Miracle-dz/UniFAR-FTUs huggingface dataset:
Kinodynamic Pose Optimization for Humanoid Loco-Manipulation

This paper presents a novel approach for controlling humanoid robots to push heavy objects. The approach combines kinodynamic pose optimization and loco-manipulation model predictive control (MPC). The proposed pose optimization considers the object-robot dynamics model, robot kinematic constraints, and object parameters to plan the optimal pushing pose for the robot. The loco-manipulation MPC is used to track the optimal pose by coordinating pushing and ground reaction forces, ensuring accurate manipulation and stable locomotion. Numerical validation demonstrates the effectiveness of the framework, enabling the humanoid robot to push objects with various parameter setups. The pose optimization can be solved as a nonlinear programming (NLP) problem within an average of 250 ms. The proposed control scheme allows the humanoid robot to push objects weighing up to 20 kg (118% of the robot's mass). Additionally, when applying a 120 N force disturbance to the object, the proposed controller can recover the system effectively.
Are the Post-transplant Outcomes of Kasai's Early Failure and Late Failure Comparable to the Primary Liver Transplantation?

It is uncertain whether prior Kasai procedures negatively impact the outcomes of liver transplantation (LT). The prior meta-analysis did not distinguish between Kasai early failure (K-EF) and late failure (K-LF). Numerous studies have been recently published; therefore, we perform a systematic review and meta-analysis. We searched PubMed and Embase databases to identify studies comparing the outcomes of biliary atresia (BA) patients undergoing primary LT versus patients with prior Kasai procedures. Subgroup analysis was done at the time of Kasai failure (early vs. late). Twenty-five studies comprising 6,408 patients receiving LT were included in the analysis. We found a statistically significant increase in one-year graft survival in K-LF versus primary liver transplant (pLT) (P = 0.0003). One-year patient survival was also increased in K-LF, although not statistically significant (P = 0.09). No difference in the one- and five-year graft and patient survival, reoperation rate, infection, and biliary complication was seen in pLT vs overall prior Kasai (K-EF and K-LF). These results suggest that prior kasai procedure does not negatively impact the outcome of LT. In addition, BA patients with prior Kasai undergoing LT later in life tend to perform better than primary liver transplants.
Investigating the Association between the Autophagy Markers LC3B, SQSTM1/p62, and DRAM and Autophagy-Related Genes in Glioma

High-grade gliomas are extremely fatal tumors, marked by severe hypoxia and therapeutic resistance. Autophagy is a cellular degradative process that can be activated by hypoxia, ultimately resulting in tumor advancement and chemo-resistance. Our study aimed to examine the link between autophagy markers’ expression in low-grade gliomas (LGGs) and high-grade gliomas (HGGs). In 39 glioma cases, we assessed the protein expression of autophagy markers LC3B, SQSTM1/p62, and DRAM by immunohistochemistry (IHC) and the mRNA expression of the autophagy genes PTEN, PI3K, AKT, mTOR, ULK1, ULK2, UVRAG, Beclin 1, and VPS34 using RT-qPCR. LC3B, SQSTM1/p62, and DRAM expression were positive in 64.1%, 51.3%, and 28.2% of glioma cases, respectively. The expression of LC3B and SQSTM1/p62 was notably higher in HGGs compared to LGGs. VPS34 exhibited a significant differential expression, displaying increased fold change in HGGs compared to LGGs. Additionally, it exhibited robust positive associations with Beclin1 (rs = 0.768), UVRAG (rs = 0.802), and ULK2 (rs = 0.786) in HGGs. This underscores a potential association between autophagy and the progression of gliomas. We provide preliminary data for the functional analysis of autophagy using a cell culture model and to identify potential targets for therapeutic interventions.
HCGNet: A Hybrid Change Detection Network Based on CNN and GNN

Image features occur at different scales, e.g., short-term and long-term ones, and both of them are significant in the change detection (CD) of remote sensing images. To the best of our knowledge, however, it is still a challenge on how to effectively combine them together for a full-scale CD. The development of deep learning techniques brings the light on this issue. In this work, we propose a hybrid initiative called HCGNet, combining convolutional neural network (CNN) and vision graph neural network (ViG) for capturing the local and global features, respectively, in which we conduct two main adaptions for high accuracy: 1) a shift graph convolution module to establish the association between a node and its surrounding nodes for enhancing the local feature extraction capability and 2) a dual-branch decoder structure that efficiently utilizes the multiscale features acquired from the encoder to enhance the accuracy of the change map. The results show that: 1) our proposal outperforms the state-of-the-art works and 2) the individual functions of each component are obvious in the ablation experiments.
Oral Appliance Therapy for Obstructive Sleep Apnea: Clinical Benefits and Limitations

Obstructive sleep apnea (OSA) is a condition characterized by recurrent upper airway obstruction during sleep. It is associated with a wide range of symptoms and complications. Management includes positive airway pressure (PAP), upper airway surgery, oral appliance therapy, positional therapy, and weight loss. Oral appliance therapy treats OSA through direct expansion of the upper airway or indirectly by preventing its collapse. Its effectiveness in improving apnea-hypopnea index and oxygen desaturation is gaining wider approval and the American Academy of Sleep Medicine and the American Academy of Dental Sleep Medicine currently recommend oral appliance as an alternative therapy for those who are unable to PAP therapy. To achieve maximum benefits, it is essential to evaluate factors that can affect treatment outcomes as well as create devices tailored to the individual patient’s dentition and capable of manipulating the degree of mandible advancement. While most are minor and transient, patients should always be warned about the potential adverse effects on mastication and mandibular joint as they can negatively influence treatment compliance. Although PAP shows superior results in improving polysomnography parameters, oral appliance therapy displays comparable health outcomes and greater adherence. Additional studies on establishing patient selection, optimal titration procedure, and measuring objective adherence will further improve oral appliance therapy acceptance.
Genetic and Environmental Factors Affecting Improvement of Rootstocks for Tomato

ii Acknowledgments. v Vita . vi Publications . vi Fields of Study . vi List of Tables . viii List of Figures . x : Introduction . 1 Chapter 1 : Genetic and environmental factors affecting seed quality of tomato rootstocks Chapter 2 using wild species . 28 : Cyanoacrylate adhesive improve grafting success between tomato rootstock Chapter 3 and scion . 61 : Analysis of Quantitative Trait Loci (QTL) associated with the failure of Chapter 4 grafting tomatoes . 81 : Conclusions . 104 Chapter 5 References . 109
Research on the Urbanization of Rural Settlement in City Outskirt in China

Rapid urbanization in China makes construction and management of city out-skirts a hot spot of the society. Empirically, this paper compares characteristics of city outskirt rural settlements before and after urbanization from the aspects of society, facilities and intentions for settlement, thus pointing out features of changing during the urbanization of rural settlements in city outskirt, and puts forward solutions and proposals in terms of coping with the low-position challenges and soft environment construction under the background of the new trends.
On-Chip Fully Reconfigurable Microwave Photonic Flat-Top Filter

We propose and experimentally demonstrate an on-chip fully reconfigurable microwave photonic flat-top filter based on cascaded ultrahigh-Q silicon racetrack micro-ring resonators (MRRs). The microwave photonic filter (MPF) is realized based on phase modulation to intensity modulation conversion, which maps the spectral response of the cascaded MRRs in the optical domain to the one of the resulted microwave filter in the electrical domain. To enable filter frequency tunability, two independent micro-heaters are placed on top of the two MRRs. By controlling the direct current (DC) voltage applied to the micro-heaters, a flat-top MPF is produced, whose center frequency and bandwidth can be flexibly tuned. A silicon-based ultrahigh-Q cascaded MRRs chip is designed, fabricated and evaluated. With the use of the chip, a flat-top MPF is experimentally demonstrated. The experimental results show that the MPF has a tunable center frequency from 4 to 24 GHz and a tunable bandwidth from 225 to 405 MHz. Thanks to its strong reconfigurability, the proposed integrated microwave photonic flat-top filter is promising to be widely used in multi-function microwave photonic signal processing systems.
Stock Volatility Forecasting with Transformer Network

Financial market is in general volatile with so many uncertainties and volatility is one of the main measures of uncertainty in the market among other measures. Hence, forecasting volatility is a critical component in risk management, optimizing portfolios, and in algorithmic trading among other financial problems. There have been few machine learning and artificial intelligence techniques used in the literature for the forecasting problem. Transformer Network (TN) architecture is one of newest such techniques proposed. In this work, we utilized this architecture with multi-head attention mechanism for volatility forecasting. To enhance the performance of the TN, we incorporated different variations of the feed forward layer. The performance of three distinct TN models was evaluated by implementing three different deep learning layers (CNN, LSTM, and a hybrid layer (CNN-LSTM)) in the encoder block of TN as the feed forward layer. The results clearly demonstrate that the TN model with the hybrid layer (CNN-LSTM) outperformed the other models, including a recently proposed data-driven approach.
On the Choice of Unique Identifiers for Predicting Pareto-Optimal Solutions Using Machine Learning

Incomplete or sparse non-dominated fronts are unavoidable in multi-objective optimization due to complexity of problems, morphology of Pareto optimal fronts, and stochasticity involved in evolutionary optimization algorithms. It is pragmatic to develop methods that can alleviate some of these issues after the optimization run is complete, without the need for re-optimization or additional solution evaluations. Previously developed methods demonstrated that it is possible to predict Pareto-optimal solutions from pseudo-weight vectors using Gaussian Process Regression (GPR) models. We extend the GPR-based method to predict new Pareto-optimal solutions using reference vectors as unique identifiers and demonstrate that like the pseudo-weight vectors, reference vectors can also used instead in learning the association between identifiers and corresponding variable vectors. Results on many test problems indicate that the choice of a suitable identifier makes a large impact on the decision-making process, particularly for visualizing the newly created non-dominated (ND) solutions. In this study, we discuss the advantages and disadvantages of using pseudo-weights and reference vectors as unique identifiers for ND solutions, paving the way to devise further identifiers for predicting new Pareto-optimal solutions.
Three essays on land and an intensive farming system in Sub-Saharan Africa: Evidence from Kenya

Thesis Ph. D. Michigan State University. Agricultural, Food and Resource Economics 2015%%%%In light of the high rate of population growth and rapid urbanization, the arable land frontier in Sub-Saharan Africa countries has been exhausted and the land-labor ratio has been shrinking. Under this setting, access to land for smallholders and the landless and improvement of land productivity have critical implication for poverty reduction and food insecurity. This dissertation is composed of three essays that study the determinants and impacts of land access, land distribution, and an intensified farming system in the context of Sub-Saharan Africa using household survey data from rural Kenya. The first essay utilizes household and parcel-level data from rural Kenya to explore the linkage between land access and food security. We find that a 10% increase in operated land size would increase household total food consumption per capita, cereal consumption per capita, non-cereal consumption, and home produced food consumption by 2.6%, 2.1%, 2.7% and 5.4%, respectively. We also find that land rental is the dominant mechanism that poor rural farmers use to access additional land for cultivation. However, the levels of long-term land investment (measured by application of organic fertilizer) and land productivity are significantly lower for rented parcels than for own parcels even after household fixed effect and parcel-level observed characteristics are controlled for. Furthermore, the amount of land actually rented in or out is found to be significantly below the amount of land desired if the land rental market is functioning perfectly. These findings point to the existence of problems with land rental markets that impede their ability to fully contribute to national food security and poverty reduction goals. The second essay aims to explore the determinants of the new maize farming system, which is characterized by adoption of high-yielding maize varieties, application of chemical fertilizer and manure produced by stall-fed improved dairy cows, and intercropping, especially the combination of maize and legumes, and its impact on land productivity and household income. We examine not only the impacts of new technologies and production practices but also the impacts of the entire new maize farming system by generating an agricultural intensification index based on a principal component analysis. Our estimation results show that an increase in sub-location level population density and a decrease in the land-labor ratio of an individual household accelerate farming intensification, and that adoption of each new technology and production practice has positive and significant impacts on land productivity. These findings are further supported by the significantly positive impacts of the agriculture intensification index on land productivity. The third essay attempts to assess the determinants of agricultural land distribution and the effects of land distribution on agricultural productivity, income and poverty based on micro-level…
A Broadband Metalens Exhibiting Superior Focusing Efficiency and Polarization Insensitivity

In this paper, we propose a broadband metalens that demonstrates efficient operation across the wavelength range of 1270 to 2070 nm and exhibits polarization insensitivity. Compared to no metalens, the metalens can enhance incident light transmissivity by approximately 10%. In the operational wavelength range, the average focusing efficiency of the metalens is 56%, with a maximum focusing efficiency of 70.7%. The average full width at half maximum (FWHM) of this structure measures $0.5526\mu \mathrm{m}$
Investigation of delay and reliability in wireless sensor networks using the Gradient Descent algorithm

Wireless sensor networks play a key role in various applications such as environmental monitoring, smart cities and medical monitoring. Latency and reliability are two basic aspects that affect the efficiency and quality of service of these networks. In this research, delay and reliability analyzes of WSN were conducted through the application of mathematical models. Different parameters were analyzed such as distance, number of devices, and bandwidth to gain a deeper understanding of their impact on network performance. Mathematical models have been developed that take into account random variables and changing factors in order to conduct a more precise analysis. Through simulations and numerical experiments, Matlab codes will be used to simulate and analyze the actual process, all with the aim of achieving minimum delay and maximum reliability. This research provides a deeper insight into the characteristics of WSN and provides guidelines for the design and optimization of these networks.
Toward Cross-Dataset Finger Vein Recognition With Single-Source Data

Finger vein recognition (FVR) has recently gained significant attention due to its high security, contactless acquisition, and reliability. However, the principle of vein imaging and touchless mode makes vein images susceptible to variations caused by different acquisition devices and environmental conditions, resulting in gaps among data from different sources. As recognition systems need to be deployed on various devices under uncontrolled environments, FVR faces the challenge of cross-dataset recognition. Moreover, sharing data from multiple datasets is often infeasible due to privacy and security concerns, leading to that training can only be performed using a single-source dataset. To address cross-dataset FVR with single-source data, this article proposes a comprehensive approach. First, an instance-based adaptive transformation component is introduced to reduce dataset gaps at the instance level by removing uncontrolled bias fields that cause dataset discrepancies, adjusting the latent distribution to be more consistent and enhancing vein patterns. Second, a feature alignment and clustering learning algorithm is employed at the feature level to learn invariant features against intraclass variations and perturbations. Third, to fast deploy the deep model to different target datasets with anticipated recognition performance, a modified extreme learning machine (ELM)-based classifier is adopted as a task-specific classifier to quicken the learning speed and improve the generalization capability of the model. Extensive experiments are conducted on four public finger vein datasets, demonstrating the superiority of the proposed method in addressing cross-dataset FVR challenges.
Enhancing Conducting Gesture Analysis: Integrating Laban Movement Analysis with Tree Ensembles and Neural Networks

Our research focuses on integrating Laban Movement Analysis (LMA) with neural network technologies for analyzing conducting gestures. While promising, this approach faces challenges in realtime speed and adaptability. To over-come these limitations, we propose using tree ensembles, conducting LMA classification on conducting gestures with time-invariant transforms. This study aims to outperform the previous neural network approach in terms of time and set a benchmark for comparison, contributing valuable insights to enhance realtime applications in conducting gestures.
Impact of Ownership Structure and Corporate Governance on Earning Management

Purpose- This study investigates the interplay among Ownership Structure, Corporate Governance, and Earnings Management by employing ordinary least square (OLS) regression. To find the relationship among the three constructs based on data sourced from listed companies on the Pakistan Stock Exchange spanning 2016-2021 were used, excluding the financial industry due to its unique reporting system.Design/Methodology- The sample comprises 111 firms chosen based on data availability. To measure earnings management, the researchers used a modified version of John's model (1995) to estimate discretionary accruals. Findings- The study's key findings include the significant role of institutional investors in reducing earnings management. The number of board directors and ownership concentration were observed to impact discretionary accruals. Control variables indicated that more profitable, growing, and highly leveraged firms tend to engage in earnings management, which decreases with the firm's age. The study revealed a diverse relationship between ownership structure, corporate governance codes, and earnings management. Notably, significant institutional investment reduces ownership concentration, leading to decreased earnings management. Moreover, the results show a positive and significant correlation between firm size and Return on Assets (ROA). Practical Implications- Board independence was found to have a positive impact on earnings management, suggesting that boards serve a more complex role than mere monitoring to mitigate accounting manipulation.
Categorization of IRIS Flower Using Different Machine Learning Strategies

Today, machine learning is important in many areas, including agriculture, computer science, healthcare, and medicine, to name a few. It is known that our planet is abundant in species and millions of biomes thrive in the Earth's ecosystem. Some of them share physical characteristics such as size, shape, and color. It is quite difficult for a general human being to determine which items are of which sort. Similarly, it is difficult to determine which flower is of which subspecies in flower species. Classification is the most essential machine learning approach for distinguishing one item from another. Setosa, Versicolor, and Virginica are the three subspecies of the Iris flower. Five approaches are presented in depth, including Decision tree, Naive Bayes, Linear Support Vector Machine (SVM), K-nearest neighbor, and Logistic regression, and the implementation is done using the sci-kit learn the tool. Furthermore, different sizes of training and testing sets are applied to and compared with each method. The IRIS dataset was analyzed using classification and regression algorithms with a 10-fold cross-validation score. The emphasis was on examining patterns utilizing the flower's sepal and petal sizes. In terms of accuracy, the findings showed that the Linear SVM classifier outperformed with 99.17% Model accuracy and 100% of Test accuracy as compared to the Decision tree, Naive Bayes, K-nearest neighbor, and Logistic regression models.
Association between Leukemic Evolution and Uncommon Chromosomal Alterations in Pediatric Myelodysplastic Syndrome

Background Pediatric myelodysplastic syndrome (pMDS) is a group of rare clonal neoplasms with a difficult diagnosis and risk of progression to acute myeloid leukemia (AML). The early stratification in risk groups is essential to choose the treatment and indication for allogeneic hematopoietic stem cell transplantation (HSCT). According to the Revised International Prognostic Scoring System, cytogenetic analysis has demonstrated an essential role in diagnosis and prognosis. In pMDS, abnormal karyotypes are present in 30–50% of the cases. Monosomy 7 is the most common chromosomal alteration associated with poor prognosis. However, the rarity of specific cytogenetic alterations makes its prognosis uncertain. Thus, this study aimed to describe uncommon cytogenetic alterations in a cohort of 200 pMDS patients and their association with evolution to AML. Methods The cytogenetic analysis was performed in 200 pMDS patients by G-banding and fluorescence in situ hybridization between 2000 to 2022. Results Rare chromosome alterations were observed in 7.5% (15/200) of the cases. These chromosome alterations were divided into four cytogenetic groups: hyperdiploidy, biclonal chromosomal alterations, translocations, and uncommon deletions representing 33.3%, 33.3%, 20%, and 13.3%, respectively. Most of these patients (10/15) were classified with advanced MDS (MDS-EB and MDS/AML) and the initial subtype was present in five patients (RCC). The leukemic evolution was observed in 66.66% (10/15) of the patients. Most patients had poor clinical outcomes and they were indicated for HSCT. Conclusion The study of uncommon cytogenetic alterations in pMDS is important to improve the prognosis and guide early indication of HSCT.