Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultra-Efficient On-Device Object Detection on AI-Integrated Smart Glasses with TinyissimoYOLO

Smart glasses are rapidly gaining advanced functionality thanks to cutting-edge computing technologies, accelerated hardware architectures, and tiny AI algorithms. Integrating AI into smart glasses featuring a small form factor and limited battery capacity is still challenging when targeting full-day usage for a satisfactory user experience. This paper illustrates the design and implementation of tiny machine-learning algorithms exploiting novel low-power processors to enable prolonged continuous operation in smart glasses. We explore the energy- and latency-efficient of smart glasses in the case of real-time object detection. To this goal, we designed a smart glasses prototype as a research platform featuring two microcontrollers, including a novel milliwatt-power RISC-V parallel processor with a hardware accelerator for visual AI, and a Bluetooth low-power module for communication. The smart glasses integrate power cycling mechanisms, including image and audio sensing interfaces. Furthermore, we developed a family of novel tiny deep-learning models based on YOLO with sub-million parameters customized for microcontroller-based inference dubbed TinyissimoYOLO v1.3, v5, and v8, aiming at benchmarking object detection with smart glasses for energy and latency. Evaluations on the prototype of the smart glasses demonstrate TinyissimoYOLO's 17ms inference latency and 1.59mJ energy consumption per inference while ensuring acceptable detection accuracy. Further evaluation reveals an end-to-end latency from image capturing to the algorithm's prediction of 56ms or equivalently 18 fps, with a total power consumption of 62.9mW, equivalent to a 9.3 hours of continuous run time on a 154mAh battery. These results outperform MCUNet (TinyNAS+TinyEngine), which runs a simpler task (image classification) at just 7.3 fps per second.
Molecular dynamics simulation of heating and collapse of methane hydrate by TIP5P-Ewald water model

Microwave heating of methane hydrate is studied with electrostatic molecular dynamics simulations by the five-body Coulomb and TIP5P-Ewald water model. The structure I of methane hydrate is constructed. When the methane hydrate of density 0.91 $\rm{g/cm}^3$ and a temperature 273 K is exposed to microwave electric fields of 10 GHz, it suddenly collapses to become liquid after a certain period of irradiation. The period of a collapse time is $1.7 \times 10^{6} \tau $ and the temperature increase is $\Delta T \cong 61$ degrees, where the external electric field is $3 \times 10^{7} \rm{V/cm}$ (i.e. 0.3 V/\AA) and $\tau = 1 \times 10^{-14}$ s.
Genetic Algorithm-based Beamforming in Subarray Architectures for GEO Satellites

The incorporation of subarrays in Direct Radiating Arrays for satellite missions is fundamental in reducing the number of Radio Frequency chains, which correspondingly diminishes cost, power consumption, space, and mass. Despite the advantages, previous beamforming schemes incur significant losses during beam scanning, particularly when hybrid beamforming is not employed. Consequently, this paper introduces an algorithm capable of compensating for these losses by increasing the power, for this, the algorithm will activate radiating elements required to address a specific Effective Isotropic Radiated Power for a beam pattern over Earth, projected from a GeoStationary satellite. In addition to the aforementioned compensation, other beam parameters have been addressed in the algorithm, such as beamwidth and Side Lobe Levels. To achieve these objectives, we propose employing the array thinning concept through the use of genetic algorithms, which enable beam shaping with the desired characteristics and power. The full array design considers an open-ended waveguide, configured to operate in circular polarization within the Ka-band frequency range of 17.7-20.2 GHz.
The Age of Generative AI and AI-Generated Everything

Generative AI (GAI) has emerged as a significant advancement in artificial intelligence, renowned for its language and image generation capabilities. This paper presents ``AI-Generated Everything'' (AIGX), a concept that extends GAI beyond mere content creation to real-time adaptation and control across diverse technological domains. In networking, AIGX collaborates closely with physical, data link, network, and application layers to enhance real-time network management that responds to various system and service settings as well as application and user requirements. Networks, in return, serve as crucial components in further AIGX capability optimization through the AIGX lifecycle, i.e., data collection, distributed pre-training, and rapid decision-making, thereby establishing a mutually enhancing interplay. Moreover, we offer an in-depth case study focused on power allocation to illustrate the interdependence between AIGX and networking systems. Through this exploration, the article analyzes the significant role of GAI for networking, clarifies the ways networks augment AIGX functionalities, and underscores the virtuous interactive cycle they form. This article paves the way for subsequent future research aimed at fully unlocking the potential of GAI and networks.
Overhead Line Defect Recognition Based on Unsupervised Semantic Segmentation

Overhead line inspection greatly benefits from defect recognition using visible light imagery. Addressing the limitations of existing feature extraction techniques and the heavy data dependency of deep learning approaches, this paper introduces a novel defect recognition framework. This is built on the Faster RCNN network and complemented by unsupervised semantic segmentation. The approach involves identifying the type and location of the target equipment, utilizing semantic segmentation to differentiate between the device and its backdrop, and finally employing similarity measures and logical rules to categorize the type of defect. Experimental results indicate that this methodology focuses more on the equipment rather than the defects when identifying issues in overhead lines. This leads to a notable enhancement in accuracy and exhibits impressive adaptability. Thus, offering a fresh perspective for automating the inspection of distribution network equipment.
Revamping AI Models in Dermatology: Overcoming Critical Challenges for Enhanced Skin Lesion Diagnosis

The surge in developing deep learning models for diagnosing skin lesions through image analysis is notable, yet their clinical black faces challenges. Current dermatology AI models have limitations: limited number of possible diagnostic outputs, lack of real-world testing on uncommon skin lesions, inability to detect out-of-distribution images, and over-reliance on dermoscopic images. To address these, we present an All-In-One \textbf{H}ierarchical-\textbf{O}ut of Distribution-\textbf{C}linical Triage (HOT) model. For a clinical image, our model generates three outputs: a hierarchical prediction, an alert for out-of-distribution images, and a recommendation for dermoscopy if clinical image alone is insufficient for diagnosis. When the recommendation is pursued, it integrates both clinical and dermoscopic images to deliver final diagnosis. Extensive experiments on a representative cutaneous lesion dataset demonstrate the effectiveness and synergy of each component within our framework. Our versatile model provides valuable decision support for lesion diagnosis and sets a promising precedent for medical AI applications.
The origin and significance of the distribution of shelly macrofauna in late Dinantian carbonate mud mounds of Derbyshire

SUMMARY The majority of macrofauna found in carbonate mud mounds of the late Brigantian Eyam Limestones Formation is preserved in isolated pockets. These pockets represent erosional hollows present during growth on the surface of the carbonate mud mound which were colonised by brachiopods and bivalves in preference to other areas. Reworking caused disarticulation and stacking of shells and winnowing of small shells and the carbonate mud. The macrofauna preserved in each pocket probably also includes some shells which were washed in. The substrate relationships of the macrofauna and the diagenetic fabrics within the core facies show that the surface of the core facies within these pockets was unlithified but consolidated. The presence of steep-sided erosional hollows on the surface of the core facies is interpreted as circumstantial evidence for the former presence of a sediment-binding community which may have contributed to carbonate mud production and stabilisation of the carbonate mud mound.
A Constant Factor Approximation for Directed Feedback Vertex Set in Graphs of Bounded Genus

The minimum directed feedback vertex set problem consists in finding the minimum set of vertices that should be removed in order to make a directed graph acyclic. This is a well-known NP-hard optimization problem with applications in various fields, such as VLSI chip design, bioinformatics and transaction processing deadlock prevention and node-weighted network design. We show a constant factor approximation for the directed feedback vertex set problem in graphs of bounded genus.
H-NeXt: The next step towards roto-translation invariant networks

The widespread popularity of equivariant networks underscores the significance of parameter efficient models and effective use of training data. At a time when robustness to unseen deformations is becoming increasingly important, we present H-NeXt, which bridges the gap between equivariance and invariance. H-NeXt is a parameter-efficient roto-translation invariant network that is trained without a single augmented image in the training set. Our network comprises three components: an equivariant backbone for learning roto-translation independent features, an invariant pooling layer for discarding roto-translation information, and a classification layer. H-NeXt outperforms the state of the art in classification on unaugmented training sets and augmented test sets of MNIST and CIFAR-10.
Millimeter-scale exfoliation of hBN with tunable flake thickness

As a two-dimensional (2D) dielectric material, hexagonal boron nitride (hBN) is in high demand for applications in photonics, nonlinear optics, and nanoelectronics. Unfortunately, the high-throughput preparation of macroscopic-scale, high-quality hBN flakes with controlled thickness is an ongoing challenge, limiting device fabrication and technological integration. Here, we present a metal thin-film exfoliation method to prepare hBN flakes with millimeter-scale dimension, near-unity yields, and tunable flake thickness distribution from 1-7 layers, a substantial improvement over scotch tape exfoliation. The single crystallinity and high quality of the exfoliated hBN are demonstrated with optical microscopy, atomic force microscopy, Raman spectroscopy, and second harmonic generation. We further explore a possible mechanism for the effectiveness and selectivity based on thin-film residual stress measurements, density functional theory calculations, and transmission electron microscopy imaging of the deposited metal films. We find that the magnitude of the residual tensile stress induced by thin film deposition plays a key role in determining exfoliated flake thickness in a manner which closely resembles 3D semiconductor spalling. Lastly, we demonstrate that our exfoliated, large-area hBN flakes can be readily incorporated as encapsulating layers for other 2D monolayers. Altogether, this method brings us one step closer to the high throughput, mass production of hBN-based 2D photonic, optoelectronic, and quantum devices.
Li-Yau Inequality and Liouville Property to a Semilinear Heat Equation on Riemannian Manifolds

This work deals with the Entire solutions of a nonlinear equation. The first part of this paper is devoted to investigation of the Liouville property on compact manifolds, which extends a result by Castorina-Mantegazza [4] for positive f. Secondly, we will turn to non-compact manifolds and prove a Liouville theorem under the assumptions of boundedness of the Ricci curvature from below, diffeomorphism of M with R^N and sub-criticality of p defined below. Finally, we also present simplified proofs of Yau's theorem for harmonic function and Gidas-Spruck's theorem for elliptic semilinear equation. Our proofs are based on Li-Yau type estimation for nonlinear equations.
Novel View Synthesis from a Single RGBD Image for Indoor Scenes

In this paper, we propose an approach for synthesizing novel view images from a single RGBD (Red Green Blue-Depth) input. Novel view synthesis (NVS) is an interesting computer vision task with extensive applications. Methods using multiple images has been well-studied, exemplary ones include training scene-specific Neural Radiance Fields (NeRF), or leveraging multi-view stereo (MVS) and 3D rendering pipelines. However, both are either computationally intensive or non-generalizable across different scenes, limiting their practical value. Conversely, the depth information embedded in RGBD images unlocks 3D potential from a singular view, simplifying NVS. The widespread availability of compact, affordable stereo cameras, and even LiDARs in contemporary devices like smartphones, makes capturing RGBD images more accessible than ever. In our method, we convert an RGBD image into a point cloud and render it from a different viewpoint, then formulate the NVS task into an image translation problem. We leveraged generative adversarial networks to style-transfer the rendered image, achieving a result similar to a photograph taken from the new perspective. We explore both unsupervised learning using CycleGAN and supervised learning with Pix2Pix, and demonstrate the qualitative results. Our method circumvents the limitations of traditional multi-image techniques, holding significant promise for practical, real-time applications in NVS.
Effects of tillage on processes of organic matter sequestration

Deutsche Forschungsgemeinschaft, DFG-Graduiertenkolleg 1397 "Steuerung des Humus- und Nahstoffhaushalts in der Okologischen Landwirtschaft"; Research Training Group 1397 "Regulation of soil organic matter and nutrient turnover in organic agriculture"
Multimodal fusion of brain signals for robust prediction of psychosis transition

Psychosis symptoms are often evident before diagnosis, suggesting the underlying biology of high-risk status may predict later disease outcomes. However, a single predictor remains unknown, indicating a need for algorithms that integrate complex information. Here, to identify risk and psychosis conversion, we implemented multiple kernel learning (MKL), a multimodal machine learning approach allowing patterns from each modality to inform each other. Baseline multimodal scans (n=74, 11 converters) included structural, resting-state functional imaging, and diffusion-weighted data. Multimodal MKL outperformed unimodal models (AUC=0.73 vs. 0.66 in predicting conversion). Moreover, patterns learned by MKL were robust to training set variations, suggesting it can identify cross-modality redundancies and synergies to stabilize the predictive pattern. We identified many predictors consistent with the literature, including frontal cortices, cingulate, thalamus, and striatum. This highlights the advantage of methods that leverage the complex pathophysiology of psychosis.
Molecular mechanism of NAD+ binding to the Nudix homology domains of DBC1

DBC1 (deleted in breast cancer 1) is a human nuclear protein that modulates the activities of various proteins. NAD+ (oxidized form of nicotinamide adenine dinucleotide) is thought to potentially bind to the Nudix homology domains (NHDs) of DBC1, thereby regulating DBC1-PARP1 [poly (adenosine diphosphate–ribose) polymerase] interactions, the modulation of which may restore DNA repair to protect against cancer, radiation, and aging. Therefore, our study comprehensively employed methods including NMR (Nuclear Magnetic Resonance), ITC (isothermal titration calorimetry), genetic mutation, and computer biology to thoroughly investigate the molecular mechanism of the binding interaction between NAD+ and its precursor NMN with the NHD domain of DBC1 (DBC1354-396). The results from NMR and ITC indicate that NAD+ likely interacts with DBC1354-396 through hydrogen bonding, with a binding affinity nearly twice that of NMN. The key binding sites are primarily E363 and D372. Molecular Docking further revealed the importance of conventional hydrogen bonds and carbon-hydrogen bonds in the binding process. These findings may lead to a better understanding of how NAD+ regulates the physiological functions of DBC1, thereby offering guiding principles for the development of targeted therapies and drug research focused on tumor diseases associated with DBC1.
Dealing with lipid effects and lipid-extraction biases in δ13C and δ15N isotopic studies: a solution based on 28 marine invertebrate, fish and mammal species

Lipids are naturally depleted in 13C isotope in relation to its C sources, causing a bias in δ13C in bulk samples that varies with lipid content. Failure to take this issue into account results in inaccurate conclusions in food web and habitat use studies. Two approaches to resolve this issue are 1) to extract lipids from samples prior to measurement, a resource-intensive process that also can alter δ15N or 2) estimating a lipid-free δ13C using one of several equations that differ in levels of sophistication and generalization across taxa. Here δ13C and δ15N were measured on bulk and lipid-extracted muscle samples of a dataset of over 2000 specimens of 28 species of marine invertebrates, fishes and mammals. Our objectives were to 1) compare the effect of lipid extraction on δ13C and δ15N across taxa; 2) compare the performance of five normalization models, overall and on subsets of species; 3) propose a model to revert lipid-extracted δ15N back to their bulk values; and 4) identify the most suitable approach for dealing with lipid biases in isotopic ratios. Extraction caused an uneven enrichment in δ13C and δ15N across species. Model taxonomic specificity increased estimate accuracy in both isotopes. Models from Logan et al. (2008) and McConnaughey and McRoy (1979) performed better than the other models tested. δ15Nbulk could be reliably estimated based on δ15Nlipid-extracted using a linear model. This study provides a way forward for obtaining reliable δ13C and δ15N values in muscle tissue without the costs of duplicate analyses and represents a major step toward the harmonization of datasets collected under the two different approaches.
Insecticidal Effects of Datura Species against Major Agricultural Pests

Insecticides derived from plants provide environmentally friendly and sustainable options as alternatives to synthetic chemicals. This investigation delves into the insecticidal capabilities of two distinct Datura species, namely, Datura alba and Datura stramonium, in combatting significant pests such as Spodoptera litura, Bemesia tabaci, and Callosobruchus maculatus. Datura plants have a well-documented history of medicinal use and are recognized for their abundant Tropane alkaloid content, which acts as an innate defense mechanism against pests. Hence, the current investigation involved the extraction of chemical compounds from the leaves, flowers, seeds, and roots of both Datura stramonium and Datura alba. Subsequently, these extracts were assessed for efficacy using Spodoptera litura larvae, Bemesia tabaci, and Callosobruchus maculatus in bioassays. The larvicidal experiments encompassed a range of concentrations (3%, 5%, 10%), with larval mortality being documented at multiple time intervals: 6, 12, 24, 48, and 72 hours. The findings from our investigation uncover the notable insecticidal attributes of root, leaf, flower, and seed extracts from both Datura alba and Datura stramonium against these larvae. These extracts demonstrated distinct mortality rates at various concentrations and exposure durations. This comprehensive analysis imparts crucial knowledge regarding the insecticidal qualities of Datura species and their prospective contributions to sustainable pest control practices. The observed effectiveness of these plant extracts against a spectrum of significant pests hints at their potential as ecological alternatives within integrated pest management strategies.
Uncultivated DPANN archaea are ubiquitous inhabitants of global oxygen deficient zones with diverse metabolic potential

Archaea belonging to the DPANN superphylum have been found within an expanding number of environments and perform a variety of biogeochemical roles, including contributing to carbon, sulfur, and nitrogen cycling. Generally characterized by ultrasmall cell sizes and reduced genomes, DPANN archaea may form mutualistic, commensal, or parasitic interactions with various archaeal and bacterial hosts, influencing the ecology and functioning of microbial communities. While DPANN archaea reportedly comprise 15–26% of the archaeal community within marine oxygen deficient zone (ODZ) water columns, little is known about their metabolic capabilities in these ecosystems. We report 33 novel metagenome-assembled genomes belonging to DPANN phyla Nanoarchaeota, Pacearchaeota, Woesarchaeota, Undinarchaeota, Iainarchaeota, and SpSt-1190 from pelagic ODZs in the Eastern Tropical North Pacific and Arabian Sea. We find these archaea to be permanent, stable residents of all 3 major ODZs only within anoxic depths, comprising up to 1% of the total microbial community and up to 25–50% of archaea. ODZ DPANN appear capable of diverse metabolic functions, including fermentation, organic carbon scavenging, and the cycling of sulfur, hydrogen, and methane. Within a majority of ODZ DPANN, we identify a gene homologous to nitrous oxide reductase. Modeling analyses indicate the feasibility of a nitrous oxide reduction metabolism for host-attached symbionts, and the small genome sizes and reduced metabolic capabilities of most DPANN MAGs suggest host-associated lifestyles within ODZs. Importance Archaea from the DPANN superphylum have diverse metabolic capabilities and participate in multiple biogeochemical cycles. While metagenomics and enrichments have revealed that many DPANN are characterized by ultrasmall genomes, few biosynthetic genes, and episymbiotic lifestyles, much remains unknown about their biology. We report 33 new DPANN metagenome-assembled genomes originating from the 3 global marine oxygen deficient zones (ODZs), the first from these regions. We survey DPANN abundance and distribution within the ODZ water column, investigate their biosynthetic capabilities, and report potential roles in the cycling of organic carbon, methane, and nitrogen. We test the hypothesis that nitrous oxide reductases found within several ODZ DPANN genomes may enable ultrasmall episymbionts to serve as nitrous oxide consumers when attached to a host nitrous oxide producer. Our results indicate DPANN archaea as ubiquitous residents within the anoxic core of ODZs with the potential to produce or consume key compounds.