Miracle-dz/UniFAR-FTUs huggingface dataset:
SimHPN : a MATLAB toolbox for simulation , analysis and design with hybrid Petri nets-draft -

This paper presents a MATLAB embedded package for hybrid Petri nets called SimHPN . It offers a collection of tools devoted to simulation, analysis and synthesis of dynamical systems modeled by hybrid Petri nets. The package supports several server semantics for the firing of both, discrete and continuous, types of transitions. Besides providing different simulation options, SimHPN offers the possibility of computing steady state throughput bounds for continuous nets. For such a class of nets, optimal control and observability algorithms are also implemented. The package is fully integrated in MATLAB what allows the creation of powerful algebraic, statistical and graphical instruments that exploit the routines available in MATLAB. Published as: J. Julvez, C. Mahulea, and C.R. Vazquez, “SimHPN: a MATLAB toolbox for simulation, analysis and design with hybrid Petri nets,” Nonlinear Analysis: Hybrid Systems, vol. 6, no. 2, pp. 806-817, May 2012. DOI: http://doi.org/ 10.1016/j.nahs. 2011.10. 001 ∗The authors are with The Aragón Institute for Engineering Research (I3A), University of Zaragoza, Maria de Luna 1, 50018 Zaragoza, Spain. E-mail: {julvez,cmahulea,cvazquez}@unizar.es. This work has been partially supported by the European Community’s Seventh Framework Programme under project DISC (Grant Agreement n. INFSO-ICT-224498), by CICYT FEDER grants DPI2010-20413 and TIN2007-66523 and by Fundación Aragón I+D.
Infinite attention: NNGP and NTK for deep attention networks

The hyperparameter search was on a fixed architecture with 8x Convolution + ReLU, Attention, Flatten, and a Dense readout layer. We used 1.7562 and 0.1841 respectively for the weight and bias variances as in (Novak et al., 2019, appendix G.1) except for the attention output variance σ O which was set to one. The convolutional layers were used with the SAME padding, stride one, and filter size 3× 3. For attention kernels with positional encodings, the reported ρ parameter (Equation (14)) is actually ρ/(σ Qσ 2 K) so that the relative scale of the contribution of R remains the same with changing σ 2 Qσ 2 K .
A Theory of Scientific Change

At any moment of history, there are theories that the scientific community of the time accepts as the best available descriptions of reality. In addition, there are methods which the scientific community of the time employs in theory assessment. This set of all accepted theories and all employed methods constitute the scientific mosaic of the time. The mosaic is in a process of constant change – both accepted theories and employed methods change through time. The main question of this dissertation is: are there any laws governing the process of transitions from one accepted theory to another and from one employed method to another? I.e. can there be a general theory of scientific change? Contrary to the widespread opinion, I argue that scientific change is a law-governed process and that there can be a general descriptive theory of scientific change (TSC). In the first part of the dissertation, I clarify the scope of TSC by defining it as a descriptive theory that reveals the actual mechanism of transitions in the mosaic, regardless of its scale, time period or field of inquiry. Then, I scrutinize and reject a number of traditional arguments against the possibility of TSC. In the second part, I construct an actual theory of scientific change. First, I postulate four axioms – four laws of scientific change. From these laws, I deduce a number of theorems concerning different aspects of scientific change. All axioms and theorems are illustrated by examples from the actual history of science. By proposing this TSC, I oppose the common wisdom of the so-called disunity of science thesis. I argue against the particularist approach which holds that the history of science can only be studied in a piecemeal fashion. On the contrary, I show that a general descriptive theory of scientific change can and does exist. %%%%PhD
1st Place Solution for the UVO Challenge on Video-based Open-World Segmentation 2021

In this report, we introduce our (pretty straightforard) two-step “detect-then-match” video instance segmentation method. The ﬁrst step performs instance segmentation for each frame to get a large number of instance mask proposals. The second step is to do inter-frame instance mask matching with the help of optical ﬂow. We demonstrate that with high quality mask proposals, a simple matching mechanism is good enough for tracking. Our approach achieves the ﬁrst place in the UVO 2021 Video-based Open-World Segmentation Challenge.
The impact of poor assembly ergonomics on product quality: A cost–benefit analysis in car manufacturing

The study aimed at analyzing the relationship between assembly ergonomics, assemblability (“ease of assembly”), and product quality and at quantifying these relationships in economic terms. This was in order to better to support the development of more ergonomic product and assembly solutions, particularly at early stages of the car development process. The assembly of 24,443 cars was studied for 8 weeks in an assembly plant and for another 16 weeks as factory‐complete vehicles. The results show increased risks for quality errors of 3.0 and 3.7 times and total action costs that were 8.7 times and 8.2 times higher for high and medium physical load assemblies compared to low physical load assemblies for 55 tasks assessed. © 2009 Wiley Periodicals, Inc.
When the Majority is Wrong: Leveraging Annotator Disagreement for Subjective Tasks

Though majority vote among annotators is typically used for ground truth labels in natural language processing, annotator disagreement in tasks such as hate speech detection may re-ﬂect differences among group opinions, not noise. Thus, a crucial problem in hate speech detection is whether a statement is offensive to the demographic group that it targets, which may constitute a small fraction of the annotator pool. We construct a model that predicts individual annotator ratings on potentially offensive text and combines this information with the predicted target group of the text to model the opinions of target group members. We show gains across a range of metrics, including raising performance over the baseline by 22% at predicting individual annotators’ ratings and 33% at predicting variance among an-notators, which provides a method of measuring model uncertainty downstream. We ﬁnd that annotator ratings can be predicted using their demographic information and opinions on online content, without the need to track identifying annotator IDs that link each anno-tator to their ratings. We also ﬁnd that use of non-invasive survey questions on annotators’ online experiences helps to maximize privacy and minimize unnecessary collection of demographic information while predicting annota-tors’ opinions.
PREPARATION AND CHARACTERIZATION OF METHOCEL K4M, EUDRAGIT RLPO AND PEG 4000 LOADED CARBAMAZEPINE MICROSPHERES.

factorial design was made by central composite design where the amount of Methocel K4M and PEG 4000 were selected as independent variables. Thirteen formulations were prepared with MDT, T50%, T80%, and swelling index as dependent variables. Data were analyzed statistically using linear regression model by IBM SPSS version 22. Effect of varying amount of Methocel K4M and PEG 4000 on the drug content, entrapment efficiency, swelling index, surface morphology (SEM), and in-vitro drug release rate were evaluated. Formulations with high amount of Methocel K4M showed good swelling properties. In SEM studies microspheres appeared rough and spherical in shape. Increasing amount of PEG produced pores on the surface of the microspheres due to the diffusion of polymers towards the external phase thus showing better drug release (B1, B3 & B8). Further clarification on release mechanism from spherical particles were studied using the Kopcha kinetics, Weibull model and Baker-Lonsdale model. In Kopcha kinetics all the formulations had ratio of A/B greater than 1 where release was primarily controlled by a Fickian diffusion. Weibull Model has parameters that are more sensitive to release kinetic data, the value of β was <1 and Td value was low showing enhance drug release. In Baker-Lonsdale model most spherical matrix had uniformly distributed drugs with consistent drug release by diffusion mechanism which was seen as linear graphs.
WHOMoVeS: An optimized broadband sensor network for military vehicle tracking

With the advance of sensing technologies and their applications, advanced sensor networks are gaining increasing interest. For certain sensitive applications, heterogeneous sensors can be deployed in the monitored space to ensure scalability, high‐speed communication, and long network lifetime. Hybrid sensor networks have capabilities to combine the use of both resource‐rich and resource‐impoverished sensor nodes. This paper proposes a heterogeneous broadband sensor network architecture for military vehicle tracking. Powerful sensor devices with good bandwidth and energy capabilities are used as a communication backbone while energy sensors are used to track moving targets. Copyright © 2007 John Wiley & Sons, Ltd.
Randomized controlled multicentre study of the prevention of complications by octreotide in patients undergoing surgery for chronic pancreatitis

A randomized double‐blind placebo‐controlled multicentre trial was carried out in 247 patients undergoing major elective surgery for chronic pancreatitis to clarify whether the perioperative application of octreotide prevents postoperative complications. Eleven complications were defined, including death, anastomotic leakage, pancreatic fistula, abscess, fluid collection, shock, sepsis, bleeding, pulmonary insufficiency, renal insufficiency and postoperative pancreatitis. A total of 124 patients underwent pancreatic head resection, 55 left resection, 61 pancreaticojejunostomy and seven had other procedures. The overall mortality rate was 1.2 per cent (octreotide group 0.8 per cent, placebo group 0.4 per cent (P not significant)). The postoperative complication rate in the octreotide group was 16.4 per cent (20 of 122 patients) and in the placebo group 29.6 per cent (37 of 125) (P < 0.007). The perioperative application of octreotide substantially reduces the risk of postoperative complications in patients undergoing major pancreatic surgery for chronic pancreatitis.
Primary cutaneous T-cell lymphoma: review and current concepts.

PURPOSE Primary cutaneous T-cell lymphomas (CTCLs) encompass a wide variety of lymphomas that are characterized by the localization of the malignant lymphocytes to the skin at presentation. Advances in molecular biologic techniques, including immunophenotyping and gene rearrangement studies to determine clonality, have led to more frequent diagnosis of CTCL as well as more consistent subclassification of these entities. However, there continues to be confusion in the classification, prognosis, and management of patients with CTCL. The purpose of this review is to present a summary of the diagnosis, prognosis, and treatment of CTCL, with specific emphasis on mycosis fungoides (MF) and Sézary syndrome (SS). We also present a detailed discussion of the entities that make up the differential diagnosis of CTCL. DESIGN We reviewed the medical literature on CTCL and other diseases that make up the differential diagnosis of CTCL. RESULTS AND CONCLUSION MF and SS are the most common forms of CTCL. The etiology of this disease is still unknown. Patients may go for months to years with skin abnormalities before being diagnosed. MF/SS is an indolent disease and patients with T1 disease have a normal life expectancy. Patients who undergo transformation to large-cell lymphoma (8% to 23% of patients) have a poor prognosis, with mean survival ranging from 2 to 19 months. Treatment for MF/SS continues to be palliative. There are many new therapies that are currently being investigated in clinical trials, and the DAB(389)IL-2 fusion protein was recently approved for the treatment of refractory MF/SS.
Learning Dynamical Systems from Data: A Simple Cross-Validation Perspective, Part V: Sparse Kernel Flows for 132 Chaotic Dynamical Systems

Regressing the vector field of a dynamical system from a finite number of observed states is a natural way to learn surrogate models for such systems. A simple and interpretable way to learn a dynamical system from data is to interpolate its vector-field with a data-adapted kernel which can be learned by using Kernel Flows. The method of Kernel Flows is a trainable machine learning method that learns the optimal parameters of a kernel based on the premise that a kernel is good if there is no significant loss in accuracy if half of the data is used. The objective function could be a short-term prediction or some other objective for other variants of Kernel Flows). However, this method is limited by the choice of the base kernel. In this paper, we introduce the method of \emph{Sparse Kernel Flows } in order to learn the ``best'' kernel by starting from a large dictionary of kernels. It is based on sparsifying a kernel that is a linear combination of elemental kernels. We apply this approach to a library of 132 chaotic systems.
TREATMENT OF ACUTE GRAFT‐VERSUS‐HOST DISEASE WITH MONOCLONAL ANTIBODY OKT3

Eight recipients of a bone-marrow graft from HLA-identical, MLR-nonreactive sibling donors who had developed grade II—IV acute graft-versus-host disease (aGVHD), were given 14 consecutive daily injections of 5 mg of a murine anti-T-cell monoclonal antibody (MCA) called OKT3. Four patients with grade II aGVHD showed a complete response; two patients with grade II had a partial response, and two patients (one with grade II and one with grade IV) showed no improvement at all. The main side effect was a high spiking fever after the first injection. T cells were monitored with monoclonal antibodies, indirect immunofluorescence, and flow cytometry. Circulating T3+ T cells dropped to virtually zero within 1 hr following the first injection. Low numbers of E-rosetting cells were still demonstrable during OKT3 therapy. During the second week of treatment, T-cell markers (T3, T4, T8) started to increase again, in spite of excess antibody in the circulation. At that time, T cells showed weaker fluorescence with OKT3 than before OKT3 therapy, suggesting modulation of the T3 antigen. After cessation of OKT3 therapy, T cells reached pretreatment levels within one week. None of the six patients studied developed anti-mouse-Ig antibodies. These results suggest that OKT3 therapy is effective in limited aGVHD. The absence of anti-mouse-Ig antibody formation may allow repeated courses of MCA that may add to their therapeutical potential.
Therapy of Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is the most common type of acute leukemia in adults1. Over the past twenty years, the studies on the pathogenesis and prognosis of AML have made considerable progress. Clinically, patients with AML typically present with signs or symptoms of bone marrow failure, although sometimes they can present with symptoms of leukostasis with pulmonary or neurological dysfunction. Rarely, patients will present with primary extramedullary disease, which should be approached in the same way as systemic AML. A certain number of factors can be involved in the etiology of AML: as an example, exposure to ionizing radiation and long-term exposure to benzene are known risk factors. AML could be part of the natural history of patients with congenital disorders of DNA repair, such as the Fanconi's anemias; also the myeloproliferative disorders (MPD) and myelodysplastic syndromes (MDS). AML is a heterogeneous disease; standard treatments may be applied to biologically distinct subgroups, resulting in different treatment outcomes. However, less than one-third of all adult patients with AML can be cured even to this date. The treatment of refractory, relapsed and elderly AML remains a major challenge. In recent years, new regimens and novel agents are being studied in an effort to improve complete remission (CR) rate and overall survival. The concept of risk-adapted therapy allows for recognition of this biologic diversity by incorporating key biologic features, such as cytogenetic and molecular markers, when formulating treatment regimens and investigating emerging targeted therapies based on disease characteristics. Although AML has been the focus of significant laboratory and clinical investigation, it remains difficult to treat, perhaps partly because of the fundamental nature of the disorder, which requires substantial institutional resources to adequately deal with the complications of bone marrow failure and sustain patients through periods of intensive therapy. Several large studies have helped categorize chromosomal abnormalities into good-, intermediate-, and poor-risk groups2-5. This hierarchical system of karyotype classification is predictive value across different age groups in de novo and secondary AML. It was also found to retain prognostic significance across the different treatment modalities of chemotherapy and autologous and allogeneic bone marrow transplantation. Generally, the poor-risk or unfavorable group includes those with complex karyotypes (> 3-5 abnormalities), chromosome 5 or 7 abnormalities, or chromosome 3q abnormalities. The results for these patients are dismal, with standard chemotherapy causing some to advocate
Rapid Publication

Parathyroid hormone-like adenylate cyclase-stimulating proteins (hACSPs) have been implicated as one of the calcemic, bone-resorbing agents in patients with humoral hypercalcemia of malignancy. We report the synthesis of an amino-terminal hACSP fragment, Tyr' hACSP (1-36) amide. The synthetic hACSP is a potent agonist of renal membrane adenylate cyclase (K., 1.7 X 10-"') and of bone cell adenylate cyclase (K. 1 X 10-9 M). It is a potent bone-resorbing agent in vitro, stimulating 45Ca release from fetal rat long bones at a concentration of 10-' M. When infused via osmotic minipumps into rats, it is also a potent calcemic factor in vivo, inducing a rise in serum calcium from (mean ± SD) 10.6 ± 0.6 to 19.7 ± 3.2 mg/dl when infused at 1.4 pg/h and from 9.9 ± 0.7 to 11.4 ± 1.2 mg/dl when infused at 0.14 ,ug/h. These findings indicate that biologically active hACSP fragments can be synthesized. One such synthetic peptide possesses the in vitro and in vivo bioactivities demonstrated in native, tumor-derived hACSPs. It is also a potent calcemic, bone-resorbing agent.
DENSITY-MAGNETIC FIELD CORRELATION IN MAGNETOHYDRODYNAMIC TURBULENCE DRIVEN BY DIFFERENT DRIVING SCHEMES WITH DIFFERENT CORRELATION TIMES

Turbulent motions naturally produce density and magnetic-field fluctuations. Correlation between the two fluctuations is important for interpretation of observations, such as observations of the rotation measure (RM). In this paper, we study the effect of driving schemes on the density-magnetic-field correlation. In particular, we numerically investigate how the correlation time of driving affects the correlation between density and magnetic field. We perform compressible magnetohydrodynamic turbulence simulations at different sonic Mach numbers (Ms), using two different driving schemes—a finite-correlated driving and a delta-correlated driving. In the former, the forcing vectors change continuously with a correlation time comparable to the large-eddy turnover time. In the latter, the direction (and amplitude) of driving changes in a very short timescale. The finite-correlated driving results in strong anti-correlation between two fields when the sonic and the Alfvénic Mach numbers are similar to unity (i.e., when Ms ∼ 1 and MA ∼ 1, respectively). However, the anti-correlation becomes weaker and approaches zero for higher values of Ms or MA. The delta-correlated driving produces virtually no correlation between two fields when Ms ∼ 1 and MA ∼ 1, and produces more and more positive correlations as Ms or MA increases. We conjecture that two competing effects, tendency for achieving balance between the gas and the magnetic pressure and simultaneous compression of fluid and magnetic field, determine the correlation behavior. We also investigate how different driving schemes affect the Probability Density Function of three-dimensional density, dispersion measure, and RM.
Electron-acoustic solitary waves in the Earth's inner magnetosphere

The broadband electrostatic turbulence observed in the inner magnetosphere is produced by large-amplitude electrostatic solitary waves of generally two types. The solitary waves with symmetric bipolar parallel (magnetic field-aligned) electric field are electron phase space holes. The solitary waves with highly asymmetric bipolar parallel electric field have been recently shown to correspond to the electron-acoustic plasma mode (existing due to two-temperature electron population). Through theoretical and numerical analysis of hydrodynamic and modified Korteweg-de Vries equations, we demonstrate that the asymmetric solitary waves appear due to the steepening of initially quasi-monochromatic electron-acoustic perturbation arrested at some moment by collisionless dissipation (Landau damping). The typical steepening time is found to be from a few to tens of milliseconds. The steepening of the electron-acoustic waves has not been reproduced in self-consistent kinetic simulations yet, and factors controlling t.
Distributed Energy Trading and Scheduling Among Microgrids via Multiagent Reinforcement Learning

Renewable energy technologies empower microgrids to generate electricity to supply themselves and trade with others. Under this paradigm, microgrids have become autonomous entities that must intelligently determine their policies for energy trading and scheduling. Many factors influence a microgrid’s decision-making, such as the complex microgrid infrastructure, the uncertain energy yield and demand, and the competition among the energy market players. These factors are usually hard to precisely model, and deriving the optimal policy for a microgrid is challenging. We propose a multiagent reinforcement learning (MARL) approach with an attention mechanism to learn the optimal policies for the microgrids without complex system modeling. We model each microgrid as an autonomous agent, which learns how to schedule energy resources and trade with others by collaborating with other agents. We adopt attention mechanism to enable intelligently selecting contextual information for the training of each agent. After training, an agent can make control decisions using only its local information, which can well preserve the microgrids’ privacy and reduce the communication overhead among microgrids to facilitate distributed control. We implement a simulation environment and evaluate the performances of our proposed method using real-world datasets. The experimental results show that our method can significantly reduce the cost of the microgrids compared with the baseline methods.
Hepatitis C virus infection and genotypes in blood donors

Genotyping of the hepatitis C virus (HCV) is important for designing therapeutic strategies and regional specific diagnostic assays. The aim of this study was to identify the HCV genotypes in HCV infected blood donors. This is the first report on HCV genotypes in blood donors in Iran. In this cross-sectional study, 103 blood donors with hepatitis C were investigated for HCV genotypes. HCV genotyping was carried out using type-specific primers from the core region of the viral genome. From 103 blood donors, only96 cases had genotypes which could be typed. The highest frequency genotype 1a, with 53 (51.5%) of subjects. Genotype 3a and 1b were the other frequent genotypes with 39 (37.9 %) and 4 (3.9%) subjects, respec-tively. These results indicate that the dominant HCV genotypes among blood donors were 1a, 3a and 1b respectively. It was also noticed that more of the blood donors infected with genotypes 1a and 3a had history of intravenous drug abuse and