Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of a space-based infrared imaging interferometer

Abstract. Present space-based optical imaging sensors are expensive. Launch costs are dictated by weight and size, and system design must take into account the low fault tolerance of a system that cannot be readily accessed once deployed. We describe the design and first prototype of the space-based infrared imaging interferometer (SIRII) that aims to mitigate several aspects of the cost challenge. SIRII is a six-element Fizeau interferometer intended to operate in the short-wave and midwave IR spectral regions over a 6×6 mrad field of view. The volume is smaller by a factor of three than a filled-aperture telescope with equivalent resolving power. The structure and primary optics are fabricated from light-weight space-qualified carbon fiber reinforced polymer; they are easy to replicate and inexpensive. The design is intended to permit one-time alignment during assembly, with no need for further adjustment once on orbit. A three-element prototype of the SIRII imager has been constructed with a unit telescope primary mirror diameter of 165 mm and edge-to-edge baseline of 540 mm. The optics, structure, and interferometric signal processing principles draw on experience developed in ground-based astronomical applications designed to yield the highest sensitivity and resolution with cost-effective optical solutions. The initial motivation for the development of SIRII was the long-term collection of technical intelligence from geosynchronous orbit, but the scalable nature of the design will likely make it suitable for a range of IR imaging scenarios.
Robust Bayesian linear regression with application to an analysis of the CODATA values for the Planck constant

Weighted least-squares estimation is commonly applied in metrology to fit models to measurements that are accompanied with quoted uncertainties. The weights are chosen in dependence on the quoted uncertainties. However, when data and model are inconsistent in view of the quoted uncertainties, this procedure does not yield adequate results. When it can be assumed that all uncertainties ought to be rescaled by a common factor, weighted least-squares estimation may still be used, provided that a simple correction of the uncertainty obtained for the estimated model is applied. We show that these uncertainties and credible intervals are robust, as they do not rely on the assumption of a Gaussian distribution of the data. Hence, common software for weighted least-squares estimation may still safely be employed in such a case, followed by a simple modification of the uncertainties obtained by that software. We also provide means of checking the assumptions of such an approach. The Bayesian regression procedure is applied to analyze the CODATA values for the Planck constant published over the past decades in terms of three different models: a constant model, a straight line model and a spline model. Our results indicate that the CODATA values may not have yet stabilized.
Interconnected Disturbance Observer-Based Tracking Control for Permanent Magnet Synchronous Motors

The novel interconnected disturbance observer (IDO) is proposed in this brief to tackle the mismatched and matched disturbances which may affect the speed control of the permanent magnet synchronous motor (PMSM). The designed IDO is composed of multiple disturbance observer (DO) pairs (DOPs), where each disturbance observer pair is constructed by two single DOs (SDOs) and is dependent on the same interconnected variable. For the IDO of PMSM, the positive definiteness of the observer gain in the DOP is not essential due to the designed structure of the IDO. In addition, weighting the DO gains in the DOP improves the disturbance estimation performance of the above IDO. Through analytical solutions of differential equations, the stable region and the decay rate of the disturbance estimation error (DEE) of the IDO for PMSMs are compared with that of the SDO. Besides, boundedness conditions of the DEE and tracking errors are also discussed. Finally, the effectiveness of the IDO-based controller (IDOBC) is verified by the experiment with TMS320F28335 DSP.
Self-Supervised Video Representation Learning by Serial Restoration With Elastic Complexity

Self-supervised video representation learning leaves out heavy manual annotation by automatically excavating supervisory signals. Although contrastive learning based approaches exhibit superior performances, pretext task based approaches still deserve further study. This is because the pretext tasks exploit the nature of data and encourage feature extractors to learn spatiotemporal logic by discovering dependencies among video clips or cubes, without manual engineering on data augmentations or manual construction of contrastive pairs. To utilize chronological property more effectively and efficiently, this work proposes a novel pretext task, named serial restoration of shuffled clips (SRSC), disentangled by an elaborately designed task network composed of an order-aware encoder and a serial restoration decoder. In contrast to other order based pretext tasks that formulate clip order recognition as a one-step classification problem, the proposed SRSC task restores shuffled clips into the right order in multiple steps. Owing to the excellent elasticity of SRSC, a novel taxonomy of curriculum learning is further proposed to equip SRSC with different pre-training strategies. According to the factors that affect the complexity of solving the SRSC task, the proposed curriculum learning strategies can be categorized into task based, model based and data based. Extensive experiments are conducted on the subdivided strategies to explore their effectiveness and noteworthy laws. Compared with existing approaches, this work demonstrates that the proposed approach achieves state-of-the-art performances in pretext task based self-supervised video representation learning and a majority of the proposed strategies further boost the performance of downstream tasks. For the first time, the features pre-trained by the pretext tasks are applied to video captioning by feature-level early fusion, and enhance the input of existing approaches as a lightweight plugin.
Schéma de lifting sur graphes et application à l'approximation d'images

Nous proposons une transformee multi-echelle s'appliquant a des fonctions scalaires definies sur l'ensemble des sommets d'un graphe non oriente pondere general. La transformee que nous proposons est basee sur une adapta- tion du schema de lifting. Une des difficultes de cette adaptation concerne le partitionnement des sommets du graphe. Nous adoptons une approche gloutonne recemment proposee et l'etendons a une transformee multi-echelle en l'iterant sur des sous-graphes induits. Nous examinons egale- ment l'effet de chaque etape sur la base sous-jacente. Nous nous interessons enfin a l'application de notre algorithme a l'approximation d'images numeriques et comparons les erreurs d'approximation non lineaire a M termes que nous obtenons avec celles obtenues avec d'autres transformees.
Fruit Detection and Counting in Apple Orchards Based on Improved Yolov7 and Multi-Object Tracking Methods

With the increasing popularity of online fruit sales, accurately predicting fruit yields has become crucial for optimizing logistics and storage strategies. However, existing manual vision-based systems and sensor methods have proven inadequate for solving the complex problem of fruit yield counting, as they struggle with issues such as crop overlap and variable lighting conditions. Recently CNN-based object detection models have emerged as a promising solution in the field of computer vision, but their effectiveness is limited in agricultural scenarios due to challenges such as occlusion and dissimilarity among the same fruits. To address this issue, we propose a novel variant model that combines the self-attentive mechanism of Vision Transform, a non-CNN network architecture, with Yolov7, a state-of-the-art object detection model. Our model utilizes two attention mechanisms, CBAM and CA, and is trained and tested on a dataset of apple images. In order to enable fruit counting across video frames in complex environments, we incorporate two multi-objective tracking methods based on Kalman filtering and motion trajectory prediction, namely SORT, and Cascade-SORT. Our results show that the Yolov7-CA model achieved a 91.3% mAP and 0.85 F1 score, representing a 4% improvement in mAP and 0.02 improvement in F1 score compared to using Yolov7 alone. Furthermore, three multi-object tracking methods demonstrated a significant improvement in MAE for inter-frame counting across all three test videos, with an 0.642 improvement over using yolov7 alone achieved using our multi-object tracking method. These findings suggest that our proposed model has the potential to improve fruit yield assessment methods and could have implications for decision-making in the fruit industry.
Treating a Case of First-Episode Psychosis to Uncover Undiagnosed Obsessive Compulsive Disorder: A Case Report

Obsessive-compulsive disorder (OCD) is a chronic psychiatric condition characterized by the presence of obsessions, compulsions, or both. Historically OCD was associated with good insight. However, there are more categories to the degrees of insight in OCD patients, namely good, fair, poor, absent, or delusional beliefs. It is also important to note that insight can fluctuate circumstantially. We describe a rare case of first-episode psychosis of undetermined cause presenting with suicidal ideation. After continued treatment, it was discovered to be previously undiagnosed OCD with poor insight.
Cayley and Tutte polytopes

Cayley polytopes were defined recently as convex hulls of Cayley compositions introduced by Cayley in 1857. In this paper we resolve Braun's conjecture, which expresses the volume of Cayley polytopes in terms of the number of connected graphs. We extend this result to a two-variable deformations, which we call Tutte polytopes. The volume of the latter is given via an evaluation of the Tutte polynomial of the complete graph. Our approach is based on an explicit triangulation of the Cayley and Tutte polytope. We prove that simplices in the triangulations correspond to labeled trees and forests. The heart of the proof is a direct bijection based on the neighbors-first search graph traversal algorithm.
Broadband metamaterial absorber on a single-layer ultrathin substrate

Abstract In this article, a broadband metamaterial microwave absorber on a low-cost FR-4 Epoxy substrate is proposed. The unit cell of the absorber consists of a staircase shape metallic patch placed on the top of the metal-backed ultrathin dielectric substrate having a thickness of 1.9 mm (0.07 λ0). The absorption of more than 90% is achieved with this proposed low profile single-layer microwave absorber throughout the operation band from 8.86 to 15.5 GHz. The performance is analyzed for different values of incident angle, polarization angle, substrate height, and dielectric constant. The surface current and the power loss density at the top and bottom planes at the two absorption peaks of 9.46 and 13.90 GHz are also analyzed to elaborate the absorption mechanism of the structure. Experimental result closely follows the simulated one. The broadband characteristics of the design with relative absorption bandwidth (RAB) of 54.51% at both TE and TM polarizations of incident wave for a wide incident angles makes it versatile for applications in the X and Ku bands of microwave frequencies. The proposed work is very compact (unit cell size: 0.22 λ0) with ultrathin substrate height (0.07 λ0) and giving RAB performance of 54.51% comparable with that of others. Thus with this single-layer low-cost substrate material a broadband absorber is achieved.
Women in Climate Change for Sustainable Impact: Ecotourism for Poverty Alleviation in Rural Akwa Ibom State

This study sought to assess the contributions of ecotourism as poverty alleviation strategy amongst women in rural Akwa Ibom State. The study was descriptive in nature and employed survey research design. Based on reconnaissance survey, three major ecotourism sites were purposively selected for the study, one from each of the senatorial districts in Akwa Ibom State, making a sample size of three (3). Fifty questionnaire forms were administered in each study site using simple random sampling method and same were returned. This makes a total of one hundred and fifty (150) questionnaire forms. Data were analysed descriptively based on the questionnaire responses. Furthermore, to evaluate the impact of women on sustainable ecotourism development, chi-square (x²) statistical method was employed for the hypothesis test. The result shows that women have significant impacts on sustainable ecotourism development. This indicates that women are the integral part of development. Also, new ecotourism attraction sites were discovered during the inventory. Therefore, the researcher recommends that women should actively participate and involve in ecotourism related activities. They should reinforce positive conservation habit towards natural resource base. Also, biodiversity should be advocated and women should be integrated in planning and decision making towards tourism development since they are more vulnerable.
Progressive Rehabilitation Based on EMG Gesture Classification and an MPC-Driven Exoskeleton

Stroke is a leading cause of disability and death worldwide, with a prevalence of 200 millions of cases worldwide. Motor disability is presented in 80% of patients. In this context, physical rehabilitation plays a fundamental role for gradually recovery of mobility. In this work, we designed a robotic hand exoskeleton to support rehabilitation of patients after a stroke episode. The system acquires electromyographic (EMG) signals in the forearm, and automatically estimates the movement intention for five gestures. Subsequently, we developed a predictive adaptive control of the exoskeleton to compensate for three different levels of muscle fatigue during the rehabilitation therapy exercises. The proposed system could be used to assist the rehabilitation therapy of the patients by providing a repetitive, intense, and adaptive assistance.
Circular Microstrip Patch Antenna for UHF RFID Reader

This paper presents an analysis of Circular shape patch antenna for Ultra High-Frequency Identification (UHF) Radio Frequency Identification (RFID) Reader Applications. The fabricated antenna has lightweight, simple structure, low profile and easy for fabrication due to the used of FR-4 materials with loss tangent 0.019, the dielectric constant of 4.7 and thickness of 1.6 mm. It can be operated for UHF RFID system in Malaysia with the frequency assigned from 919 MHz to 923 MHz. The antenna simulation was analysed by using CST Studio Suite 2016. From the results, the antenna has the reflection coefficient (S11) less than -10dB together with the bandwidth of 90 MHz. Other results of antenna parameter such as voltage standing wave ratio (VSWR), circular polarized radiation pattern, return loss and gain were also discussed. The complete size of the proposed antenna is 120 mm x 120 mm x 1.6 mm. Thus, it is suitable for RFID portable reader applications.
Convex Regression: Theory, Practice, and Applications

This thesis explores theoretical, computational, and practical aspects of convex (shape-constrained) regression, providing new excess risk upper bounds, a comparison of convex regression techniques with theoretical guarantee, a novel heuristic training algorithm for max-affine representations, and applications in convex stochastic programming. The new excess risk upper bound is developed for the general empirical risk minimization setting without any shape constraints, and provides a probabilistic guarantee for cases with unbounded hypothesis classes, targets, and noise models. The strength of the general result is demonstrated by applying it to linear regression under the squared loss both for lasso and ridge regression, as well as for convex nonparametric least squares estimation, in each case allowing one to obtain near-minimax upper bounds on the risk. Next, cutting plane and alternating direction method of multipliers algorithms are compared for training the max-affine least squares estimators; estimators for which we provide explicit excess risk bounds. These techniques are also extended for the partitioned convex formulation (which is shown to enjoy optimal minimax rates). We also provide an empirical study of various heuristics for solving the non-convex optimization problem underlying the partitioned convex formulation. A novel max-affine estimator is designed, which scales well for large sample sizes and improves the generalization error of current techniques in many cases. Its training time is proportional to the adaptively set model size, making it computationally attractive for estimation problems where the target can be efficiently approximated by max-affine functions. Realistic convex regression applications are synthetized for the convex stochastic programming framework such as an energy storage optimization using a solar source with an Economy 7 tariff pricing model, as well as a multi-product assembly problem of operating a beer brewery.
Cuticular Wax Triterpenes Maintain Storage Quality of Blueberries by Reducing Water Loss

Cuticular wax contributes to maintaining postharvest storage quality against fruit water loss and softening. Triterpenoids, such as oleanolic acid (OA) and ursolic acid (UA), are the main components in blueberry cuticular wax, but their role in water migration during the storage of blueberries remains to be determined. Here, we examined the relationship between the content of OA and UA and the storage quality of blueberry fruit (25 °C). The results revealed that the UA content during eight-day postharvest storage ranged from 58 to 77 μg cm−2, which was negatively related to weight loss. Additionally, we investigated the effect of exogenous OA and UA on water migration in the blueberry fruit during storage at room temperature; the weight loss was significantly lower (by 22%) with UA treatment than in the control fruit. Our findings indicate that OA and UA effectively affect water migration in blueberry fruit during postharvest storage, which could contribute to improving postharvest preservation techniques.
Use of high‐k encapsulation to improve mobility in trap‐limited metal‐oxide semiconductors

Low‐temperature spray‐deposited ZnO films are encapsulated by insulating Ga2O3 films and the resulting electronic properties of heterostructure films and transistors are analyzed and compared to un‐encapsulated ZnO as a function of processing temperature. An enhancement in Hall mobility and field‐effect mobility is observed similar to when high‐k dielectrics are used as gate‐dielectrics in transparent conductive oxide thin‐film transistors except without the presence of undesirable device behavior such as gate‐leakage and hysteresis. Based on a recently proposed theory consisting of electron donation from high‐k dielectrics, work function measurements show that conditions for electron transfer from Ga2O3 to ZnO can be met by optimally‐tuning the deposition temperature of the respective films resulting in an effective doping which is qualitatively similar to modulation doping. As a result, TFTs composed of ZnO/Ga2O3 heterostructures exhibit greatly improved switching characteristics and electron transport relative to the otherwise poor performance of low‐temperature processed ZnO achieved through a simple modification to TFT device structure.
Photogrammetry for 3D Reconstruction of Objects: Effects of Geometry, Texture and Photographing

Today, the use of 3D scanning methods is increasing in a wide range of industries from biomedical to game production. The photogrammetry method is in demand by 3D scanning users due to its portability, photorealistic textured modeling, and low cost. Scanned objects vary according to industries and can have various materials, geometries and textures. In order to better benefit from the advantages of the photogrammetry method, it is necessary to know the response of the method against different parameters. Unlike the studies in the literature, in this study, many objects with various geometries, different materials and textures were reconstructed and evaluated by using the same equipment and workflow in the same environment, and thus a fair evaluation was performed for various factors affecting the reconstruction process. In this context, 24 differently shaped objects with metal, fabric, plastic, wood, glass, ceramic and organic textures were reconstructed. A comprehensive evaluation was conducted on the solid and textured models generated from the objects. In addition to the effects of object geometry, texture properties, photo shooting angle and distance, it also sought answers to questions about factors affecting the accuracy and mesh quality of the 3D models. In this study, it is possible to find some recent specific results and validated general results together related to photogrammetric modeling.
Recent advances of nanocrystals in cancer theranostics

Emerging cancer cases across the globe and treating them with conventional therapies with multiple limitations have been challenging for decades. Novel drug delivery systems and alternative theranostics are required for efficient detection and treatment. Nanocrystals (NCs) have been established as a significant cancer diagnosis and therapeutic tool due to their ability to deliver poorly water-soluble drugs with sustained release, low toxicity, and flexibility in the route of administration, long-term sustainable drug release, and noncomplicated excretion. This review summarizes several therapies of NCs, including anticancer, immunotherapy, radiotherapy, biotheranostics, targeted therapy, photothermal, and photodynamic. Further, different imaging and diagnostics using NCs are mentioned, including imaging, diagnosis through magnetic resonance imaging (MRI), computed tomography (CT), biosensing, and luminescence. In addition, the limitations and potential solutions of NCs in the field of cancer theranostics are discussed. Preclinical and clinical data depicting the importance of NCs in the spotlight of cancer, its current status, future aspects, and challenges are covered in detail.
The Role of Measurement Uncertainty in Optimal Control for Contact Interactions

Stochastic Optimal Control (SOC) typically considers noise only in the process model, i.e. unknown disturbances. However, in many robotic applications that involve interaction with the environment, such as locomotion and manipulation, uncertainty also comes from lack of precise knowledge of the world, which is not an actual disturbance. We develop a computationally efficient SOC algorithm, based on risk-sensitive control, that takes into account uncertainty in the measurements. We include the dynamics of an observer in such a way that the control law explicitly depends on the current measurement uncertainty. We show that high measurement uncertainty leads to low impedance behaviors, a result in contrast with the effects of process noise variance that creates stiff behaviors. Simulation results on a simple 2D manipulator show that our controller can create better interaction with the environment under uncertain contact locations than traditional SOC approaches.
Ptychographic phase retrieval by applying hybrid input-output (HIO) iterations sequentially

We have designed a ptychographical algorithm (HIO-PIE) in which HIO (Hybrid Input-Output) iterations are applied for each probe position sequentially. Simulations indicated that HIO-PIE tends to outperform PIE (Ptychographical Iterative Engine) even in the presence of shot noise, especially when the number of probe positions is small. Thus, this adaptation may reduce the number of scan positions required for a successful image reconstruction, thereby relaxing the constraints on an experimental setup. In this article, experimental results obtained using an SLM (spatial light modulator) and visible laser light (633 nm) show that when few probe positions are used, HIO-PIE converges significantly faster than PIE