Miracle-dz/UniFAR-FTUs huggingface dataset:
SYMORO+: A system for the symbolic modelling of robots

This paper presents the software package SYMORO+ for the automatic symbolic modelling of robots. This package permits to generate the direct geometric model, the inverse geometric model, the direct kinematic model, the inverse kinematic model, the dynamic model, and the inertial parameters identification models. The structure of the robots can be serial, tree structure or containing closed loops. The package runs on Sun stations and PC computers, it has been developed under MATHEMATICA and C language. In this paper we give an overview of the algorithms used in the different models; the computational cost of the dynamic models of the PUMA robot are given.
A dual mode microbubble pressure and flow sensor

We present the first microbubble (μB) based dual mode sensor for static pressure and liquid flow. The transducer harnesses the inverse pressure-volume relation and high precision electrochemical impedance measurement (2% RSD) to monitor size of electrolytically generated μBs. By altering sensor orientation, static pressure measurement (0-20 mmHg) or flow rate was transduced (0-1000 μL/min). The design targets chronic in vivo monitoring applications and therefore incorporates the following features: non-hermetic packaging, absence of moving parts, low power consumption (<;5 μW), and biocompatible construction (Parylene and Pt).
Generalization Error and Algorithmic Convergence of Median Boosting

We have recently proposed an extension of ADABOOST to regression that uses the median of the base regressors as the final regressor. In this paper we extend theoretical results obtained for ADABOOST to median boosting and to its localized variant. First, we extend recent results on efficient margin maximizing to show that the algorithm can converge to the maximum achievable margin within a preset precision in a finite number of steps. Then we provide confidence-interval-type bounds on the generalization error.
Effects of Single Amino Acid Substitution on the Biophysical Properties and Biological Activities of an Amphipathic α-Helical Antibacterial Peptide Against Gram-Negative Bacteria

An antimicrobial peptide, known as V13K, was utilized as the framework to study the effects of charge, hydrophobicity and helicity on the biophysical properties and biological activities of α-helical peptides. Six amino acids (Lys, Glu, Gly, Ser, Ala, and Leu) were individually used to substitute the original hydrophobic valine at the selected sixteenth location on the non-polar face of V13K. The results showed that the single amino acid substitutions changed the hydrophobicity of peptide analogs as monitored by RP-HPLC, but did not cause significant changes on peptide secondary structures both in a benign buffer and in a hydrophobic environment. The biological activities of the analogs exhibited a hydrophobicity-dependent behavior. The mechanism of peptide interaction with the outer membrane and cytoplasmic membrane of Gram-negative bacteria was investigated. We demonstrated that this single amino acid substitution method has valuable potential for the rational design of antimicrobial peptides with enhanced activities.
Active Learning with Distributional Estimates

Active Learning (AL) is increasingly important in a broad range of applications. Two main AL principles to obtain accurate classification with few labeled data are refinement of the current decision boundary and exploration of poorly sampled regions. In this paper we derive a novel AL scheme that balances these two principles in a natural way. In contrast to many AL strategies, which are based on an estimated class conditional probability ^p(y|x), a key component of our approach is to view this quantity as a random variable, hence explicitly considering the uncertainty in its estimated value. Our main contribution is a novel mathematical framework for uncertainty-based AL, and a corresponding AL scheme, where the uncertainty in ^p(y|x) is modeled by a second-order distribution. On the practical side, we show how to approximate such second-order distributions for kernel density classification. Finally, we find that over a large number of UCI, USPS and Caltech4 datasets, our AL scheme achieves significantly better learning curves than popular AL methods such as uncertainty sampling and error reduction sampling, when all use the same kernel density classifier.
Superedge grouping for object localization by combining appearance and shape information

Both appearance and shape play important roles in object localization and object detection. In this paper, we propose a new superedge grouping method for object localization by incorporating both boundary shape and appearance information of objects. Compared with the previous edge grouping methods, the proposed method does not subdivide detected edges into short edgels before grouping. Such long, unsubdivided superedges not only facilitate the incorporation of object shape information into localization, but also increase the robustness against image noise and reduce computation. We identify and address several important problems in achieving the proposed superedge grouping, including gap filling for connecting superedges, accurate encoding of region-based information into individual edges, and the incorporation of object-shape information into object localization. In this paper, we use the bag of visual words technique to quantify the region-based appearance features of the object of interest. We find that the proposed method, by integrating both boundary and region information, can produce better localization performance than previous subwindow search and edge grouping methods on most of the 20 object categories from the VOC 2007 database. Experiments also show that the proposed method is roughly 50 times faster than the previous edge grouping method.
Design adventures in public sector

Public organizations are slowly awakening for applying service design and design thinking. There is a huge potential but there are also challenges ahead. The aim of this paper is to discuss the benefits and challenges that occurred when design researchers coming from outside the organization applied and tested co-design methods as well as visual thinking and communication tools in two case experiments that were conducted in a context of a City of Helsinki. These activities were part of internal and external development projects of the organization. The paper also contributes to the discussion around the topic of organizations' design readiness in practical level, by reflecting some conditions and capabilities that are needed from both organization's as well as design professional's side, when designerly approaches are introduced and applied for the first time.
Modelling of Bonded Post-Tensioned Concrete Cantilever Beams under Flexural Loading

Prestressing is widely used technic all over the world for constructions of buildings, bridges, towers, offshore structures etc. due to its efficiency and economy for achieving requirements of long span with small depth. It is used for flexural strengthening of reinforced concrete structures for improving cracking loads and decreasing deflections due to service loads. There are two methods for prestressing (pre-tensioning and post-tensioning). In this paper, a three-dimensional nonlinear Finite Element (FE) method is used to determine the behaviour of Post-Tensioned (PT) concrete cantilever beams with different tendon profiles. Numerical analyses ANSYS package program is used for analysis of beams. The results from FE analysis is verified by experimental reference test result and good agreement is achieved. This paper is focused on the effect of different tendon profiles on the flexural behaviour of Bonded Post Tensioned (BPT) reinforced concrete cantilever beams. Six models with different tendon profiles are investigated. These models are without tendons, two tendons at the bottom, middle, top, parabolic tendons with one draped point and two draped points. Failure loads, deflections, and load versus deflection relationships for all models are examined and it is seen that the beam with one draped tendon profile shows a highest performance.
Zinc and Calcium Ions Cooperatively Modulate ADAMTS13 Activity*

ADAMTS13 is a metalloproteinase that cleaves von Willebrand factor (VWF) multimers. The metal ion dependence of ADAMTS13 activity was examined with multimeric VWF and a fluorescent peptide substrate based on Asp1596–Arg1668 of the VWF A2 domain, FRETS-VWF73. ADAMTS13 activity in citrate-anticoagulated plasma was enhanced ∼2-fold by zinc ions, ∼3-fold by calcium ions, and ∼6-fold by both ions, suggesting cooperative activation. Cleavage of VWF by recombinant ADAMTS13 was activated up to ∼200-fold by zinc ions (KD app ∼0.5 μm), calcium ions (KD app ∼4.8 μm), and barium ions (KD app ∼1.7 mm). Barium ions stimulated ADAMTS13 activity in citrated plasma but not in citrate-free plasma. Therefore, the stimulation by barium ions of ADAMTS13 in citrated plasma appears to reflect the release of chelated calcium and zinc ions from complexes with citrate. At optimal zinc and calcium concentrations, ADAMTS13 cleaved VWF with a Km app of 3.7 ± 1.4 μg/ml (∼15 nm for VWF subunits), which is comparable with the plasma VWF concentration of 5–10 μg/ml. ADAMTS13 could cleave ∼14% of VWF pretreated with guanidine HCl, suggesting that this substrate is heterogeneous in susceptibility to proteolysis. ADAMTS13 cleaved FRETS-VWF73 with a Km app of 3.2 ± 1.1 μm, consistent with an ∼200-fold decrease in affinity compared with VWF. ADAMTS13 cleaved VWF and FRETS-VWF73 with roughly comparable catalytic efficiency of 55 μm–1 min–1 and 18 μm–1 min–1, respectively. The striking preference of ADAMTS13 for VWF suggests that substrate recognition depends on structural features or exosites on multimeric VWF that are missing from FRETS-VWF73.
Processing of metals and semiconductors by a femtosecond laser-based microfabrication system

A microfabrication system with the use of a femtosecond laser was designed for 3D processing of industrially important materials. The system includes a 120 fs, 1 kHz laser; beam delivery and focusing system, systems for automated 3D target motion and real-time imaging of the sample placed in a vacuum chamber. The first tests of the system on the processing of stainless steel and silicon are presented. We established thresholds and regimes of ablation for both materials. It was found that at relatively low laser fluences I < 3-5 J/cm2 the regime of “gentle” ablation takes place, which is characterized by exceptional quality of the ablated surface, but slow ablation rate (< 25 nm/pulse). This regime is especially efficient for the patterning of markers on steel or silicon surfaces. The “fast” ablation regime at I > 10 J/cm2 provides much higher ablation rate of 30-100 nm/pulse, giving an opportunity of fast high-quality processing of materials. This regime is well suited for drilling of through holes and fast cutting of materials. However, it was found that fast ablation regime imposes additional requirements on the quality of delivery and focusing of the laser beam because of the presence of parasitic ablation around the main spot on the tail of the radiation intensity distribution. As industrial machining examples, we demonstrate heat-affected-zone free drilling of through holes in a 50 μm thick stainless steel foil and cutting of a 50 μm thick Si wafer with a net cutting speed of 8 μm/sec.
Acoustic Emission Source Identification Based on Pattern Recognition Method

A new pattern recognition method based on harmonic wavelet packet (HWPT) and hierarchy support vector machine (H-SVM) is proposed to solve the fatigue damage identification problem of helicopter component. In this approach, HWPT is used to extract the energy feature of acoustic emission (AE) signals on different frequency bands and to reduce the dimensionality of original data features. The H-SVM classifier is used to identify the AE source type. A subset of the experimental data for known AE source type is used to train the H-SVM classifier, the remaining set of data is used to test the H-SVM classifier. Also, the pressure off experiment on specimen of carbon fiber materials is investigated. The results indicate that the proposed approach can implement AE source type identification effectively, and has better performance on computational efficiency and identification accuracy than wavelet packet (WPT) feature extraction and RBF neural network classification. DOI : http://dx.doi. org/10.11591/telkomnika.v12i5. 3587
Model predictive control for tracking random references

This paper proposes a simple model predictive control scheme for linear systems, tracking a random reference and analysis its performance. In such situations it is usual to assume that the reference eventually converges to a constant in which case convergence to zero of the tracking error can be established. In this note we characterize the set to which the tracking error converges and the associated region of convergence when the reference does not converge to a constant.
Causal Inference by Choosing Graphs with Most Plausible Markov Kernels

We propose a new inference rule for estimating causal structure that underlies the observed statistical dependencies among n random variables. Our method is based on comparing the conditional distributions of variables given their direct causes (the so-called “Markov kernels”) for all hypothetical causal directions and choosing the most plausible one. We consider those Markov kernels most plausible, which maximize the (conditional) entropies constrained by their observed first moment (expectation) and second moments (variance and covariance with its direct causes) based on their given domain. In this paper, we discuss our inference rule for causal relationships between two variables in detail, apply it to a real-world temperature data set with known causality and show that our method provides a correct result for the example.
CD28-independent, TRAF2-dependent Costimulation of Resting T Cells by 4-1BB Ligand

4-1BB ligand (4-1BBL) is a member of the tumor necrosis factor (TNF) family expressed on activated antigen-presenting cells. Its receptor, 4-1BB, is a member of the TNF receptor family expressed on activated CD4 and CD8 T cells. We have produced a soluble form of 4-1BBL using the baculovirus expression system. When coimmobilized on plastic with anti-CD3, soluble 4-1BBL induces interleukin (IL)-2 production by resting CD28+ or CD28− T cells, indicating that 4-1BBL can function independently of other cell surface molecules, including CD28, in costimulation of resting T cell activation. At low concentrations of anti-CD3, 4-1BBL is inferior to anti-CD28 in T cell activation. However, when 4-1BB ligand is provided together with strong TCR signals, then 4-1BBL and anti-CD28 are equally potent in stimulation of IL-2 production by resting T cells. We find that TNF receptor–associated factor (TRAF)1 or TRAF2 associate with a glutathione S-transferase–4-1BB cytoplasmic domain fusion protein in vitro. In T cells, we find that association of TRAF1 and TRAF2 with 4-1BB requires 4-1BB cross-linking. In support of a functional role for TRAF2 in 4-1BB signaling, we find that resting T cells isolated from TRAF2-deficient mice or from mice expressing a dominant negative form of TRAF2 fail to augment IL-2 production in response to soluble 4-1BBL. Thus 4-1BB, via the TRAF2 molecule, can provide CD28-independent costimulatory signals to resting T cells.
Automatic reporting of estimated glomerular filtration rate--just what the doctor ordered.

Chronic kidney disease (CKD) is a recently recognized public health problem. CKD is defined as the presence of markers of kidney damage or of glomerular filtration rate (GFR) <60 mL · min−1 · (1.73 m−2) [<1 mLs−1 · (1.73 m−2)−1] for three months or more (1)(2)(3). According to these definitions, the prevalence of CKD in noninstitutionalized US adults is estimated as ∼11%, or ∼20 million people (4). CKD is associated with poor outcomes and high cost, disproportionately affecting the elderly and racial and ethnic minorities. Thus, new public health campaigns focus on early detection of CKD, especially in patients at increased risk, including those with hypertension, diabetes, cardiovascular disease, or a family history of CKD (5). To facilitate early detection of CKD, many national and international organizations now recommend automatic reporting of estimated glomerular filtration rate (eGFR) whenever serum creatinine is measured (1)(2)(3)(5)(6)(7)(8)(9)(10)(11)(12). Approximately 20% of participants in the College of American Pathologists’ chemistry survey were reporting eGFR calculations in 2005 (13). In this issue of Clinical Chemistry , Dr. Rainey offers a dissenting view, likening automatic eGFR reporting to “jumping the gun”. A careful examination of the arguments reveals, in our view, that reporting eGFR whenever a serum creatinine is requested is “just what the doctor ordered”. Dr. Rainey asserts that “Most creatinine measurements are not made to assess renal function in persons who have CKD or are at high risk for CKD. The great majority of creatinine measurements are ordered as part of a Basic Metabolic Panel”. The serum creatinine level holds no clinically useful meaning other than to serve as an index of kidney function. …
Near-field thermal transport in a nanotip under laser irradiation

We report on a systematic study of highly enhanced optical field and its induced thermal transport in nanotips under laser irradiation. The effects on electric field distribution caused by curvature radius, tip aspect ratio, and polarization angle of the incident laser are studied. Our Poynting vectors’ study clearly shows that when a laser interacts with a metal tip, it is bent around the tip and concentrated under the apex, where extremely high field enhancement appears. This phenomenon is more like a liquid flow being forced/squeezed to go through a narrow channel. As the tip–substrate distance increases, the peak field enhancement decreases exponentially. A shift of field peak position away from the tip axis is observed. For the incident light, only its component along the tip axis direction has a contribution to the electric field enhancement under the tip apex. The optimum tip apex radius for field enhancement is about 9 nm when the half taper angle is 10°. For a tip with a fixed radius of 30 nm, field enhancement increases with the half taper angle when it is less than 25°. The thermal transport inside the nanoscale tungsten tips due to absorption of incident laser light is explored using the finite element method. A small fraction of light penetrates into the tip. As the polarization angle or apex radius increases, the peak apex temperature decreases. The peak apex temperature goes down as the half taper angle increases, even though the mean laser intensity inside the tip increases, revealing a very strong effect of the taper angle on thermal transport.
Likelihood-based approaches to modeling the neural code

One of the central problems in systems neuroscience is that of characterizing the functional relationship between sensory stimuli and neural spike responses. Investigators call this the neural coding problem, because the spike trains of neurons can be considered a code by which the brain represents information about the state of the external world. One approach to understanding this code is to build mathematical models of the mapping between stimuli and spike responses; the code can then be interpreted by using the model to predict the neural response to a stimulus, or to decode the stimulus that gave rise to a particular response. In this chapter, we will examine likelihood-based approaches, which use the explicit probability of response to a given stimulus for both fitting the model and assessing its validity. We will show how the likelihood can be derived for several types of neural models, and discuss theoretical considerations underlying the formulation and estimation of such models. Finally, we will discuss several ideas for evaluating model performance, including time-rescaling of spike trains and optimal decoding using Bayesian inversion of the likelihood function.
Should we consider serum potassium level as a mortality predictor in ST-elevation myocardial infarction?

1. Wilkins GT, Weyman AE, Abascal VM, Block PC, Palacios IF. Percutaneous balloon dilatation of the mitral valve: an analysis of echocardiographic variables related to outcome and the mechanism of dilatation. Br Heart J 1988; 60: 299-308. [CrossRef] 2. Schievano S, Kunzelman K, Nicosia MA, Cochran RP, Einstein DR, Khambadkone S, et al. Percutaneous mitral valve dilatation: single balloon versus double balloon. A finite element study. J Heart Valve Dis 2009; 18: 28-34. 3. Garbarz E, Iung B, Cormier B, Vahania A. Echocardiographic Criteria in Selection of Patients for Percutaneous Mitral Commissurotomy. Echocardiography 1999; 16: 711-21. [CrossRef]
Sensor-Free or Sensor-Full: A Comparison of Data Modalities in Multi-Channel Affect Detection

Computational models that automatically detect learners’ affective states are powerful tools for investigating the interplay of affect and learning. Over the past decade, affect detectors—which recognize learners’ affective states at run-time using behavior logs and sensor data—have advanced substantially across a range of K-12 and postsecondary education settings. Machine learningbased affect detectors can be developed to utilize several types of data, including software logs, video/audio recordings, tutorial dialogues, and physical sensors. However, there has been limited research on how different data modalities combine and complement one another, particularly across different contexts, domains, and populations. In this paper, we describe work using the Generalized Intelligent Framework for Tutoring (GIFT) to build multi-channel affect detection models for a serious game on tactical combat casualty care. We compare the creation and predictive performance of models developed for two different data modalities: 1) software logs of learner interactions with the serious game, and 2) posture data from a Microsoft Kinect sensor. We find that interaction-based detectors outperform posture-based detectors for our population, but show high variability in predictive performance across different affect. Notably, our posture-based detectors largely utilize predictor features drawn from the research literature, but do not replicate prior findings that these features lead to accurate detectors of learner affect.