An overview of techniques applied to the extraction of non-pollen palynomorphs, their known taphonomic issues and recommendations to maximize recovery

AbstractThis chapter synthesizes the most common processing techniques applied to palynomorphs and their known issues. We primarily focus on non-pollen palynomorphs (NPPs), but include studies on pollen grains where the information might be relevant. An overview of recent (2017–19) NPP publications is used to connect the most common techniques to known taphonomic issues. Finally, general recommendations are made to minimize processing bias and maximize NPP recovery.</jats:p

Deposition of Cerium-Based Conversion Coatings on Aluminum Alloy 380

Cerium-based conversion coatings were deposited on as-cast aluminum alloy 380 substrates by a spontaneous immersion process. In this study, the effects of rinsing temperature prior to immersion in the coating deposition solution were studied with respect to the surface morphology, electrochemical response, and corrosion resistance of the coatings. Panels rinsed at 25°C prior to coating had large cracks and holes in the coating. In contrast, panels rinsed at 100°C prior to coating had a uniform coating morphology with fewer, smaller cracks. Electrochemical testing revealed that coatings deposited on substrates rinsed at 100°C had higher impedance (~80 kΩ·cm2) and lower corrosion current (~0.34 μA/cm2) compared to coatings deposited on substrates rinsed at 25°C, which had 10 kΩ·cm2 impedance and 2.7 μA/cm2 corrosion current. Finally, ASTM B117 salt spray testing showed that rinsing at 100°C prior to coating resulted in cerium-based conversion coatings that could resist the formation of salt tails for at least 8 days

Event-based camera refractory period characterization and initial clock drift evaluation

Event-based camera (EBC) technology provides high-dynamic range operation and shows promise for efficient capture of spatio-temporal information, producing a sparse data stream and enabling consideration of nontraditional data processing solutions (e.g., new algorithms, neuromorphic processors, etc.). Given the fundamental difference in camera architecture, the EBC response and noise behavior differ considerably compared to standard CCD/CMOS framing sensors. These differences necessitate the development of new characterization techniques and sensor models to evaluate hardware performance and elucidate the trade-space between the two camera architectures. Laboratory characterization techniques reported previously include noise level as a function of static scene light level (background activity) and contrast responses referred to as S-curves. Here we present further progress on development of basic characterization methods and test capabilities for commercial-off-the-shelf (COTS) visible EBCs, with a focus on measurement of pixel deadtime (refractory period) including results for the 4th-generation sensor from Prophesee and Sony. Refractory period is empirically determined from analysis of the interspike intervals (ISIs), and results visualized using log-histograms of the minimum per-pixel ISI values for a subset of pixels activated by a controlled dynamic scene. Our tests of the Prophesee gen4 EVKv2 yield refractory period estimates ranging from 6.1 msec to 6.8 μsec going from the slowest (20) to fastest (100) settings of the relevant bias parameter, bias_refr. We also introduce and demonstrate the concept of pixel bandwidth measurement from data captured while viewing a static scene – based on recording data at a range of refractory period setting and then analyzing noise-event statistics. Finally, we present initial results for estimating and correcting EBC clock drift using a GPS PPS signal to generate special timing events in the event-list data streams generated by the DAVIS346 and DVXplorer EBCs from iniVation

Why a new volume on Non-Pollen Palynomorphs?

Here we introduce SP511, Applications of Non-Pollen Palynomorphs: from Palaeoenvironmental Reconstructions to Biostratigraphy. The study of Non-pollen palynomorphs (NPPs) has a long and rich history that is interwoven with that of pollen-based studies. NPPs are among the oldest fossils on record, and are instrumental in determining the origin and evolution of life, as well as studying origination and extinction events prior to the origin of pollen-producing angiosperms. This new volume on NPPs provides an up-to-date and seminal overview of the subject, linking deep-time and Quaternary study of the subject for the first time

A complementary systems account of word learning: neural and behavioural evidence

In this paper we present a novel theory of the cognitive and neural processes by which adults learn new spoken words. This proposal builds on neurocomputational accounts of lexical processing and spoken word recognition and complementary learning systems (CLS) models of memory. We review evidence from behavioural studies of word learning that, consistent with the CLS account, show two stages of lexical acquisition: rapid initial familiarization followed by slow lexical consolidation. These stages map broadly onto two systems involved in different aspects of word learning: (i) rapid, initial acquisition supported by medial temporal and hippocampal learning, (ii) slower neocortical learning achieved by offline consolidation of previously acquired information. We review behavioural and neuroscientific evidence consistent with this account, including a meta-analysis of PET and functional Magnetic Resonance Imaging (fMRI) studies that contrast responses to spoken words and pseudowords. From this meta-analysis we derive predictions for the location and direction of cortical response changes following familiarization with pseudowords. This allows us to assess evidence for learning-induced changes that convert pseudoword responses into real word responses. Results provide unique support for the CLS account since hippocampal responses change during initial learning, whereas cortical responses to pseudowords only become word-like if overnight consolidation follows initial learning

Theta Rhythms Coordinate Hippocampal–Prefrontal Interactions in a Spatial Memory Task

Decision-making requires the coordinated activity of diverse brain structures. For example, in maze-based tasks, the prefrontal cortex must integrate spatial information encoded in the hippocampus with mnemonic information concerning route and task rules in order to direct behavior appropriately. Using simultaneous tetrode recordings from CA1 of the rat hippocampus and medial prefrontal cortex, we show that correlated firing in the two structures is selectively enhanced during behavior that recruits spatial working memory, allowing the integration of hippocampal spatial information into a broader, decision-making network. The increased correlations are paralleled by enhanced coupling of the two structures in the 4- to 12-Hz theta-frequency range. Thus the coordination of theta rhythms may constitute a general mechanism through which the relative timing of disparate neural activities can be controlled, allowing specialized brain structures to both encode information independently and to interact selectively according to current behavioral demands

Reservoir Computing with Thin-film Ferromagnetic Devices

Advances in artificial intelligence are driven by technologies inspired by the brain, but these technologies are orders of magnitude less powerful and energy efficient than biological systems. Inspired by the nonlinear dynamics of neural networks, new unconventional computing hardware has emerged with the potential for extreme parallelism and ultra-low power consumption. Physical reservoir computing demonstrates this with a variety of unconventional systems from optical-based to spintronic. Reservoir computers provide a nonlinear projection of the task input into a high-dimensional feature space by exploiting the system's internal dynamics. A trained readout layer then combines features to perform tasks, such as pattern recognition and time-series analysis. Despite progress, achieving state-of-the-art performance without external signal processing to the reservoir remains challenging. Here we show, through simulation, that magnetic materials in thin-film geometries can realise reservoir computers with greater than or similar accuracy to digital recurrent neural networks. Our results reveal that basic spin properties of magnetic films generate the required nonlinear dynamics and memory to solve machine learning tasks. Furthermore, we show that neuromorphic hardware can be reduced in size by removing the need for discrete neural components and external processing. The natural dynamics and nanoscale size of magnetic thin-films present a new path towards fast energy-efficient computing with the potential to innovate portable smart devices, self driving vehicles, and robotics

A perspective on physical reservoir computing with nanomagnetic devices

Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this success comes at a great price; the energy requirements for training advanced models are unsustainable. One promising way to address this pressing issue is by developing low-energy neuromorphic hardware that directly supports the algorithm's requirements. The intrinsic non-volatility, non-linearity, and memory of spintronic devices make them appealing candidates for neuromorphic devices. Here we focus on the reservoir computing paradigm, a recurrent network with a simple training algorithm suitable for computation with spintronic devices since they can provide the properties of non-linearity and memory. We review technologies and methods for developing neuromorphic spintronic devices and conclude with critical open issues to address before such devices become widely used

Gendered financial &amp; nutritional benefits from access to pay-as-you-go LPG for cooking in an informal settlement in Nairobi, Kenya

This study investigates the association between adoption of pay-as-you-go (PAYG) liquefied petroleum gas (LPG), an emerging alternative to full cylinder LPG, and women's economic empowerment in an informal settlement in Nairobi, Kenya. From December 2021-January 2022, 293 customers of a PAYG LPG company (PayGo Energy) were surveyed on their cooking patterns, financial savings and shifts in dietary behaviors following uptake of the technology. Among PayGo Energy customers that previously cooked only with polluting fuels (N = 78; 27 % of customers), daily cooking time was reduced by an average of 42 min/day; 82 % of PayGo Energy customers that previously cooked with full cylinder LPG (N = 216; 73 % of customers) also decreased their cooking time (average of 20 min/day) when switching to PAYG LPG. The majority (58 %; N = 70) of female household heads took on additional employment after switching to PAYG LPG, compared with 36 % (N = 55) of females living in male-headed households. Among female household heads, the proportion of informal sector workers earning wages on an irregular (71 %) or daily basis (61 %) that took on new income-generating activities after transitioning to PAYG LPG was over 20 % higher than those earning monthly salaries (39 %). Increased dietary diversity and consumption of protein-rich foods (legumes, meat, fish) from cooking with PAYG LPG was reported by 15 % of female household heads compared with 5 % of those living in male-headed households. While nearly three-quarters (73 %) of PayGo Energy customers would recommend the service to others because of the added convenience it provides, only one-third (29 %) reported associated health benefits as a key reason for promoting use of PAYG LPG to community members. Female household heads were more likely than non-household heads to be socioeconomically empowered when adopting PAYG LPG, illustrating that women's agency may influence the associated benefits of clean energy transitions. Nonetheless, the time savings reported by nearly all women who switched to PAYG LPG for cooking suggests that promoting the increased convenience of cooking with PAYG LPG may be useful for accelerating its adoption