulation of energy balance. We investigated the associations of single nucleotide polymorphisms (SNPs) of these genes with obesity and metabolic traits. Methodology/Principal Findings In a population of 166 200 young men examined at the draft boards, obese men (n = 726, BMI≥31.0 kg/m2) and a randomly selected group (n = 831) were re-examined at two surveys at mean ages 46 and 49 years (S-46, S-49). Anthropometric, physiological and biochemical measures were available. Logistic regression analyses were used to assess age-adjusted odds ratios. No significant associations were observed of 5HT2A rs6311, 5HT2C rs3813929 and COMT rs4680 with obesity, except that COMT rs4680 GG-genotype was associated with fat-BMI (OR = 1.08, CI = 1.01–1.16). The SNPs were associated with a number of physiological variables; most importantly 5HT2C rs3813929 T-allele was associated with glucose (OR = 4.56, CI = 1.13–18.4) and acute insulin response (OR = 0.65, CI = 0.44–0.94) in S-49. COMT rs4680 GG-genotype was associated with glucose (OR = 1.04, CI = 1.00–1.09). Except for an association between 5HT2A rs6311 and total-cholesterol at both surveys, significant in S-46 (OR = 2.66, CI = 1.11–6.40), no significant associations were observed for the other phenotypes. Significant associations were obtained when combined genotype of 5HT2C rs3813929 and COMT rs4680 were examined in relation to BMI (OR = 1.12, CI = 1.03–1.21), fat-BMI (OR = 1.22, CI = 1.08–1.38), waist (OR = 1.13, CI = 1.04–1.22), and cholesterol (OR = 5.60, CI = 0.99–31.4). Analyses of impaired glucose tolerance (IGT) and type 2 diabetes (T2D) revealed, a 12.3% increased frequency of 5HT2C rs3813929 T-allele and an 11.6% increased frequency of COMT rs4680 GG-genotype in individuals with IGT or T2D (χ2, p = 0.05 and p = 0.06, respectively). Examination of the combined genotypes of 5HT2C and COMT showed a 34.0% increased frequency of IGT or T2D (χ2, p = 0.01). Conclusions The findings lend further support to the involvement of serotonin, noradrenaline and dopamine pathways on obesity and glucose homeostasis, in particular when combined genotype associations are explored.
Simple Substrates for Complex Cognition

Complex cognitive tasks present a range of computational and algorithmic challenges for neural accounts of both learning and inference. In particular, it is extremely hard to solve them using the sort of simple policies that have been extensively studied as solutions to elementary Markov decision problems. There has thus been recent interest in architectures for the instantiation and even learning of policies that are formally more complicated than these, involving operations such as gated working memory. However, the focus of these ideas and methods has largely been on what might best be considered as automatized, routine or, in the sense of animal conditioning, habitual, performance. Thus, they have yet to provide a route towards understanding the workings of rule-based control, which is critical for cognitively sophisticated competence. Here, we review a recent suggestion for a uniform architecture for habitual and rule-based execution, discuss some of the habitual mechanisms that underpin the use of rules, and consider a statistical relationship between rules and habits.
Speeding Up Graph Regularized Sparse Coding by Dual Gradient Ascent

Graph regularized Sparse Coding (GSC) considers data relationships during Sparse Coding (SC) and thus has better performance in certain image analysis tasks. However, it is very time consuming. This letter aims at speeding up GSC. The alternating optimization framework for GSC involves repeatedly solving a variant of <formula formulatype="inline"><tex Notation="TeX">${\ell _1}$</tex></formula> minimization referred to as GSRsub in this letter. Traditional ways to deal with GSRsub are to generalize optimization strategies for <formula formulatype="inline"> <tex Notation="TeX">${\ell _1}$</tex></formula> minimization to solve its primal problem that is strongly convex but non-differentiable, thus converging slowly. We propose that GSC can be accelerated by solving a new dual problem of GSRsub called D-GSRsub. Compared with the primal form and the existing dual form of GSRsub, D-GSRsub has a strongly convex and smooth objective function with less variables. Based on these properties, four dual gradient ascent strategies with lower computational complexities are developed. Experimental results on real-world datasets demonstrate that these strategies can dramatically and stably speed up GSC without affecting its performance in the corresponding image analysis tasks.
Emerging microscopy techniques for pediatric vocal fold evaluation

Understanding vocal fold (VF) structure and development is primordial in developing new methods to prevent and treat pathologies. Optical microscopy techniques have the potential to impact the field of pediatric laryngology through their ability to non-invasively image sub-surface structures with high resolution and contrast. In this chapter, we describe and discuss three optical imaging modalities for applications to clinical laryngology: optical coherence tomography (OCT), reflectance confocal microscopy (RCM), and nonlinear microscopy (NLM).
Software Inspection Using CodeSurfer

Software Inspection is a technique for detecting flaws in software before deployment. It was introduced by Fagan in 1976 [12], and since then its use has spread widely. Despite the widespread adoption and success of software inspection, many software products continue to be released with large numbers of flaws. This can partly be attributed to the inherent complexity of software systems. The complexity of the software thwarts manual attempts to comprehend it. Furthermore, the ideal situation for conducting software inspections in the field may often not be feasible. Time, geographical, or other constraints may mean that the original author of the code is not available to explain the structure of the code or describe its intended properties. Documentation may be misleading or even missing. General-purpose program understanding tools are crucial if code is to be inspected efficiently. However such tools until now have mostly operated on the surface-level syntactic features of the code. Yet another difficulty is raised by the fact that safety or secuiry requirements of software may be extremely difficult to show using manual techniques. For example, regulatory authorities that specify standards for safety-critical programs such as the Federal Aviation Authority (FAA) or the Nuclear Regulatory Commision (NRC) sometimes require that programs involved in the control of components have specific properties such as “part A must be independent of part B”. It is a difficult and error-prone process for a human to determine whether these properties hold for a program. We believe that tools that allow reasoning about the deep structure of the code at a high level of detail will be extremely useful for doing software inspections. In this paper we describe how our own system—CodeSurfer—provides access to and queries on the system-dependence graph representation of a program for the purposes of helping with software inspections. The remainder of the paper is structured as follows. Section II presents some basic material on dependence graphs. Section III describes CodeSurfer—our system for program understanding. Section IV describes how queries on the system dependence dependence graph can be used for software inspection. Section V describes using model checking
An experimental model of idiopathic pneumonia syndrome after bone marrow transplantation: I. The roles of minor H antigens and endotoxin.

Idiopathic pneumonia syndrome (IPS) refers to diffuse, non-infectious pneumonia that occurs after allogeneic bone marrow transplantation (BMT). We have developed a model of IPS using a well-characterized murine BMT system (B10.BR-->CBA) in which lung injury after BMT can be induced by minor histocompatibility (H) antigenic differences between donor and host. Lung pathology and broncho-alveolar lavage (BAL) fluid were analyzed in transplant recipients before and after both syngeneic and allogeneic BMT. At 2 weeks after BMT, no specific pathologic abnormalities were noted; at 6 weeks, both pneumonitis and mononuclear cell infiltration around vessels and bronchioles were observed only in mice receiving allogeneic BMT. This injury was associated with elevated BAL fluid levels of endotoxin (lipopolysaccharide [LPS]), neutrophils, and tumor necrosis factor alpha. No pathologic organisms were isolated from the respiratory tract of any animal. We also tested the role of endotoxin in the development of this injury. Injection of LPS 6 weeks after transplantation caused profound lung injury only in mice with moderate graft-versus-host disease; dramatic increases in BAL neutrophils and tumor necrosis factor alpha were observed, with alveolar hemorrhage occurring in 4 of 12 of these mice but in no other group. We conclude that (1) this murine BMT system is a potentially useful model of clinical IPS; (2) minor H differences between donor and recipient can be important stimuli in the pathogenesis of IPS; and (3) endotoxin in BAL fluid is associated with lung injury, and excess endotoxin can cause the development of alveolar hemorrhage in this model.
Comparative analysis of cascade fuzzy logic controllers for boiler water level control

Water level control in boilers is an age old control problem that has challenged the frontiers of control engineering. More so the control problem offered by a marine boiler is even more intriguing. In this paper, a comparative study of cascade fuzzy logic controller for boiler water level control has been carried out between its different configurations. Thereafter a comparative study has been made to evaluate the merits of each controller. To evaluate performance of each controller, various performance parameters such as maximum overshoot, undershoot and settling time have been determined through MATLAB. After the comparative analysis of the controllers, it was concluded that the Interval Type-2 fuzzy logic cascade controller (IT2FLC) fares better than the Type-1 fuzzy logic cascade controller (T1FLC) and Mixed cascade controller.
Exploiting Domain structure to Achieve Efficient Temporal Reasoning

We take temporal reasoning to be the problem of ma in ta i n i ng a set of constraints between t i m e po in ts a n d / o r intervals, and responding to queries about the tempora l separat i on between those ind iv idua ls . Forma l invest i gat ions of th is constra int -sat is fact ion p rob lem have demonst ra ted tradeoffs between the expressive power of the constra int language and the t i m e required to answer queries. A s imple constra int language admi ts an a lgo r i t hm cubic in the number o f ind iv idua ls ; a l lowing unrest r ic ted d is junct ive constraints makes the a l g o r i t h m exponent ia l . The prob lem is tha t app l icat ions of t empora l reasoning, e.g. p lan proj ec t i on , need bo th d is junct ive constraints and an a l go r i t hm much faster than 0 ( n 3 ) . I t is s igni f icant , however, t ha t the nature of the constra ints added by and the queries posed by an app l ica t ion tend to be s t ruc tured and pred ic table. Our so lu t ion to the p rob lem is to exp lo i t the s t ruc ture o f the app l ica t ion doma in to prov ide fast responses to typ ica l queries. We consider the p rob lem of p lan project ion under uncer ta in ty and bu i ld a tempora l representat ion—hierarchical in terval const ra ints ( H I C ) — t h a t al lows appropr ia te disj unc t i ve constra ints. We then implement the H I C representat ion in a temporal reasoning modu le , and test i t using a p lan-pro jec t ion app l i ca t ion . A p p l y i n g the H I C modu le to a s imple t empora l p ro jec t ion p rob lem shows orders-of -magni tude improvement over runn ing the same pro jec tor using current imp lementa t ions o f domainindependent tempora l cons t ra in t propagators.
Apoptosis and Regression of Embryonic Mesenchyme in the Development of the Middle Ear Spaces

Apoptosis was studied using temporal bones from three fetuses representing different times of gestation and from three neonates. Paraffin-embedded sections 20-μm thick were studied using the terminal deoxynucleotide transferase-mediated dUTP nick-end labeling method based on 3′-end-labeling of fragmented DNA. Phenotyping of the immune cells was performed using regular monoclonal antibodies. In the bone marrow the granulocyte series dominated and the number of cells in the macrophage series was noticeably fewer, with apoptotic cells occurring in both. In the embryonic mesenchyme, solitary apoptotic cells occurred in all locations in both the fetuses and neonates. Apoptosis is a basic factor in the regression of embryonal mesenchyme, but may not be preprogrammed. Basic scientific data obtained from modified tissue cultures show that mechanical forces cause cells to switch between different genetic programs. It is suggested that the act of swallowing causes periodic changes in the amniotic fluid pressure and provides the necessary force for regression of the mesenchyme by apoptosis.
Enabling energy-efficient and quality localization services

Localization research has focused on improving the accuracy of pinpointing the physical location of a target. We think that the energy efficiency and the quality of the localization services (QoLS) are equally important properties of localization systems. We refer to the QoLS as the timely supply of the location information to the applications. Energy efficiency and quality are seemingly two contradictive goals in terms of determining the rate of triggering the localization systems to perform the necessary computation and communication. In that, a lower location sampling rate contributes to a lower level of energy consumption but in the meantime compromises the timeliness of acquiring the location information. Opting for energy efficient and quality localization services, we propose a mobility-aware mechanism that adapts the sampling rate to the target mobility. Results from our simulations confirm that the adaptive sampling approach is promising and effective
An Efficient Optimization Based Method to Evaluate the DRV of SRAM Cells

To reduce the substantial leakage current, the supply voltage of SRAM cells has being scaled down towards its lower limit, which is called the data Retention Voltage (DRV). Although the power consumption is largely reduced, this down-scaling trend, however, impacts the stability of the SRAM cell due to the unpredictable process or device parameter variations. In this work, we propose a novel method to evaluate the DRV of SRAM cells at the presence of variations. The DRV issue is first formulated as a time domain worst performance bound problem. To accurately and efficiently evaluate the DRV, a multi-start point (MSP) optimization strategy is then studied and developed with the use of practical circuit simulator. One feature of the proposed method is that it can efficiently evaluate the DRV without suffering from any process/model accuracy. Experiment results show that it achieves a speedup of 3 and 5-7 order over the Importance Sampling (IS) and Monte Carlo (MC) method respectively under the context of the DRV evaluation in this paper. The proposed method can serve as an efficient DRV evaluation tool on any specific technology process or in-house circuit simulator. In this work, the DRVs at the technology node from 130 nm to 45 nm under the influence of different variation sources are also presented and analyzed.
Dealbreaker: A Nonlinear Latent Variable Model for Educational Data

Statistical models of student responses on assessment questions, such as those in homeworks and exams, enable educators and computer-based personalized learning systems to gain insights into students' knowledge using machine learning. Popular student-response models, including the Rasch model and item response theory models, represent the probability of a student answering a question correctly using an affine function of latent factors. While such models can accurately predict student responses, their ability to interpret the underlying knowledge structure (which is certainly nonlinear) is limited. In response, we develop a new, nonlinear latent variable model that we call the dealbreaker model, in which a student's success probability is determined by their weakest concept mastery. We develop efficient parameter inference algorithms for this model using novel methods for nonconvex optimization. We show that the deal-breaker model achieves comparable or better prediction performance as compared to affine models with real-world educational datasets. We further demonstrate that the parameters learned by the dealbreaker model are interpretable--they provide key insights into which concepts are critical (i.e., the "dealbreaker") to answering a question correctly. We conclude by reporting preliminary results for a movie-rating dataset, which illustrate the broader applicability of the dealbreaker model.
prospective study on alveolar bone augmentation and dental implant success in patients with type 2 diabetes

Objectives: The objective of this prospective, controlled clinical study was to determine the outcomes of dental implant therapy with staged guided bone regeneration procedures in patients with type 2 diabetes. Patients and methods: Twenty-four patients were included in the study. Half of the patients were diagnosed with type 2 diabetes mellitus (group 1) while the other half (group 2) of the patients consisted of patients without diabetes. The edentulous maxillary anterior/premolar regions with sufficient vertical height but inadequate horizontal width were treated with staged guided bone regeneration technique and with one or two implant-supported fixed restorations. The patients were followed up at least for 12 months. The parameters that were evaluated were radiographic evaluations on CBCT images and periapical radiographs, histomorphometric analysis, resonance frequency analysis (RFA) and wound-healing parameters. The data were analyzed statistically. Results: A total of 43 implants were placed in 24 patients (22 implants in group 1 and 21 implants in group 2). The survival rates of implants were 100% for both groups. The success rate of implants was 95% for group 1 and 100% for group 2. None of the parameters including CBCT findings, RFA values, success rates and wound-healing scores showed a significant difference between the two