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Many forms of inductive logic programming (ILP) use metarules, second-order Horn clauses, to define the structure of learnable programs and thus the hypothesis space. Deciding which metarules to use for a given learning task is a major open problem and is a trade-off between efficiency and expressivity: the hypothesis space grows given more metarules, so we wish to use fewer metarules, but if we use too few metarules then we lose expressivity. In this paper, we study whether fragments of metarules can be logically reduced to minimal finite subsets. We consider two traditional forms of logical reduction: subsumption and entailment. We also consider a new reduction technique called derivation reduction, which is based on SLD-resolution. We compute reduced sets of metarules for fragments relevant to ILP and theoretically show whether these reduced sets are reductions for more general infinite fragments. We experimentally compare learning with reduced sets of metarules on three domains: Michalski trains, string transformations, and game rules. In general, derivation reduced sets of metarules outperform subsumption and entailment reduced sets, both in terms of predictive accuracies and learning times.

Modern practice for training classification deepnets involves a terminal phase of training (TPT), which begins at the epoch where training error first vanishes. During TPT, the training error stays effectively zero, while training loss is pushed toward zero. Direct measurements of TPT, for three prototypical deepnet architectures and across seven canonical classification datasets, expose a pervasive inductive bias we call neural collapse (NC), involving four deeply interconnected phenomena. (NC1) Cross-example within-class variability of last-layer training activations collapses to zero, as the individual activations themselves collapse to their class means. (NC2) The class means collapse to the vertices of a simplex equiangular tight frame (ETF). (NC3) Up to rescaling, the last-layer classifiers collapse to the class means or in other words, to the simplex ETF (i.e., to a self-dual configuration). (NC4) For a given activation, the classifier’s decision collapses to simply choosing whichever class has the closest train class mean (i.e., the nearest class center [NCC] decision rule). The symmetric and very simple geometry induced by the TPT confers important benefits, including better generalization performance, better robustness, and better interpretability.

During the 1980s Michie defined Machine Learning in terms of two orthogonal axes of performance: predictive accuracy and comprehensibility of generated hypotheses. Since predictive accuracy was readily measurable and comprehensibility not so, later definitions in the 1990s, such as Mitchell’s, tended to use a one-dimensional approach to Machine Learning based solely on predictive accuracy, ultimately favouring statistical over symbolic Machine Learning approaches. In this paper we provide a definition of comprehensibility of hypotheses which can be estimated using human participant trials. We present two sets of experiments testing human comprehensibility of logic programs. In the first experiment we test human comprehensibility with and without predicate invention. Results indicate comprehensibility is affected not only by the complexity of the presented program but also by the existence of anonymous predicate symbols. In the second experiment we directly test whether any state-of-the-art ILP systems are ultra-strong learners in Michie’s sense, and select the Metagol system for use in humans trials. Results show participants were not able to learn the relational concept on their own from a set of examples but they were able to apply the relational definition provided by the ILP system correctly. This implies the existence of a class of relational concepts which are hard to acquire for humans, though easy to understand given an abstract explanation. We believe improved understanding of this class could have potential relevance to contexts involving human learning, teaching and verbal interaction.

Wireless electricity is a method of sending electrical energy to a load without a conductor. Some methods use inductive, inductive resonant and capacitive methods, as well as radio frequency. There have been many implementations of wireless power transfers (WPTs), but limited in testing. Therefore, both concerns should be investigated. This research implementation of an inductive resonance wireless power transfer circuit consisted of an oscillator, solar panel source, inductive-capacitive (L-C) circuit, copper pipes of 20 cm, 22 cm, 24 cm, and 30 cm diameters for transmitting and receiving media and light-emitting diodes (LEDs) as burdens. While, the testing consisted of power, efficiency and power factor to straight distances, frequency, power and efficiency to the left shifted and right distances, frequency, power and efficiency to the right rotated angles, and power angle, power and efficiency to various lamp loads. Based on the yielded measurements, the generated frequency range is between 0.8 MHz and 1.1 MHz. The transmitting and receiving frequencies are almost the same for every measurement. The most excellent efficiency was 97 %, generated on the copper pipe of the 24 cm diameter and one cm distance. The electrical transfer power and efficiency decreased inversely and the power factor decreased linearly as the distance increased. The power and efficiency decreased inversely and the frequency increased linearly as the left, and right shifted distance increased. The power and efficiency decreased inversely and the frequency rose and decreased linearly as the left-rotated angle increased. The power decreased slight inversely as the right-rotated angle increased. Generally, the power, power angle and frequency varied slightly as the capacitor capacitance increased. The power angle increased and decreased as the secondary capacitance and LED number increased, depended on the coil pipe diameters. While, the efficiency decreased significantly as the primary capacitance increased. Vice versa, the efficiency slightly increased as the LED number increased too. Thus, the output parameters were influenced both internal and external factors.

Thematic analysis is a highly popular technique among qualitative researchers for analyzing qualitative data, which usually comprises thick descriptive data. However, the application and use of thematic analysis has also involved complications due to confusion regarding the final outcome’s presentation as a conceptual model. This paper develops a systematic thematic analysis process for creating a conceptual model from qualitative research findings. It explores the adaptability of the proposed process across various research methodologies, including constructivist methodologies, positivist methodologies, grounded theory, and interpretive phenomenology, and justifies their application. The paper distinguishes between inductive and deductive coding approaches and emphasizes the merits of each. It suggests that the derived systematic thematic analysis model is valuable across multiple disciplines, particularly in grounded theory, ethnographic approaches, and narrative approaches, while also being adaptable to more descriptive, positivist-based methodologies. By providing a methodological roadmap, this study enhances the rigor and replicability of thematic analysis and offers a comprehensive strategy for theoretical conceptualization in qualitative research. The contribution of this paper is a systematic six-step thematic analysis process that leads to the development of a conceptual model; each step is described in detail and examples are given.

Glucose‐oxidase (GOx)‐mediated starvation by consuming intracellular glucose has aroused extensive exploration as an advanced approach for tumor treatment. However, this reaction of catalytic oxidation by GOx is highly dependent on the on‐site oxygen content, and thus starvation therapy often suffers unexpected anticancer outcomes due to the intrinsic tumorous hypoxia. Herein, porous platinum nanospheres (pPts), incorporated with GOx molecules (PtGs), are synthesized to enable synergistic cancer therapy. In this system, GOx can effectively catalyze the oxidation of glucose to generate H2O2, while pPt triggers the decomposition of both endogenous and exogenous H2O2 to produce considerable content of O2 to facilitate the glucose consumption by GOx. Meanwhile, pPt induces remarkable content of intracellular reactive oxygen species (ROS) under an alternating electric field, leading to cellular oxidative stress injury and promotes apoptosis following the mechanism of electrodynamic therapy (EDT). In consequence, the PtG nanocomposite exhibits significant anticancer effect both in vitro and in vivo. This study has therefore demonstrated a fascinating therapeutic platform enabling oxygen‐inductive starvation/EDT synergistic strategy for effective tumor treatment. Porous platinum nanospheres (pPts) incorporated with glucose oxidase (GOx) molecules (PtGs) are synthesized for synergistic electrodynamic/starvation therapy. Despite its feasible loading for GOx, pPt enables sufficient O2 supply to facilitate GOx‐mediated starvation by decomposing H2O2. Meanwhile, PtG induces reactive oxygen species (ROS) under an electric field following an electrodynamic mechanism. Considerable in vitro and in vivo tumor inhibition is consequently achieved.

Management journals are currently responding to challenges raised by the “replication crisis” in experimental social psychology, leading to new standards for transparency. These approaches are spilling over to qualitative research in unhelpful and potentially even dangerous ways. Advocates for transparency in qualitative research mistakenly couple it with replication. Tying transparency tightly to replication is deeply troublesome for qualitative research, where replication misses the point of what the work seeks to accomplish. We suggest that transparency advocates conflate replication with trustworthiness. We challenge this conflation on both ontological and methodological grounds, and we offer alternatives for how to (and how not to) think about trustworthiness in qualitative research. Management journals need to tackle the core issues raised by this tumult over transparency by identifying solutions for enhanced trustworthiness that recognize the unique strengths and considerations of different methodological approaches in our field.

Almost all of the wear debris generated during the operation of the machine is suspended in the circulating lubricating oil. The analysis of the wear debris in the lubricating oil can effectively monitor the wear state of the machine and provide early warning of failures. An overview on inductive sensors for measuring wear debris in lubrication is introduced. To begin with, the significance of analyzing the wear debris in lubricating oil is explained and the working principle of the inductive wear sensors is illustrated. Furthermore, the development of inductive wear sensors and the key limitations are summarized. Finally, some rest factors affecting the sensor and the processing method of the induction signal aliasing are discussed, and the future development trend is prospected. It is pointed out that developing high sensitivity wear debris inductive sensors, increasing sensor throughput, and solving the problem of aliasing of detection signals are the following issues that should be further studied in the future.

Vision transformers have shown great potential in various computer vision tasks owing to their strong capability to model long-range dependency using the self-attention mechanism. Nevertheless, they treat an image as a 1D sequence of visual tokens, lacking an intrinsic inductive bias (IB) in modeling local visual structures and dealing with scale variance, which is instead learned implicitly from large-scale training data with longer training schedules. In this paper, we leverage the two IBs and propose the ViTAE transformer, which utilizes a reduction cell for multi-scale feature and a normal cell for locality. The two kinds of cells are stacked in both isotropic and multi-stage manners to formulate two families of ViTAE models, i.e., the vanilla ViTAE and ViTAEv2. Experiments on the ImageNet dataset as well as downstream tasks on the MS COCO, ADE20K, and AP10K datasets validate the superiority of our models over the baseline and representative models. Besides, we scale up our ViTAE model to 644 M parameters and obtain the state-of-the-art classification performance, i.e., 88.5% Top-1 classification accuracy on ImageNet validation set and the best 91.2% Top-1 classification accuracy on ImageNet Real validation set, without using extra private data. It demonstrates that the introduced inductive bias still helps when the model size becomes large. The source code and pretrained models are publicly available atcode.

Osteoporosis is an incurable chronic disease characterized by a lack of mineral mass in the bones. Here, the full recovery of osteoporotic bone is achieved by using a calcium phosphate polymer‐induced liquid‐precursor (CaP‐PILP). This free‐flowing CaP‐PILP material displays excellent bone inductivity and is able to readily penetrate into collagen fibrils and form intrafibrillar hydroxyapatite crystals oriented along the c‐axis. This ability is attributed to the microstructure of the material, which consists of homogeneously distributed ultrasmall (≈1 nm) amorphous calcium phosphate clusters. In vitro study shows the strong affinity of CaP‐PILP to osteoporotic bone, which can be uniformly distributed throughout the bone tissue to significantly increase the bone density. In vivo experiments show that the repaired bones exhibit satisfactory mechanical performance comparable with normal ones, following a promising treatment of osteoporosis by using CaP‐PILP. The discovery provides insight into the structure and property of biological nanocluster materials and their potential for hard tissue repair. The fluidity of calcium phosphate polymer‐induced liquid‐precursor (CaP‐PILP) allows the minimally‐invasive injection recovery of osteoporotic bone without the need for surgical incision in clinical applications. CaP‐PILP can recover osteoporotic bone back to normal, with a mechanical performance comparable to that of healthy bone. The unique characteristics of the material enable its application in osteoporotic bone recovery.