Actively Mapping Industrial Structures with Information Gain-Based Planning on a Quadruped Robot

In this paper, we develop an online active mapping system to enable a quadruped robot to autonomously survey large physical structures. We describe the perception, planning and control modules needed to scan and reconstruct an object of interest, without requiring a prior model. The system builds a voxel representation of the object, and iteratively determines the Next-Best-View (NBV) to extend the representation, according to both the reconstruction itself and to avoid collisions with the environment. By computing the expected information gain of a set of candidate scan locations sampled on the as-sensed terrain map, as well as the cost of reaching these candidates, the robot decides the NBV for further exploration. The robot plans an optimal path towards the NBV, avoiding obstacles and un-traversable terrain. Experimental results on both simulated and real-world environments show the capability and efficiency of our system. Finally we present a full system demonstration on the real robot, the ANYbotics ANYmal, autonomously reconstructing a building facade and an industrial structure

Superlens-Assisted Laser Nanostructuring of Long Period Optical Fiber Gratings (LPGs) for Enhanced Refractive Index Sensing

We present an innovative method to enhance Long Period Optical Fiber Gratings (LPGs) for refractive index sensing using microsphere-assisted superlens laser nanostructuring. This technique involves self-assembling a silica microsphere monolayer on LPGs' outer surface, followed by pulsed laser irradiation to generate nanoholes (300-500 nm) forming nanohole-structured LPGs (NS-LPGs). In experiments, two nanohole densities were compared for their impact on sensing performance in sucrose and glycerin solutions. The nanostructured NS-LPGs showed improved sensitivity by 16.08% and 19.57% compared to regular LPGs, with higher nanohole density yielding greater enhancement. Importantly, the permanent nanohole structures ensure durability in harsh environments, surpassing conventional surface-coating-based LPGs. Further improvements can be achieved by refining nanostructuring density and controlling nanohole size and depth. Our work represents a notable advancement in LPG sensor engineering, prioritizing surface nanostructuring over nano-coating, promising enhanced refractive index sensing applications.Comment: 13 pages, 5 figure

Enhancing Security with Superlens-Enabled Laser Direct Marking of Anti-counterfeiting DotCode

We report a novel anti-counterfeiting laser marking technology based on superlens- assisted nanoscale marking of 2D Dotcodes, which replaces conventional TEXT or other 2D code schemes for enhanced security

Generation Expansion Planning with Large Amounts of Wind Power via Decision-Dependent Stochastic Programming

Power generation expansion planning needs to deal with future uncertainties carefully, given that the invested generation assets will be in operation for a long time. Many stochastic programming models have been proposed to tackle this challenge. However, most previous works assume predetermined future uncertainties (i.e., fixed random outcomes with given probabilities). In several recent studies of generation assets\u27 planning (e.g., thermal versus renewable), new findings show that the investment decisions could affect the future uncertainties as well. To this end, this paper proposes a multistage decision-dependent stochastic optimization model for long-term large-scale generation expansion planning, where large amounts of wind power are involved. In the decision-dependent model, the future uncertainties are not only affecting but also affected by the current decisions. In particular, the probability distribution function is determined by not only input parameters but also decision variables. To deal with the nonlinear constraints in our model, a quasi-exact solution approach is then introduced to reformulate the multistage stochastic investment model to a mixed-integer linear programming model. The wind penetration, investment decisions, and the optimality of the decision-dependent model are evaluated in a series of multistage case studies. The results show that the proposed decision-dependent model provides effective optimization solutions for long-term generation expansion planning

Time To Rethink Engineering Outreach?

Starting with the research question ‘Does engineering outreach work?’ this paper looks at the often ‘sticky’ subject of the validity of engineering outreach in UK High Schools. It examines how Engineering Outreach Activities are conceptualised by external bodies (RAEng., 2016) and critiques the complex range of practical experiential engineering educational interventions offered in school (Neon, 2023, STEM learning, 2023). Drawing upon the findings of, what is, a small single strand of a much larger multi-method, longitudinal analysis of Engineering Education Outreach Activities provided across the West Midlands region of the UK (LBEEP, 2023) ], the paper provides a unique insight and descriptive analysis of engineering outreach in schools. The findings section comprises a comparative analysis of the socio-economic background of schools before looking at the gender breakdown of outreach participants. The various engineering interventions provided are briefly discussed before consideration is given as to how sustainable current engineering outreach activities are. Finally, in questioning whether the UK’s current approach of providing engineering education experiences in the form of what are often idiosyncratic, short term episodic activities, the paper questions the financial, pedagogic and practical wisdom of confining engineering education to ‘outreach’. The conclusion suggests that it’s time for a sea-change in how we, as a society, teach children and young people about engineering and suggests that perhaps it is time to embed the subject into more established areas of study such as maths and science but also in history and social science

AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models

Evaluating the general abilities of foundation models to tackle human-level tasks is a vital aspect of their development and application in the pursuit of Artificial General Intelligence (AGI). Traditional benchmarks, which rely on artificial datasets, may not accurately represent human-level capabilities. In this paper, we introduce AGIEval, a novel benchmark specifically designed to assess foundation model in the context of human-centric standardized exams, such as college entrance exams, law school admission tests, math competitions, and lawyer qualification tests. We evaluate several state-of-the-art foundation models, including GPT-4, ChatGPT, and Text-Davinci-003, using this benchmark. Impressively, GPT-4 surpasses average human performance on SAT, LSAT, and math competitions, attaining a 95% accuracy rate on the SAT Math test and a 92.5% accuracy on the English test of the Chinese national college entrance exam. This demonstrates the extraordinary performance of contemporary foundation models. In contrast, we also find that GPT-4 is less proficient in tasks that require complex reasoning or specific domain knowledge. Our comprehensive analyses of model capabilities (understanding, knowledge, reasoning, and calculation) reveal these models' strengths and limitations, providing valuable insights into future directions for enhancing their general capabilities. By concentrating on tasks pertinent to human cognition and decision-making, our benchmark delivers a more meaningful and robust evaluation of foundation models' performance in real-world scenarios. The data, code, and all model outputs are released in https://github.com/microsoft/AGIEval.Comment: 19 page

A Novel STAP Algorithm for Airborne MIMO Radar Based on Temporally Correlated Multiple Sparse Bayesian Learning

In a heterogeneous environment, to efficiently suppress clutter with only one snapshot, a novel STAP algorithm for multiple-input multiple-output (MIMO) radar based on sparse representation, referred to as MIMOSR-STAP in this paper, is presented. By exploiting the waveform diversity of MIMO radar, each snapshot at the tested range cell can be transformed into multisnapshots for the phased array radar, which can estimate the high-resolution space-time spectrum by using multiple measurement vectors (MMV) technique. The proposed approach is effective in estimating the spectrum by utilizing Temporally Correlated Multiple Sparse Bayesian Learning (TMSBL). In the sequel, the clutter covariance matrix (CCM) and the corresponding adaptive weight vector can be efficiently obtained. MIMOSR-STAP enjoys high accuracy and robustness so that it can achieve better performance of output signal-to-clutter-plus-noise ratio (SCNR) and minimum detectable velocity (MDV) than the single measurement vector sparse representation methods in the literature. Thus, MIMOSR-STAP can deal with badly inhomogeneous clutter scenario more effectively, especially suitable for insufficient independent and identically distributed (IID) samples environment

Highly curved reflective W-shape and J-shape photonic hook induced by light interaction with partially coated microfluidic channels

Photonic hook (PH) is a new type of artificial self-bending beam focused by a dielectric particle-lens with a curved waist smaller than the wavelength, which has the potential to revolutionize mesoscale photonics in many applications, e.g., optical trapping, signal switching, imaging, etc. In this paper, we discover a new mechanism that the highly curved PHs can be realised by the light interaction with the fully or partially metal-coated microchannels. The generated W-shaped and J-shaped PHs have bending angles exceeding 80-degree. Compared to other PH setups, the proposed design has a larger range to flexibly control the bending angle through the coating process and can be easily integrated with the established microfluidic systems.Comment: 10 pages, 5 figure

SUPERFAMILY—sophisticated comparative genomics, data mining, visualization and phylogeny

SUPERFAMILY provides structural, functional and evolutionary information for proteins from all completely sequenced genomes, and large sequence collections such as UniProt. Protein domain assignments for over 900 genomes are included in the database, which can be accessed at http://supfam.org/. Hidden Markov models based on Structural Classification of Proteins (SCOP) domain definitions at the superfamily level are used to provide structural annotation. We recently produced a new model library based on SCOP 1.73. Family level assignments are also available. From the web site users can submit sequences for SCOP domain classification; search for keywords such as superfamilies, families, organism names, models and sequence identifiers; find over- and underrepresented families or superfamilies within a genome relative to other genomes or groups of genomes; compare domain architectures across selections of genomes and finally build multiple sequence alignments between Protein Data Bank (PDB), genomic and custom sequences. Recent extensions to the database include InterPro abstracts and Gene Ontology terms for superfamiles, taxonomic visualization of the distribution of families across the tree of life, searches for functionally similar domain architectures and phylogenetic trees. The database, models and associated scripts are available for download from the ftp site