Beyond bibliometrics, there is interest in characterizing the evolution of the number of ideas in scientific papers. A common approach for investigating this involves analyzing the titles of publications to detect vocabulary changes over time. With the notion that phrases, or more specifically keyphrases, represent concepts, lexical diversity metrics are applied to phrased versions of the titles. Thus changes in lexical diversity are treated as indicators of shifts, and possibly expansion, of research. Therefore, optimizing detection of keyphrases is an important aspect of this process. Rather than just one, we propose to use multiple phrase detection models with the goal to produce a more comprehensive set of keyphrases from the source corpora. Another potential advantage to this approach is that the union and difference of these sets may provide automated techniques for identifying and omitting non-specific phrases. We compare the performance of several phrase detection models, analyze the keyphrase sets output of each, and calculate lexical diversity of corpora variants incorporating keyphrases from each model, using four common lexical diversity metrics.

Offshore wind farms are among the biggest machines we build--vast arrays of towers topped with slowly turning blades. They generate megawatts of power from their giant turbines, taking up miles of space. That means that, as green as they are, they still have an immense impact on the ecology around them, affecting birds, fish, and even the growth of kelp and other marine plants. Managing those turbines is a big issue. Instead, we need to consider them as part of a larger system, one that includes the environment they're part of.

NIR Calibration-Model Services Spectroscopy and Chemometrics News Weekly 33, 2022 NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 33, 2022 NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK Spettroscopia e Chemiometria Weekly News 33, 2022 NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK Near-Infrared Spectroscopy (NIRS) "Comparative Performance of NIR-Hyperspectral Imaging Systems" LINK "Near infrared spectroscopy calibration strategies to predict multiple nutritional parameters of pasture species from different functional groups" LINK "Near-infrared spectroscopy as a tool to assist Sargassum fusiforme quality grading: Harvest time discrimination and polyphenol prediction" LINK "Sensors : Using Vis-NIR Spectroscopy for Predicting Quality Compounds in Foods" LINK "Development of an amino acid sequence-dependent analytical method for peptides using near-infrared spectroscopy" LINK "NDT model study of crown pear based on near infrared spectroscopy" LINK "Analyzing the Water Confined in Hydrogel Using Near-Infrared Spectroscopy" LINK "Foods : Finite Element Analysis and Near-Infrared Hyperspectral Reflectance Imaging for the Determination of Blueberry Bruise Grading" LINK "Application of near infrared spectroscopy in sub-surface monitoring of petroleum contaminants in laboratory-prepared soils" LINK "Identification of multiple raisins by feature fusion combined with NIR spectroscopy" LINK " … of quality markers for quality control of Zanthoxylum nitidum using ultra-performance liquid chromatography coupled with near infrared spectroscopy" LINK "Karakterisasi Fitokimia Enkapsulasi Nira Tebu Powder dengan Menggunakan Varietas BL, PSDK-923, dan PSBM-901" LINK "Inside the Egg--Demonstrating Provenance Without the Cracking Using Near Infrared Spectroscopy" LINK "Organic resources from Madagascar: Dataset of chemical and near-infrared spectroscopy measurements" LINK "An alternative method for identification of industrial tomato hybrids using NIRS" LINK "Uniformity evaluation of stem distribution in cut tobacco and single cigarette by near infrared spectroscopy" LINK "A General and Scalable Vision Framework for Functional Near-Infrared Spectroscopy Classification" LINK "Near infrared spectroscopy for the pre-cure freezing discrimination of Montanera Iberian dry-cured lomito" LINK "Determination of Moisture and Protein Content in Living Mealworm Larvae (Tenebrio molitor L.) Using Near-Infrared Reflectance Spectroscopy (NIRS)" LINK "Towards Inline Prediction of Color Development for Wood Stained with Chemical Stains Using Near-Infrared Spectroscopy" LINK "Comparison Between Pure Component Modeling Approaches for Monitoring Pharmaceutical Powder Blends with Near-Infrared Spectroscopy in Continuous Manufacturing Schemes" LINK "Potential of NIRS technology for the determination of cannabinoid content in industrial hemp (Cannabis sativa L.)" LINK " A Variable Selection Method Based on Fast Nondominated Sorting Genetic Algorithm for Qualitative Discrimination of Near Infrared Spectroscopy" LINK "Scale invariance in fNIRS as a measurement of cognitive load" LINK "Quantification of Salicylates and Flavonoids in Poplar Bark and Leaves Based on IR, NIR, and Raman Spectra" LINK Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR) "Near-infrared spectroscopy monitoring during endovascular treatment for acute ischaemic stroke" LINK "Keakuratan Teknologi Near Infrared Dalam Mengukur Dan Memetakan Bahan Organik Di Pulau Lombok" LINK "NearInfrared Spectroscopic Characterization of Cardiac and Renal Fibrosis in Fixed and Fresh Rat Tissue" LINK "Application of Fourier transform infrared spectroscopy (FTIR) techniques in the mid-IR (MIR) and near-IR (NIR) spectroscopy to determine n-alkane and long-chain alcohol contents in plant species and faecal samples" LINK Hyperspectral Imaging (HSI) "Detection Storage Time of Mild Bruise's Loquats Using Hyperspectral Imaging" LINK "Determination of plumpness for kernel of semen ziziphi spinosae use of hyperspectral transmittance imaging technology coupled with improved Otsu algorithm" LINK "Prediction of oil content in single maize kernel based on hyperspectral imaging and attention convolution neural network" LINK "Convolutional neural networks for mapping of lake sediment core particle size using hyperspectral imaging" LINK Spectral Imaging "Applied Sciences : Non-Invasive Monitoring of the Thermal and Morphometric Characteristics of Lettuce Grown in an Aeroponic System through Multispectral Image System" LINK Chemometrics and Machine Learning "Rapid quantification of goat milk adulteration with cow milk using Raman spectroscopy and chemometrics" LINK "Plants : Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow" LINK "Near Infrared Spectra Data Analysis by Using Machine Learning Algorithms" LINK "Applied Sciences : Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan" LINK "Predicting maize LAI in partial least square modeling by continuous wavelet transform and uninformative variable elimination from canopy spectral reflectance" LINK "Machine Learning Algorithms for Protein Physicochemical Component Prediction Using Near Infrared Spectroscopy in Chickpea Germplasm" LINK "NIR Validation and Calibration of Proximate components of available Corn Silage in Bangladesh." So interested people will connect.

With the uptake of electric vehicles (EVs) increasing across the automotive market, there is a need to ensure optimized function and reliability of the battery that is powering the vehicle. Across many industries and markets, lithium-ion (Li-ion) batteries are crucial components of devices and machinery, including smartphones, solar power storage, and power supplies. Thus, maintaining good battery health is absolutely vital in today's world. Now, a group of researchers from the University of Cambridge has recently developed a new algorithm that uses machine learning to help preserve good battery health in EVs. The algorithm is able to use pattern recognition and predictability models to see how various driving styles influence the performance of the vehicle's battery.

The rapid emergence of new technologies, such as artificial intelligence, edge computing and smart mobility, is accelerating the pace of digital transformation worldwide. Covid-induced market disruptions and widespread adoption of hybrid work models have also fast-tracked the process along with the influx of new investment in the sector. Global spending on digital transformation is predicted to jump almost 18 per cent annually to $1.8 trillion this year, according to Massachusetts-based researcher International Data Corporation. "Technology is changing everything in our work and home lives," Lareina Yee, senior partner at McKinsey and chair at McKinsey Technology Council, said. The consultancy launched the McKinsey Technology Council to help understand new technologies and how they will affect end users.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Here's what businesses can learn from the small group of organizations that already use artificial (AI) to their competitive advantage. If the world's largest companies were people, most would be in their teenage years when it comes to using Artificial Intelligence (AI). According to new research from Accenture on AI maturity, 63% of 1,200 companies were identified as "Experimenters," or companies that are stuck in the experimentation phase of their AI lives.

Benefiting from the search efficiency, differentiable neural architecture search (NAS) has evolved as the most dominant alternative to automatically design competitive deep neural networks (DNNs). We note that DNNs must be executed under strictly hard performance constraints in real-world scenarios, for example, the runtime latency on autonomous vehicles. However, to obtain the architecture that meets the given performance constraint, previous hardware-aware differentiable NAS methods have to repeat a plethora of search runs to manually tune the hyper-parameters by trial and error, and thus the total design cost increases proportionally. To resolve this, we introduce a lightweight hardware-aware differentiable NAS framework dubbed LightNAS, striving to find the required architecture that satisfies various performance constraints through a one-time search (i.e., \underline{\textit{you only search once}}). Extensive experiments are conducted to show the superiority of LightNAS over previous state-of-the-art methods.

This paper presents a fault-tolerant 3D vision system for autonomous robotic operation. In particular, pose estimation of space objects is achieved using 3D vision data in an integrated Kalman filter (KF) and an Iterative Closest Point (ICP) algorithm in a closed-loop configuration. The initial guess for the internal ICP iteration is provided by the state estimate propagation of the Kalman filer. The Kalman filter is capable of not only estimating the target's states but also its inertial parameters. This allows the motion of the target to be predictable as soon as the filter converges. Consequently, the ICP can maintain pose tracking over a wider range of velocity due to the increased precision of ICP initialization. Furthermore, incorporation of the target's dynamics model in the estimation process allows the estimator continuously provide pose estimation even when the sensor temporally loses its signal namely due to obstruction. The capabilities of the pose estimation methodology is demonstrated by a ground testbed for Automated Rendezvous & Docking. In this experiment, Neptec's Laser Camera System (LCS) is used for real-time scanning of a satellite model attached to a manipulator arm, which is driven by a simulator according to orbital and attitude dynamics. The results showed that robust tracking of the free-floating tumbling satellite can be achieved only if the Kalman filter and ICP are in a closed-loop configuration.

The central object of this PhD thesis is known under different names in the fields of computer science and statistical mechanics. In computer science, it is called the Maximum Cut problem, one of the famous twenty-one Karp's original NP-hard problems, while the same object from Physics is called the Ising Spin Glass model. This model of a rich structure often appears as a reduction or reformulation of real-world problems from computer science, physics and engineering. However, solving this model exactly (finding the maximal cut or the ground state) is likely to stay an intractable problem (unless $\textit{P} = \textit{NP}$) and requires the development of ad-hoc heuristics for every particular family of instances. One of the bright and beautiful connections between discrete and continuous optimization is a Semidefinite Programming-based rounding scheme for Maximum Cut. This procedure allows us to find a provably near-optimal solution; moreover, this method is conjectured to be the best possible in polynomial time. In the first two chapters of this thesis, we investigate local non-convex heuristics intended to improve the rounding scheme. In the last chapter of this thesis, we make one step further and aim to control the solution of the problem we wanted to solve in previous chapters. We formulate a bi-level optimization problem over the Ising model where we want to tweak the interactions as little as possible so that the ground state of the resulting Ising model satisfies the desired criteria. This kind of problem arises in pandemic modeling. We show that when the interactions are non-negative, our bi-level optimization is solvable in polynomial time using convex programming.

Garnets, known since the early stages of human civilization, have found important applications in modern technologies including magnetorestriction, spintronics, lithium batteries, etc. The overwhelming majority of experimentally known garnets are oxides, while explorations (experimental or theoretical) for the rest of the chemical space have been limited in scope. A key issue is that the garnet structure has a large primitive unit cell, requiring an enormous amount of computational resources. To perform a comprehensive search of the complete chemical space for new garnets,we combine recent progress in graph neural networks with high-throughput calculations. We apply the machine learning model to identify the potential (meta-)stable garnet systems before systematic density-functional calculations to validate the predictions. In this way, we discover more than 600 ternary garnets with distances to the convex hull below 100~meV/atom with a variety of physical and chemical properties. This includes sulfide, nitride and halide garnets. For these, we analyze the electronic structure and discuss the connection between the value of the electronic band gap and charge balance.