Miracle-dz/UniFAR-FTUs huggingface dataset:
Finding Sparse Initialisations using Neuroevolutionary Ticket Search (NeTS)

The explosion of interest in deep learning over the last decade has been driven by scaling mathematical models of brains referred to as Deep Neural Networks (DNNs). Common deep learning architectures have millions of parameters that require optimisation (training) for the network to learn tasks; and empirical evidence suggests training an overparame-terised network (with more parameters than data points in the training data) is necessary for the network to learn. Despite this, it has been shown that overparameterised DNNs can typically be compressed to a fraction of their original size once trained (frequently by over 90%). Further, the Lottery Ticket Hypothesis (LTH) asserts that there exists a much sparser but equally trainable subnetwork within any sufficiently overpa-rameterised DNN initialisation . In other words, the number of parameters could be significantly reduced from the outset if we could find these smaller initialisations (referred to as winning tickets ). In this paper, we introduce a new evolutionary algorithm for finding winning tickets given a feed-forward or convolutional DNN architecture, and compare our approach to the current state-of-the-art. We refer to our algorithm as Neuroevolutionary Ticket Search (NeTS) and find it discovers competitive winning tickets for a variety of architectures and two common training datasets (MNIST and CIFAR-10). We show that NeTS can be applied to pruning DNNs before substantial training with gradient descent by genetically optimising a genome consisting of a set of initial weights and a binary pruning mask; this appears to offer a significant performance benefit.
Model Predictive Control Framework for Improving Vehicle Cornering Performance Using Handling Characteristics

This paper proposes a new control strategy to improve vehicle cornering performance in a model predictive control framework. The most distinguishing feature of the proposed method is that the natural handling characteristics of the production vehicle is exploited to reduce the complexity of the conventional control methods. For safety’s sake, most production vehicles are built to exhibit an understeer handling characteristics to some extent. By monitoring how much the vehicle is biased into the understeer state, the controller attempts to adjust this amount in a way that improves the vehicle cornering performance. With this particular strategy, an innovative controller can be designed without road friction information, which complicates the conventional control methods. In addition, unlike the conventional controllers, the reference yaw rate that is highly dependent on road friction need not be defined due to the proposed control structure. The optimal control problem is formulated in a model predictive control framework to handle the constraints efficiently, and simulations in various test scenarios illustrate the effectiveness of the proposed approach.
Revealing the Influence of SiC Particle Size on the Hot Workability of SiCp/6013 Aluminum Matrix Composites

SiC particle (SiCp) size has been found to significantly influence the hot workability of particle-reinforced aluminum matrix composites (AMC). In this work, therefore, three types of SiCp/6013 composites with different SiCp sizes (0.7, 5 and 15 μm) were prepared and then subjected to isothermal hot compression tests. In addition, constitutive analysis, processing maps and microstructural characterizations were used to reveal the influence of SiCp size on the hot workability of SiCp/6013 composite. The results showed that the values of hot deformation activation energy Q increased with decreasing SiCp size. Specifically, at lower temperatures (e.g., 350 and 400 °C), the highest peak stress was shown in the AMC with SiCp size of 0.7 μm (AMC-0.7), while in the AMC with SiCp size of 5 μm (AMC-5) at higher temperatures (e.g., 450 and 500 °C). This is because a finer SiCp size would lead to stronger dislocation pinning and grain refinement strengthening effects, and such effects would be weakened at higher temperatures. Further, dynamic softening mechanisms were found to transform from dynamic recovery to dynamic recrystallization with increasing SiCp size, and the dynamic recrystallization occurred more easily at higher temperatures and lower strain rates. Consequently, the instability zones of the composites are all mainly located in the deformation region with lower temperature and higher strain rate, and smaller SiCp results in larger instability zones.
Association of Oral and General Health with Nutritional Status of Older Adults Attending Two Medical Centers in Riyadh, Saudi Arabia: A Cross-Sectional Study

Malnutrition could profoundly affect older adults’ oral health and quality of life, whereas oral health might, in turn, impact dietary intake and nutritional status. The present study aimed to investigate the association between general and oral health and nutritional status among older adults attending nutrition clinics at two main medical centers in Riyadh, Saudi Arabia. A cross-section study was carried out among adult patients (≥60 years) who attended a geriatric clinic or nutrition clinic at King Khalid University Hospital or King Abdulaziz Medical City, Riyadh. A validated clinician’s Mini Nutritional Assessment Short-Form (MNA-SF), Oral Health Impact Profile-5 (OHIP-5), and 36-Item Short Form Survey (SF-36) were collected from each participant. A total of 261 participants with a mean age of 72.14 (±8.97) years were recruited. Diabetes (71%) and hypertension (80%) were present in the majority of patients. The overall MNA-SF score was (10 ± 3). Based on the categorization of the MNA-SF score, 65.9% were classified as malnourished or at risk of malnutrition. Participants with OHIP-5 scores higher than the median (>5) were more likely to be malnourished than those with scores at or lower than 5 (p < 0). The adjusted odd ratio for the MNA-SF score categories indicated that for a one-unit increase in the total SF-36 score, the odds of the malnourished category are 0.94 times less than the risk of malnutrition and normal nutritional status, with OR 0.97 (95% CI 0.94–0.95). Malnutrition or being at risk of malnutrition is likely associated with poor general and oral health. Healthcare providers need to incorporate dietitians into care plans to promote the nutritional health of older adults.
A novel RF energy harvesting cube based on air dielectric antenna arrays

In this article, a new 2 × 2 circular microstrip antenna array with air dielectric layer for ambient RF energy harvesting has been proposed. Two pairs of arc‐shaped slots located close to the boundary of the circular microstrip patch have been designed for achieving dual‐band response and extending the frequency bandwidth. The antenna has a frequency bandwidth from 1.85 to 1.93 GHz and from 2.0 to 2.1 GHz which can cover GSM‐1800 and UMTS‐2100 bands. At the frequency of 1.89 and 2.05 GHz, the measured gain is 5.3 and 6.6 dBi, respectively, and high gain of 3.8‐9.3 dBi has been achieved over the whole band. Also, a broadband rectifier that can cover all the bandwidths of the antenna array is designed for the rectenna, which has the maximum rectifying efficiency of 53.6%. Finally, a cube device formed of four antenna and four rectifiers is designed to harvest RF energy, whose maximum output DC voltage is 2.3 V and the maximum output power is 4 mW that can drive four LEDs and an electronic watch.
The Gender of Retirement in a Double-Ageing Country: Perspectives and Experiences of Retired Women and Men in Portugal

This article aimed to explore the perspectives and experiences of women and men at the stage in their lives following professional retirement, enriching the present and future of a continuously ageing Portuguese society. In order to better capture the diversity and complexity of each individual’s experiences, a qualitative methodology was used. Semi-structured individual interviews were carried out with sixteen retired people, eight women and eight men, aged between 59 and 88 years old. A thematic analysis allowed us to identify five themes in the interviewees’ discourses. We concluded that gender may be a source of heterogeneity at this stage of life, suggesting that these findings should be analysed in the scope of a life course perspective, and highlighting the impact that the different trajectories of women and men have, as well as the historical and social context in which they take place.
Bacterial Membrane Vesicles for In Vitro Catalysis

The use of biological systems in manufacturing and medical applications has seen a dramatic rise in recent years as scientists and engineers have gained a greater understanding of both the strengths and limitations of biological systems. Biomanufacturing, or the use of biology for the production of biomolecules, chemical precursors, and others, is one particular area on the rise as enzymatic systems have been shown to be highly advantageous in limiting the need for harsh chemical processes and the formation of toxic products. Unfortunately, biological production of some products can be limited due to their toxic nature or reduced reaction efficiency due to competing metabolic pathways. In nature, microbes often secrete enzymes directly into the environment or encapsulate them within membrane vesicles to allow catalysis to occur outside the cell for the purpose of environmental conditioning, nutrient acquisition, or community interactions. Of particular interest to biotechnology applications, researchers have shown that membrane vesicle encapsulation often confers improved stability, solvent tolerance, and other benefits that are highly conducive to industrial manufacturing practices. While still an emerging field, this review will provide an introduction to biocatalysis and bacterial membrane vesicles, highlight the use of vesicles in catalytic processes in nature, describe successes of engineering vesicle/enzyme systems for biocatalysis, and end with a perspective on future directions, using selected examples to illustrate these systems’ potential as an enabling tool for biotechnology and biomanufacturing.
Nanosensor of gallium nitride for methanol adsorption for producing hydrogen: A computational chemistry study

The selective hydrogen production from methanol on the graphitic-like gallium nitride (GaN) and carbon doped gallium nitride (C–GaN) nanosheets has been challenged using the density functional theory (DFT) method. In this work, we report that GaN and C-doped GaN can catalyze the direct producing hydrogen (H2) of methanol (CH3OH) through Langmuir adsorption. The changes of charge density have shown a more important charge transfer for C-doped GaN compared to GaN which act both as the electron acceptor while CH3OH molecules in water act as the stronger electron donors through adsorption on the GaN and C-doped GaN surfaces. The adsorption of CH3OH molecules on the GaN and C-doped GaN surfaces represented spin polarization in the GaN and C-doped GaN which can be employed as thee magnetic sensors for running the reaction of H2 producing. The partial electron density states based on “PDOS” graphs have explained that the CH3OH states in both of GaN and C-doped GaN nanosheets, respectively, have more conduction bands between −5 eV to −10 eV. The simulated distribution functions of CH3O@GaN and CH3O@C–GaN complexes exhibits that the bond lengths of O–Ga in CH3O–GaN complex is 1.99 Å and O–C in CH3O–C–GaN complex is 1.43 Å. Besides, the plot for electric potential versus atomic charge has been shown around carbon doping of the GaN which presents the electron accepting characteristics of this element via the electron donor of oxygen atom of hydroxyl group in CH3OH with linear relation coefficient of R2 = 0.9948. GaN and C–GaN nanosheets seem to have enough efficiency for adsorption CH3OH molecules through charge transfer from oxygen to the gallium and carbon elements due to intra-atomic and interatomic interactions.
A spatially specified systems pharmacology therapy for axonal recovery after injury

There are no known drugs or drug combinations that promote substantial central nervous system axonal regeneration after injury. We used systems pharmacology approaches to model pathways underlying axonal growth and identify a four-drug combination that regulates multiple subcellular processes in the cell body and axons using the optic nerve crush model in rats. We intravitreally injected agonists HU-210 (cannabinoid receptor-1) and IL-6 (interleukin 6 receptor) to stimulate retinal ganglion cells for axonal growth. We applied, in gel foam at the site of nerve injury, Taxol to stabilize growing microtubules, and activated protein C to clear the debris field since computational models predicted that this drug combination regulating two subcellular processes at the growth cone produces synergistic growth. Physiologically, drug treatment restored or preserved pattern electroretinograms and some of the animals had detectable visual evoked potentials in the brain and behavioral optokinetic responses. Morphology experiments show that the four-drug combination protects axons or promotes axonal regrowth to the optic chiasm and beyond. We conclude that spatially targeted drug treatment is therapeutically relevant and can restore limited functional recovery.
A Multiscale Point-Supervised Network for Counting Maize Tassels in the Wild

Accurate counting of maize tassels is essential for monitoring crop growth and estimating crop yield. Recently, deep-learning-based object detection methods have been used for this purpose, where plant counts are estimated from the number of bounding boxes detected. However, these methods suffer from 2 issues: (a) The scales of maize tassels vary because of image capture from varying distances and crop growth stage; and (b) tassel areas tend to be affected by occlusions or complex backgrounds, making the detection inefficient. In this paper, we propose a multiscale lite attention enhancement network (MLAENet) that uses only point-level annotations (i.e., objects labeled with points) to count maize tassels in the wild. Specifically, the proposed method includes a new multicolumn lite feature extraction module that generates a scale-dependent density map by exploiting multiple dilated convolutions with different rates, capturing rich contextual information at different scales more effectively. In addition, a multifeature enhancement module that integrates an attention strategy is proposed to enable the model to distinguish between tassel areas and their complex backgrounds. Finally, a new up-sampling module, UP-Block, is designed to improve the quality of the estimated density map by automatically suppressing the gridding effect during the up-sampling process. Extensive experiments on 2 publicly available tassel-counting datasets, maize tassels counting and maize tassels counting from unmanned aerial vehicle, demonstrate that the proposed MLAENet achieves marked advantages in counting accuracy and inference speed compared to state-of-the-art methods. The model is publicly available at https://github.com/ShiratsuyuShigure/MLAENet-pytorch/tree/main.
Language Models with Transformers

The Transformer architecture is superior to RNN-based models in computational efficiency. Recently, GPT and BERT demonstrate the efficacy of Transformer models on various NLP tasks using pre-trained language models on large-scale corpora. Surprisingly, these Transformer architectures are suboptimal for language model itself. Neither self-attention nor the positional encoding in the Transformer is able to efficiently incorporate the word-level sequential context crucial to language modeling. In this paper, we explore effective Transformer architectures for language model, including adding additional LSTM layers to better capture the sequential context while still keeping the computation efficient. We propose Coordinate Architecture Search (CAS) to find an effective architecture through iterative refinement of the model. Experimental results on the PTB, WikiText-2, and WikiText-103 show that CAS achieves perplexities between 20.42 and 34.11 on all problems, i.e. on average an improvement of 12.0 perplexity units compared to state-of-the-art LSTMs. The source code is publicly available.
Language Features of Construction Contracts From the Lens of Genre Analysis

With the increase of overseas construction projects, there is great demand for a comprehensive understanding of construction contracts (CC) from the language perspective. But such perspectives are understudied when exploring CC. This study aims to tap language features from the lenses of genre analysis (GA), which is conducive to a better understanding of CC. In this study, a corpus-driven exploration of CC was carried out, in which striking language features were revealed in a visualized manner by adopting the Multidimensional Analysis Tagger (MAT). The study found that nouns and normalization were dense in CC, with attributive adjectives and total prepositional phrases frequently used. When it comes to the overall generic features of the CC used in this corpus, it was found that CC belong to the written genre with the informational, non-narrative, context-independent, persuasive, and abstract generic feature. Those features further make CC formal in style, explicit in communicative purposes, objective in stance, and authoritative in position. The language features in question are not out of thin air but the results of the evolution of professional practices (PP) and professional cultures (PC). Limited as the language features have been discussed, insight can still be obtained, which is conducive to further studies
Exploring Pt-Pd Alloy Nanoparticle Cluster Formation through Conventional Sizing Techniques and Single-Particle Inductively Coupled Plasma—Sector Field Mass Spectrometry

Accurate characterization of Pt-Pd alloy nanoparticle clusters (NCs) is crucial for understanding their synthesis using Gas-Diffusion Electrocrystallization (GDEx). In this study, we propose a comprehensive approach that integrates conventional sizing techniques—scanning electron microscopy (SEM) and dynamic light scattering (DLS)—with innovative single-particle inductively coupled plasma—sector field mass spectrometry (spICP-SFMS) to investigate Pt-Pd alloy NC formation. SEM and DLS provide insights into morphology and hydrodynamic sizes, while spICP-SFMS elucidates the particle size and distribution of Pt-Pd alloy NCs, offering rapid and orthogonal characterization. The spICP-SFMS approach presented enables detailed characterization of Pt-Pd alloy NCs, which was previously challenging due to the absence of multi-element capabilities in conventional spICP-MS systems. This innovative approach not only enhances our understanding of bimetallic nanoparticle synthesis, but also paves the way for tailoring these materials for specific applications, marking a significant advancement in the field of nanomaterial science.
Gender and Race-Based Differences in Negotiating Behavior among Tenured and Tenure-Track Faculty at Four-Year Institutions

Abstract:Inequities in faculty representation and support have long been documented, though a potentially key contributor to these inequities—negotiation behavior—remains underexplored. We leverage descriptive analyses and hierarchical linear modeling with a sample of over 30,000 faculty to explore gender- and race-based differences across negotiating behavior and institutional responses. Results suggest significant differences in the likelihood of seeking and receiving external offers and negotiating employment. Men of color are less likely to engage in many behaviors, though women of color may be less successful in receiving changes to their employment context. Results have implications for faculty, institutional policy, and future research.
On the multiple holomorph of a finite almost simple group

Let $G$ be a group. Let $\mathrm{Perm}(G)$ denote its symmetric group and write $\mathrm{Hol}(G)$ for the normalizer of the subgroup of left translations in $\mathrm{Perm}(G)$. The multiple holomorph $\mathrm{NHol}(G)$ of $G$ is in turn defined to be the normalizer of $\mathrm{Hol}(G)$ in $\mathrm{Perm}(G)$. In this paper, we shall show that the quotient group $\mathrm{NHol}(G)/\mathrm{Hol}(G)$ has order two when $G$ is finite and almost simple. As an application of our techniques, we shall also develop a method to count the number of Hopf-Galois structures of isomorphic type on a finite almost simple extension in terms of fixed point free endomorphisms.
Insulator Abnormal Condition Detection from Small Data Samples

Insulators are an important part of transmission lines in active distribution networks, and their performance has an impact on the power system’s normal operation, security, and dependability. Traditional insulator detection methods, on the other hand, necessitate a significant amount of labor and material resources, necessitating the development of a new detection method to substitute manpower. This paper investigates the abnormal condition detection of insulators based on UAV vision sensors using artificial intelligence algorithms from small samples. Firstly, artificial intelligence for the image data volume requirements was large, i.e., the insulator image samples taken by the UAV vision sensor inspection were not enough, or there was a missing image problem, so the data enhancement method was used to expand the small sample data. Then, the YOLOV5 algorithm was used to compare detection results before and after the extended dataset’s optimization to demonstrate the expanded dataset’s dependability and universality, and the results revealed that the expanded dataset improved detection accuracy and precision. The insulator abnormal condition detection method based on small sample image data acquired by the visual sensors studied in this paper has certain theoretical guiding significance and engineering application prospects for the safe operation of active distribution networks.
Acceleration of the Power Method with Dynamic Mode Decomposition

Abstract An algorithm based on dynamic mode decomposition (DMD) for acceleration of the power method (PM) is presented. The PM is a simple technique for determining the dominant eigenmode of an operator A, and variants of the PM are widely used in reactor analysis. DMD is an algorithm for decomposing a time series of spatially dependent data and producing an explicit-in-time reconstruction for that data. By viewing successive PM iterates as snapshots of a time-varying system tending toward a steady state, DMD can be used to predict that steady state using (sometimes surprisingly small) iterates. The process of generating snapshots with the PM and extrapolating forward with DMD can be repeated. The resulting restarted, DMD-accelerated PM [or DMD-PM()] was applied to the two-dimensional International Atomic Energy Agency diffusion benchmark and compared to the unaccelerated PM and the Arnoldi method. Results indicate that DMD-PM() can reduce the number of power iterations required by a factor of approximately 5. However, the Arnoldi method always outperformed DMD-PM() for an equivalent number of matrix-vector products Av. In other words, DMD-PM() cannot compete with leading eigensolvers if one is not limited to snapshots produced by the PM. Contrarily, DMD-PM() can be readily applied as a postprocess to existing PM applications for which the Arnoldi method and similar methods are not directly applicable. A slight variation of the method was also found to produce reasonable approximations to the first and second harmonics without substantially affecting convergence of the dominant mode.