Miracle-dz/UniFAR-FTUs huggingface dataset:
PACCDU: Pyramid Attention Cross-Convolutional Dual UNet for Infrared and Visible Image Fusion

Infrared and visible image fusion is an important branch in the field of information fusion, which aims to integrate effective information from different sensors and enhance the integrity of image information. Therefore, how to fully extract and retain the structural information and texture details in the source images is a pressing problem at present. In this article, a dual-branch UNet fusion network based on pyramidal attention and cross-convolution (PACCDU) is proposed to obtain fused images with high contrast, rich information, and clear contours. The network encoder uses cross-encoding blocks and pyramidal attention blocks to extract contextual features and cross-scale correlation features in different directions. The fusion block uses parallel spatial attention and channel attention to fuse feature information at different scales. The decoding stage uses large kernel convolution blocks and pyramidal attention to reconstruct the fused features. Ablation experiments and comparison experiments are conducted on the public datasets TNO, RoadScene, and NIR, and the results show that the algorithm in this article is superior in both subjective visual and objective evaluation.
High-Resolution Remote Sensing Image Semantic Segmentation via Multiscale Context and Linear Self-Attention

Remote sensing image semantic segmentation, which aims to realize pixel-level classification according to the content of remote sensing images, has broad applications in various fields. Thanks to the superiority of deep learning (DL), the semantic segmentation model based on the convolutional neural network (CNN) dramatically promotes the development of remote sensing image semantic segmentation. Due to the high resolution, comprehensive coverage, extensive data, and sizeable spectral difference of high-resolution remote sensing images (HRRSI), the existing GPU is not suitable for directly semantic segmentation of the whole image. Cutting the image into small patches will lead to the loss of context information, resulting in the decline of accuracy. To address this issue, we propose the multiscale context self-attention network (MSCSANet). It combines the benefits of the self-attention mechanism with CNN to improve the segmentation quality of various remote sensing images. The MSCSANet extracts multiscale features from multiscale context images to solve the problem of feature loss caused by image segmentation. In addition, in order to make use of the feature of large-scale context, the multiscale context patches are used to guide the local image patch to focus on different fine-grained objects to enhance the feature of the local image patch. Moreover, considering the limited computing resources, we designed a linear self-attention module to reduce the computational complexity. Compared with other DL models, our proposed model can enhance the ability of multiscale features in complex scenes, and realizes improvements of 1.56% mean intersection over union (MIoU) on the Gaofen Image Dataset and 1.93% MIoU on the ISPRS Potsdam Dataset, respectively.
Haptic Touch: A retrofittable tactile sensing glove and haptic feedback armband for scalable and robust sensory feedback

Vibrotactile haptic feedback is a non-invasive method of providing tactile sensory feedback to amputees that has been shown to improve prosthetic task performance. However, current haptic feedback technologies remain inaccessible to much of the amputee population due to unscalable, tethered, and non-retrofittable designs that are difficult to distribute. In this work, we propose a wireless, retrofittable, scalable, and distributable haptic feedback system, ‘Haptic Touch’ using flexible PCBs. The proposed system consists of a tactile sensing glove with 27 tactile sensors and a haptic feedback armband with 6 vibration motors: both featuring rechargeable batteries, wireless communication, and embedded processing. As simple demonstrations of potential experiments that can be investigated using Haptic Touch, we conducted two preliminary experiments: 1) evaluating two-point touch vibrotactile discrimination accuracy and 2) evaluating the effect of different haptic feedback strategies on object manipulation. Our results showed that participants were able to discriminate two-point touch stimuli via vibrotactile feedback with 83% accuracy. Furthermore, our object manipulation experiment showed that haptic feedback based on the derivative of force led to a trend of improved performance compared to proportional feedback. Future studies can leverage the Haptic Touch platform developed here as a distributable research tool to further study and optimize vibrotactile sensory feedback for amputees.
Electromyography-Based, Robust Hand Motion Classification Employing Temporal Multi-Channel Vision Transformers

With an increasing use of robotic and bionic devices for the execution of everyday life, complex tasks, Electromyography (EMG) based interfaces are being explored as candidate technologies for facilitating an intuitive interaction with such devices. However, EM G- based interfaces typically require appropriate features to be extracted from the raw EMG signals using a plethora of feature extraction methods to achieve excellent performance in practical applications. To select an appropriate feature set that will lead to significant EMG-based decoding performance, a deep understanding of available methods and the human musculoskeletal system is needed. To overcome this issue, researchers have proposed the use of deep learning methods for automatically extracting complex features directly from the raw EMG data. In this work, we propose Temporal Multi-Channel Vision Transformers as a deep learning technique that has the potential to achieve dexterous control of robots and bionic hands. The performance of this method is evaluated and compared with other well- known methods, employing the open-access Ninapro dataset.
Three-dimensional sphericity, roundness and fractal dimension of sand particles

Particle morphological features at different scale levels hold the key to understanding the geological origin and mechanical behaviour of natural sands. In this context, it is necessary to characterise and quantify these morphological features by defining a series of reasonable descriptors. In this study, based on X-ray micro-computed tomographic (μCT) images collected from a series of image-processing techniques, the authors first introduced spherical harmonic analysis to reconstruct a three-dimensional (3D) realistic surface of the sand particles. Then 3D sphericity, roundness and fractal dimension were introduced to define the global form, local features and surface textures of the particle morphology. Based on the spherical harmonic–reconstructed surface, a novel framework was established to measure the descriptors of 3D sphericity, roundness and fractal dimension of sand particles. The 3D fractal dimension was an original descriptor used to characterise the fractal nature of the surface textures of r.
A Deep Wide-Field, Optical, and Near-Infrared Catalog of a Large Area around the Hubble Deep Field North

We have conducted a deep multicolor imaging survey of 0.2 deg2 centered on the Hubble Deep Field North (HDF-N). We shall refer to this region as the Hawaii HDF-N. Deep data were collected in U, B, V, R, I, and z′ bands over the central 0.2 deg2 and in HK′ over a smaller region covering the Chandra Deep Field North. The data were reduced to have accurate relative photometry and astrometry across the entire field to facilitate photometric redshifts and spectroscopic follow-up. We have compiled a catalog of 48,858 objects in the central 0.2 deg2 detected at 5 σ significance in a 3″ aperture in either R or z′ band. Number counts and color-magnitude diagrams are presented and shown to be consistent with previous observations. Using color selection we have measured the density of objects at 3 < z < 7. Our multicolor data indicates that samples selected at z > 5.5 using the Lyman break technique suffer from more contamination by low-redshift objects than suggested by previous studies.
Byzantine Resilience With Reputation Scores

Training distributed machine learning algorithms is prone to Byzantine attacks where the adversarial workers send corrupted model updates to derail the training. In this paper, we propose a reputation score based gradient aggregation as a possible solution. We introduce two novel stochastic gradient descent algorithms, ByGARS (Byzantine Gradient Aggregation using Reputation Scores) and By $\mathbf{GARS++}$ that involve computing reputation scores (of workers) using an auxiliary dataset at the server. These reputation scores are then used for aggregating the gradients (model updates) at the server. Under reasonable assumptions, we show that using these reputation scores is robust to any number of adversaries and prove the convergence of By $\mathbf{GARS++}$ for strongly convex objective functions using results from two-timescale stochastic approximation theory. The computational complexity of By $\mathbf{GARS++}$ is the same as the usual distributed stochastic gradient descent method with only an additional inner product computation in every iteration. We also demonstrate the effectiveness of the algorithms for non-convex learning problems using MNIST and CIFAR-10 datasets against almost all state-of-the-art Byzantine attacks.
A simple protocol for fault tolerant verification of quantum computation

With experimental quantum computing technologies now in their infancy, the search for efficient means of testing the correctness of these quantum computations is becoming more pressing. An approach to the verification of quantum computation within the framework of interactive proofs has been fruitful for addressing this problem. Specifically, an untrusted agent (prover) alleging to perform quantum computations can have his claims verified by another agent (verifier) who only has access to classical computation and a small quantum device for preparing or measuring single qubits. However, when this quantum device is prone to errors, verification becomes challenging and often existing protocols address this by adding extra assumptions, such as requiring the noise in the device to be uncorrelated with the noise on the prover’s devices. In this paper, we present a simple protocol for verifying quantum computations, in the presence of noisy devices, with no extra assumptions. This protocol is based on post hoc techniques for verification, which allow for the prover to know the desired quantum computation and its input. We also perform a simulation of the protocol, for a one-qubit computation, and find the error thresholds when using the qubit repetition code as well as the Steane code.
Thermodynamics of dual conformal field theories for kerr-ads black holes

Recently Gibbons et al. in [G. W. Gibbons et al. Class. Quant. Grav. 22, 1503 (2005)] defined a set of conserved quantities for Kerr-AdS black holes with the maximal number of rotation parameters in arbitrary dimension. This set of conserved quantities is defined with respect to a frame which is nonrotating at infinity. On the other hand, there is another set of conserved quantities for Kerr-AdS black holes, defined by Hawking et al. in [Hawking et al. Phys. Rev. D 59, 064005 (1999)], which is measured relative to a frame rotating at infinity. Gibbons et al. explicitly showed that the quantities defined by them satisfy the first law of black hole thermodynamics, while those quantities defined by Hawking et al. do not obey the first law. In this paper we discuss thermodynamics of dual CFTs to the Kerr-AdS black holes by mapping the bulk thermodynamic quantities to the boundary of the AdS space. We find that thermodynamic quantities of dual CFTs satisfy the first law of thermodynamics and Cardy-Verlinde formula only when these thermodynamic quantities result from the set of bulk quantities given by Hawking et al. We discuss the implication of our results.
Dual-View 3D Reconstruction via Learning Correspondence and Dependency of Point Cloud Regions

Multi-view 3D reconstruction generally adopts the feature fusion strategy to guide the generation of 3D shape for objects with different views. Empirically, the correspondence learning of object regions across different views enables better feature fusion. However, such idea has not been fully exploited in existing methods. Furthermore, current methods fail to explore the intrinsic dependency among regions within a 3D shape, leading to a rough reconstruction result. To address the above issues, we propose a Dual-View 3D Point Cloud reconstruction architecture named DVPC, which takes two views images as inputs, and progressively generates a refined 3D point cloud. First, a point cloud generation network is assigned to generate a coarse point cloud for each input view. Second, a dual-view point clouds synthesis network is presented in DVPC. It constructs a regional attention mechanism to learn a high-quality correspondence among regions across two coarse point clouds in different views, so that our DVPC can achieve feature fusion accurately. And then it develops a point cloud deformation module to produce a relatively-precise point cloud via establishing the communication between the coarse point cloud and the fused feature. Lastly, a point-region transformer network is devised to model the dependency among regions within the relatively-precise point cloud. With the dependency, the relatively-precise point cloud is refined into a desirable 3D point cloud with rich details. Qualitative and quantitative experiments on the ShapeNet and Pix3D datasets demonstrate that the proposed DVPC outperforms the state-of-the-art methods in terms of reconstruction quality.
Progress on Cutting-Edge Infrared-Terahertz Biophysics

Infrared-terahertz (IRT) resonant absorption complies with unique biomolecule fingerprints, including neurotransmitters, proteins, ribonucleic acid, and deoxyribonucleic acid, rendering it a suitable technology to achieve conformational variations and functional adjustments. Recent research has preliminarily confirmed that IRT waves can reversibly and nonthermally regulate gene expression, transmit biological information, transform protein function, and intervene in the interchange of material at the molecular level. Regarding macroscopic effects, IRT waves can enhance cell motility, improve brain ability, and affect mood. This review article attempts to provide comprehensive insight to illustrate the application of IRT waves in biophysics. Some typical experiments and equipment are summarized to demonstrate how to conduct IRT biophysics research. This cutting-edge field involves interesting research topics spanning physics and biology and opens an avenue to the performance of interdisciplinary research.
APPROXIMATE INERTIAL MANIFOLDS TO THE NAVIER-STOKES EQUATIONS

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Thermal and energetic processing of astrophysical ice analogues rich in SO2

Context. Sulfur is an abundant element in the cosmos and it is thus an important contributor to astrochemistry in the interstellar medium and in the solar system. Astronomical observations of the gas and of the solid phases in the dense interstellar/circumstellar regions have evidenced that sulfur is underabundant. The hypothesis to explain such a circumstance is that it is incorporated in some species in the solid phase (i.e. as frozen gases and/or refractory solids) and/or in the gas phase, which for different reasons have not been observed so far. Aims. Here we wish to give a contribution to the field by studying the chemistry induced by thermal and energetic processing of frozen mixtures of sulfur dioxide (one of the most abundant sulfur-bearing molecules observed so far) and water. Methods. We present the results of a series of laboratory experiments concerning thermal processing of different H 2 O:SO 2 mixtures and ion bombardment (30 keV He + ) of the same mixtures. We used in situ Fourier transform infrared (FTIR) spectroscopy to investigate the induced effects. Results. The results indicate that ionic species such as HSO 3 − , HSO 4 − , and S 2 O 5 2− are easily produced. Energetic processing also produces SO 3 polymers and a sulfurous refractory residue. Conclusions. The produced ionic species exhibit spectral features in a region that, in astronomical spectra of dense molecular clouds, is dominated by strong silicate absorption. However, such a dominant feature is associated with some spectral features, some of which have not yet been identified. We suggest adding the sulfur-bearing ionic species to the list of candidates to help explain some of those features. In addition, we suggest that once expelled in the gas phase by sublimation, due to the temperature increase, and/or by non-thermal erosion those species would constitute a class of molecular ions not detected so far. We also suggest that molecular sulfur-bearing ions could be present on the surfaces and/or in the atmospheres of several objects in the solar system, for example icy satellites of the giant planets and comets.
Image Classification of Biota Specimens Based On Multi-Scale Bilinear Feature Fusion Model

Objective Biological image detection and classification is a classic and challenging task at present. In order to accurately and automatically identify biological species and ownership relationship, a new classification model and tree structure are proposed to improve the performance of biological detection or classification. Methods Two groups of experiments were set up. Using YOLOv5 as the first layer of the tree structure, YOLOv5 is easy to deploy and can roughly locate the image and remove noise information beyond the target. Then the first group of experiments were trained with RESNET-101, RESNET-200 and DenSenet-201 at different levels of biological tree structure, respectively, and the results were compared with those directly using YOLOv5. In the second group of experiments, the new multi-scale bilinear feature fusion classification model is used to classify images successively, and finally obtain the accurate classification of objects. Result In the first group of experiments, the best result of the customized data set of 80 insect species was 1.09% higher than the mAP value of YOLOv5x. In the second group of experiments, the accuracy of cuB-200-2011(Caltech- UCSDbirds-200-2011) data was 86.4%. The accuracy is 2.3% higher than that of B-CNN classification model, which verifies the effectiveness of the improved method and model in this paper. Conclusion More complementary information can be obtained by multi-scale feature fusion. Bilinear fusion of features at different scales can fully express features at different scales and improve the accuracy of classification tasks. The tree structure effectively removes irrelevant noise information, and at the same time classifies layer by layer, simplifies the classification task, enriches the feature information, and makes the result more accurate
Early Diagnosis of Melanoma by Augmenting Feature Extraction of Epidermis using Faster Region-Based Convolutional Neural Networks

As fatal diseases like cancer are becoming more common nowadays and skin cancer being one of them, detection of the disease and its treatment at an early stage holds vital importance in order for the patient to recover from it both physically and mentally. Similarly, number of melanoma cases have been increasing gradually and subsequently. Introducing advanced and state-of-the-art technologies like computer vision to the medical field has proven nothing but advantageous to the patients as well as doctors. Our study helps identify skin cancers like melanoma without getting a physical biopsy done. The affected region of the skin is given as an input to our model and which runs inference on it to identify whether melanoma is detected or not. Image segmentation and feature extraction of the image takes place and then the Fast RCNN R101-FPN model from detectron model zoo is used for image classification. Detectron2 consists a number of object detection algorithms like faster R-CNN, Densepose, RetinaNet that help us detect the problem region. The model consists a number of hyper-parameters and the input images of lesion is up-sampled to give the desired output. Faster R-CNN has been used for instance detection in this research.
Variability and Wave Power for Open Ocean Wave Conditions of the Peru Basin

Technologies for converting wave energy to electricity are not mature yet. It is necessary to understand the characteristics and potential of the wave climate of the local area where wave energy converters will be installed. State of the art presents that wave spectrums for confined ocean wave conditions, like JONSWAP, were assumed for the wave climate of the Peruvian sea. The present work aims to approximate the wave power average in the Peru Basin, considering its open sea wave conditions through statistical and spectral analysis and contrasting its results with previous research. The wave power resulted in stable behaviour throughout the year, with a shallow temporal variability, mainly at half of the year. The amount of wave power is profitable for deploying wave energy converters. The wave power minimum average equals the minimum beneficial prescribed value (15 kW/m). The significant wave height is also considered stable throughout the year with a low coefficient of variability. This variability directly influences the variability of the wave power. Our results indicate that the Peru Basin is a sea with suitable and predictable wave power, and its low variability allows the safe deployment of wave energy converters.
Systematic Study on Positive Bias Temperature Instability(PBTI) of ZrO2-based Ge nMOSFETs with Interlayer Passivations

In this work, the positive bias temperature instability (PBTI) characteristics of ZrO<inf>2</inf>-based Ge n-channel metal–oxide–semiconductor field-effect transistors (nMOSFETs) with different interlayer (IL) passivation were systematically studied. The threshold voltage (V<inf>TH</inf>) shift, maximum transconductance (g<inf>max</inf>), and the trapping behaviors of devices after stress and recovery procedure are characterized. The generation of the interfacial state dominates the PBTI degradation, and the location of trap generation occurs at the Ge/IL interface rather than at the IL/ZrO<inf>2</inf> interface only, resulting in the g<inf>max</inf> degradation along with the VTH shift. The device with Al<inf>2</inf>O<inf>3</inf>/GeO<inf>x</inf> IL exhibits higher performance on mobility, while the SiO<inf>2</inf> IL sample has a higher voltage acceleration exponent(γ), and larger V<inf>OV</inf> for achieving a 10-year lifetime.
Comparison of In-home Robotic Companion Pet Use in South Korea and the United States: A Case Study

This paper presents an intensive case study of 10 participants in the US and South Korea interacting with a robotic companion pet in their own homes over the course of several weeks. Participants were tracked every second of every day during that period of time. The fundamental goal was to determine whether there were significant differences in the types of interactions that occurred across those cultural settings, and how those differences affected modeling of the human-robot interactions. We collected a mix of quantitative and qualitative data through sensors onboard the robot, ecological momentary assessment (EMA), and participant interviews. Results showed that there were significant differences in how participants in Korea interacted with the robotic pet relative to participants in the US, which impacted machine learning and deep learning models of the interactions. Moreover, those differences were connected to differences in participant perceptions of the robot based on the qualitative interviews. The work here suggests that it may be necessary to develop culturally-specific models and/or sensor suites for human-robot interaction (HRI) in the future, and that simply adapting the same robot's behavior through cultural homophily may be insufficient.