Development of a metrological reference system for the form measurement of aspheres and freeform surfaces based on a tilted-wave interferometer

Aspheres and freeform surfaces play an important role in today's optics industry. However, the measurement of such complex surfaces is still challenging even with state-of-the-art manufacturing technology, and there is an urgent need in industry for a non-contact, highly accurate reference measurement technique. To meet this demand, at PTB, a metrological reference system for the contact-free form measurement of aspheres and freeform surfaces is under development. The measurement system is based on a tilted-wave interferometer. Advances in computational capabilities have made it possible to solve the complex inverse problems associated with this measurement system and to develop sophisticated analysis procedures for reconstructing the surface under test from the measured interferogram data. In this paper, we will present the status of the tilted-wave interferometer-based measurement system at PTB, describe the analysis procedures we have designed and show initial measurement results. The benefit of the implementation presented here is that it allows insight to be gained into the performance of the measurement system and enables traceable measurements to be established with low uncertainty.

Variation in isocentre location of an Elekta Unity MR-linac through full gantry rotation

Objective: The objective of this study was to separately quantify the stability of the megavoltage imager (MVI) and radiation head of an Elekta Unity MRL, throughout full gantry rotation. Approach: A ball-bearing (BB) phantom was attached to the radiation head of the Unity, while a single BB was placed at isocentre. Images were acquired during rotation, using the MVI. These images were processed using an in-house developed MATLAB program to reduce the errors resulted by noise, and the positions of the BBs in the images were analysed to extract MVI and radiation head sag data. Main results: The results returned by this method showed reproducibility, with a mean standard deviation of 7 µm for the position of BBs across all gantry angles. The radiation head was found to sag throughout rotation, with a maximum course of movement of 0.59 mm. The sag pattern was stable over a period greater than a year but showed some dependence on gantry rotation direction. Significance: As MRL is a relatively new system, it is promising to have data supporting the high level of precision on one Elekta Unity machine. Isolating and quantifying the sources of uncertainty in radiation delivery may allow more sophisticated analysis of how the system performance may be improved.

Intact Drosophila Whole Brain Cellular Quantitation reveals Sexual Dimorphism

Establishing with precision the quantity and identity of the cell types of the brain is a prerequisite for a detailed compendium of gene and protein expression in the central nervous system. Currently however, strict quantitation of cell numbers has been achieved only for the nervous system of C.elegans. Here we describe the development of a synergistic pipeline of molecular genetic, imaging, and computational technologies designed to allow high-throughput, precise quantitation with cellular resolution of reporters of gene expression in intact whole tissues with complex cellular constitutions such as the brain. We have deployed the approach to determine with exactitude the number of functional neurons and glia in the entire intact Drosophila larval brain, revealing fewer neurons and many more glial cells than previously estimated. Moreover, we discover an unexpected divergence between the sexes at this juvenile developmental stage, with female brains having significantly more neurons than males. Topological analysis of our data establishes that this sexual dimorphism extends to deeper features of brain organisation. Our methodology enables robust and accurate quantification of the number and positioning of cells within intact organs, facilitating sophisticated analysis of cellular identity, diversity, and expression characteristics.

Full-Page Wrapper Generation for Unsupervised Deep Web Data Extraction

<div>Web data extraction is a key component in many business intelligence tasks, such as data transformation, exchange, and analysis. Many approaches have been proposed, with either labeled training examples (supervised) or annotation-free training pages (unsupervised). However, most research focuses on extraction effectiveness. Not much attention has been paid to extraction efficiency. In fact, most unsupervised web data extraction ignores wrapper generation because they could work alone without any supervision. </div><div>In this paper, we argue that wrapper generation for unsupervised web data extraction is as important as supervised wrapper induction because the generated wrappers could work more efficiently without sophisticated analysis during testing. We consider two approaches for wrapper generation: schema-guided finite-state machine (FSM) approaches and data-driven machine learning (ML) approaches. We exploit unique mandatory templates to improve the FSM-based wrapper, and proposed two convolutional neural network (CNN)-based models for sequence-labeling. The experimental results show that the FSM wrapper performs well even with small training data, while the CNN-based models require more training pages to achieve the same effectiveness but are more efficient with GPU support. Furthermore, FSM wrappers can work as a filter to reduce the number of training pages and advance the learning curve for wrapper generation.</div>

Full-Page Wrapper Generation for Unsupervised Deep Web Data Extraction

<div>Web data extraction is a key component in many business intelligence tasks, such as data transformation, exchange, and analysis. Many approaches have been proposed, with either labeled training examples (supervised) or annotation-free training pages (unsupervised). However, most research focuses on extraction effectiveness. Not much attention has been paid to extraction efficiency. In fact, most unsupervised web data extraction ignores wrapper generation because they could work alone without any supervision. </div><div>In this paper, we argue that wrapper generation for unsupervised web data extraction is as important as supervised wrapper induction because the generated wrappers could work more efficiently without sophisticated analysis during testing. We consider two approaches for wrapper generation: schema-guided finite-state machine (FSM) approaches and data-driven machine learning (ML) approaches. We exploit unique mandatory templates to improve the FSM-based wrapper, and proposed two convolutional neural network (CNN)-based models for sequence-labeling. The experimental results show that the FSM wrapper performs well even with small training data, while the CNN-based models require more training pages to achieve the same effectiveness but are more efficient with GPU support. Furthermore, FSM wrappers can work as a filter to reduce the number of training pages and advance the learning curve for wrapper generation.</div>

Analysis Strategies for MHz XPCS at the European XFEL

The nanometer length-scale holds precious information on several dynamical processes that develop from picoseconds to seconds. In the past decades, X-ray scattering techniques have been developed to probe the dynamics at such length-scales on either ultrafast (sub-nanosecond) or slow ((milli-)second) time scales. With the start of operation of the European XFEL, thanks to the MHz repetition rate of its X-ray pulses, even the intermediate μs range have become accessible. Measuring dynamics on such fast timescales requires the development of new technologies such as the Adaptive Gain Integrating Pixel Detector (AGIPD). μs-XPCS is a promising technique to answer many scientific questions regarding microscopic structural dynamics, especially for soft condensed matter systems. However, obtaining reliable results with complex detectors at free-electron laser facilities is challenging and requires more sophisticated analysis methods compared to experiments at storage rings. Here, we discuss challenges and possible solutions to perform XPCS experiments with the AGIPD at European XFEL; in particular, at the Materials Imaging and Dynamics (MID) instrument. We present our data analysis pipeline and benchmark the results obtained at the MID instrument with a well-known sample composed by silica nanoparticles dispersed in water.

ESTIMATING DESIGN EFFORT NEEDS OF PRODUCT DESIGN PROJECTS USING CAPTURED EXPERT KNOWLEDGE – A PROPOSED METHOD

The quick and accurate estimation of design effort can be make or break for all but the largest of product design consultancies. Traditional design project planning see designers being taken away from the metaphorical drawing board to spend time assessing project briefs and estimating workloads. Typically these designers base these estimates on their tacit knowledge and experience, and for the most part, they are accurate. However, this is time-consuming and therefore (indirectly) costly, as time spent planning, is not time spent designing. Many more sophisticated approaches for estimating design effort have been developed, but many require large bodies of past data and sophisticated analysis, such as artificial neural networks; and others have highly-specific use cases.This paper proposes a new method to develop a design effort estimation tool for product design consultancies. This method captures the tacit knowledge and experience of design team members and the tool replicates it quickly and effectively; graphically modelling factors that influence design effort needs in product design projects.

ribofootPrinter: A precision python toolbox for analysis of ribosome profiling data

Ribosome profiling is a valuable methodology for measuring changes in a cell's translational program. The approach can report how efficiently mRNA coding sequences are translated and pinpoint positions along mRNAs where ribosomes slow down or arrest. It can also reveal when translation takes place outside coding regions, often with important regulatory consequences. While many useful software tools have emerged to facilitate analysis of these data, packages can become complex and challenging to adapt to specialized needs. In particular, the results of meta analysis of average footprint data around sequence motifs of interest can vary substantially depending on the normalization method that is utilized. We therefore introduce ribofootPrinter, a suite of Python tools designed to offer an accessible and modifiable set of code for analysis of ribosome profiling data. Footprint alignments are made to a simplified transcriptome, keeping the code intuitive, and multiple normalization options help facilitate interpretation of meta analysis, particularly outside coding regions. We believe this tool has promise to carry out sophisticated analysis yet offer simplicity to make it readily understandable and adaptable.

Discovering the origin of Yukawa couplings at the LHC with a singlet Higgs and vector-like quarks

Although the 125 GeV Higgs boson discovered at the LHC is often heralded as the origin of mass, it may not in fact be the origin of Yukawa couplings. In alternative models, Yukawa couplings may instead arise from a seesaw type mechanism involving the mixing of Standard Model (SM) chiral fermions with new vector-like fermions, controlled by the vacuum expectation value (VEV) of a new complex Higgs singlet field 〈Φ〉. For example, the largest third family (t, b) quark Yukawa couplings may be forbidden by a U(1)′ gauge or global symmetry, broken by 〈Φ〉, and generated effectively via mixing with a vector-like fourth family quark doublet (T, B). Such theories predict a new physical Higgs singlet ϕ, which we refer to as the Yukon, resulting from 〈Φ〉, in the same way that the Higgs boson h0 results from 〈H〉. In a simplified model we discuss the prospects for discovering the Yukon ϕ in gluon-gluon fusion production, with (t, b) and (T, B) quarks in the loops, and decaying in the channels ϕ → γγ, Zγ and ϕ → tT → tth0, ttZ. The potential for discovery of the Yukon ϕ is studied at present or future hadron colliders such as the LHC (Run 3), HL-LHC, HE-LHC and/or FCC. For example, we find that a 300–350 GeV Yukon ϕ could be accessed at LHC Run 3 in the di-photon channel in the global model, providing a smoking gun signature of the origin of Yukawa couplings. The tth0, ttZ channels are more involved and warrant a more sophisticated analysis.