Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving robot locomotion through learning methods for expensive black-box systems

The modular snake robots in Carnegie Mellons Biorobotics lab provide an intriguing platform for research: they have already been shown to excel at a variety of locomotive tasks and have incredible potential for navigating complex terrains, but much of that potential remains untapped. Unfortunately, many techniques commonly used in robotics prove inapplicable to these snake robots. This is because of the robots complex multi-modal locomotion dynamics, which are difficult to model, and their small size and frequent impacts, which preclude the addition of many standard sensors. The motivation to expand the capabilities of these robots stems from experiencing several failures and limitations in real world tests. In an archaeological expedition near the Red Sea, the robot was able to move further than a human could into a collapsed cave containing four-millenia-old ship timbers. However, a gradual sandy slope prevented the robot from moving further and potentially making an archaeological discovery. At a disaster response training site, the robot was able to navigate a narrow passage underneath a rubble pile, but was unable to pass over a four inch high piece of wood which lay across its path once the passage widened. This thesis addresses the improvement of these capabilities by developing methods for the optimization of functions which are expensive (requiring significant time, money, computation, or other resources), black-box (providing no gradient or derivative information), need not be convex or linear, and may have many local optima. Objectives evaluated through tests on physical robotic systems often fit these categories. Several approaches are derived and tested for optimization of snake robot gait motion, leading to improved locomotion across flat and sloped terrain. Additional unique challenges posed by robotic systems are addressed, including stochasticity in the objective, consideration of multiple conflicting objectives, and the desire to adapt to changing environments. Although gaits are the motion of choice for traversing long distances over uniform terrain, real-world environments will rarely be completely uniform. Instead, complex motions also must be learned and optimized that enable navigation over complex terrain and large obstacles. To address this challenge, I describe an approach to record, simplify, and parameterize demonstrated trajectories from expert and novice users. As the settings which require such motions usually can only quantify the result of the motion in terms of success and failure rather than a numerical score, I derive extensions to the optimization framework used for improving gaits to handle stochastic binary functions, and use this to optimize robustness of trajectories for moving over obstacles. Overall, these algorithms allow snake robot locomotion through any type of environment to be optimized. Furthermore, the generality inherent in the black-box approach allows these techniques to be applicable to a wide variety of problems in robotics.
Can Corporate Digitalization Promote Green Innovation? The Moderating Roles of Internal Control and Institutional Ownership

This study examined the impact of corporate digitalization on green innovation using data concerning A-share companies listed on the Shanghai and Shenzhen stock markets from 2012 to 2018. We measured the degree of corporate digitalization through a text analysis of annual reports and empirically tested its impact on green innovation using a panel data fixed effects model. We found that digital transformation can improve green innovation levels, especially when internal control is weak and institutional ownership is low. These findings support the theoretical expectation that digitalization can strengthen supervision. The relationship between digitization and corporate governance mechanisms was proved to be complementary. Additionally, the analysis of economic consequences showed that digitalization improves financial performance by promoting green innovation. The results clarify the determinants of corporate green innovation and provide relevant insights for increasing the value of corporate digital transformation.
Comments on “ An Efficient PML Implementation for the ADI– FDTD Method”

For original paper see Wang and Teixeira (IEEE Microwave Wireless Comp. Lett., vol.13, p.72-4, 2003 February). In this paper, a more precise way to evaluate the actual performance of the perfectly matched layer (PML) used for the alternating direction implicit finite-difference time-domain (ADI-FDTD) method is presented. It is shown that the intrinsic numerical dispersion error of the ADI-FDTD method must be taken into account when the actual performance of the ADI-PML (as well as the ADI-FDTD method) is evaluated. Most importantly, it is demonstrated that the ADI-PMLs implemented with either the traditional manner or the way proposed in have almost the same level of accuracy when the performance of the ADI-PML is correctly evaluated.
Too Much, Too Soon?: The Multiple Roles and Identities of Newly Qualified Early Childhood Teachers

As recent legislation and future policy directions in the early childhood sector emphasise the importance of teacher qualification, there is a need to examine how this may be impacting on teachers, and in particular on those who are newly qualified. This paper reports on research which examined the experiences of a group of eight newly qualified teachers in the 18 months following the completion of their initial teacher education. The notion of communities of practice, originally developed from the work of Jean Lave and Etienne Wenger (1991), was used as a means of viewing and analysing the teachers' experiences. The findings of the study suggest that for all of the teachers in the study, being qualified resulted in an immediate change of identity, and with this new identity often came multiple roles and/or increasing levels of responsibility in their centre. The conflicting nature of these roles and identities meant that for some of the teachers in the study, very little support or acknowledgment of their newly qualified status was given to them in their first years of teaching. This paper concludes that further consideration and recognition of the experiences of newly qualified teachers in the early childhood sector is warranted to ensure that teachers get initial support, and that as a consequence, the retention of teachers is promoted.
The phenomenon of bubu tradition in the cycle of time: Portrait of reciprocity in rural Madura

Bubu is a form of reciprocity that occurs in Madura generally and Langkap Village particularly. Generally, the exchange is made by the community as a form of “donations” to the celebration host. The host hopes for a return in the future. The reciprocal incident that occurred in Madura was not just a hope but more of an “obligation” to pay the “donation” or bubu that had been given on the previous occasion. This study seeks to describe the form and meaning of the bubu as well as to explain the changes that occur in the bubu phenomenon which used to be only in the form of voluntary donations into various forms of bubu with their meanings. The research design used was an ethnographic study with a moderate method where the researcher was occasionally directly involved in the tradition. The results of this study reveal several forms of bubu that occur in Langkap Village with different ways and meanings with the same motivation. The bubu given is a transaction of accounts payable and investment with the hope of a return in the future. More than that, there are social sanctions that will await if the bubu that have been given are not returned with the same value.
Role of protein deimination in cardiovascular diseases: potential new avenues for diagnostic and prognostic biomarkers

ABSTRACT Introduction Arginine deimination (citrullination) is a post-translational modification catalyzed by a family of peptidyl arginine deiminase (PAD) enzymes. Cell-based functional studies and animal models have manifested the key role of PADs in various cardiovascular diseases (CVDs). Area Covered This review summarizes the past 10 years of knowledge on the role of PADs in CVD pathogenesis. It focuses on the PAD functions and diverse citrullinated proteins in cardiovascular conditions like deep vein thrombosis, ischemia/reperfusion, and atherosclerosis. Identification of PAD isoforms and citrullinated targets are essential for directing diagnosis and clinical intervention. Finally, anti-citrullinated protein antibodies (ACPAs) are addressed as an independent risk factor for cardiovascular events. Expert Opinion PAD is an unique family of enzymes that permanently converts amino acid arginine to amino acid citrulline in protein . Overexpression or increased activity of PAD has been observed in various CVDs with acute and chronic inflammation as the background. Importantly, far beyond being simply involved in forming neutrophil extracellular traps (NETs), accumulating evidence indicated PAD activation as a trigger for numerous processes, such as transcriptional regulation, endothelial dysfunction, and thrombus formation. In summary, the findings so far have testified the important role of deimination in cardiovascular biology, while more basic and translational studies are essential for further exploration.
Similarity-Aware CNN for Efficient Video Recognition at the Edge

Convolutional neural networks (CNNs) often extract similar features from successive video frames due to having identical appearances. In contrast, conventional CNNs for video recognition process individual frames with a fixed computational effort. Each video frame is independently processed, resulting in numerous redundant computations and an inefficient use of limited energy resources, particularly for edge computing applications. To alleviate the high energy requirements associated with video frame processing, this article presented similarity-aware CNNs that recognize similar feature pixels across frames and avoid computations on them. First, with a loss of less than 1% in recognition accuracy, a proposed similarity-aware quantization technique increases the average number of unchanged feature pixels across frame pairs by up to 85%. Then, a proposed similarity-aware dataflow improves energy consumption by minimizing redundant computations and memory accesses across frame pairs. According to simulation experiments, the proposed dataflow decreases the energy consumed by video frame processing by up to 30%.
Clinical value of serum VEGF and sFlt-1 in pernicious placenta previa

Abstract This study was designed to explore the expression and the diagnostic value of vascular endothelial growth factor (VEGF) and soluble fms-like tyrosine kinase-1 (sFlt-1) in pernicious placenta previa (PPP) combined placental accreta/increta. A total of 140 PPP patients were enrolled and divided into two groups: 56 patients with placenta accreta/increta (PA group), and 84 patients without placenta accreta/increta (non-PA group). In the same period, 46 pregnant women without PPP who had undergone caesarean section were selected as controls. The levels of VEGF and sFlt-1 in serum were detected by enzyme-linked immunosorbent assay. Diagnostic efficiency of VEGF and sFlt-1 in serum were evaluated by receiver operating characteristics curve. It was found that both VEGF and sFlt-1 had diagnostic value for PPP and placenta accreta/increta combined PPP. In addition, the levels of VEGF and sFlt-1 could be used to distinguish placenta accreta from placenta increta. VEGF was negatively correlated with sFlt-1 in PPP patients. In summary, the levels of VEGF and sFlt-1 could be used as auxiliary indicators to diagnose PPP and distinguish between placenta accreta and increta. KEY POINTS The levels of VEGF and sFlt-1 could be used to distinguish placenta accreta from placenta increta. VEGF is negatively correlated with sFlt-1 in PPP patients. The levels of VEGF and sFlt-1 could be used as auxiliary indicators to diagnose PPP and distinguish between placenta accreta and increta.
Theoretical and experimental studies on repaired and rehabilitated reinforced concrete frames

In this paper, an effective repair and rehabilitation working method is proposed for moderately damaged reinforced concrete (RC) building structures after major earthquakes. Three RC frames with nil, half-height, and full- height brick walls are designed and tested at the National Center for Research on Earthquake Engineering (NCREE), Taiwan. After the columns of these nonductile RC frames are damaged, steel wire cables with nonshrinkage mortar are used to repair the damaged columns, and carbon fiber reinforced polymers (CFRP) are used to rehabilitate the damaged columns. A stress-strain relationship of the confined concrete is used in the theoretical sectional analysis. The columns are confined by steel wires and CFRP, and the "equivalent column model" is proposed in this paper and used to analyze the brick walls inside the RC frames. The analytical results can reasonably predict the lateral force-displacement relationships of these RC frames.
A Reinforced Active Learning Algorithm for Semantic Segmentation in Complex Imaging

Semantic segmentation annotation helps train computer vision based Artificial Intelligence models where each image pixel is assigned to a specific object class. The model developers try to identify the features helpful for determining the objects of interest by using various supervised deep learning techniques. However, this is a difficult task due to the complexity of object structures. Two difficulties arise in the current approaches for semantic segmentation. The pixel-wise label approach is costly to obtain and is time consuming. Second, the datasets taken for the semantic segmentation task are not balanced since certain classes are present more than the others. This biases the model performance to the most represented ones. We propose a new reinforced active learning strategy based on a deep reinforcement learning algorithm. This work presents a modified Deep $Q$ Learning formulation for active learning. An agent learns the strategy of selecting a subset of small image regions, which are more knowledgeable than the whole set of images from an unlabeled data pool. The decision on the area of selection is dependent on the assumptions and segmentation model uncertainties taken for training purposes. We use the CamVid and RGB indoor test scenes dataset to evaluate the proof of concept. Our results infer that our approach demands more labels from under-represented groups than the baselines, thus enhancing their efficiency and mitigating the class imbalance. Our method’s performance is superior to the conventional deep learning models in detecting 8 out of 11 classes on the Camvid road segmentation scene dataset. It achieves an accuracy of 90.56%, a mIoU score of 87.17%, and a BF score of 93.14%. On the SUNRGB indoor scenes dataset, it gives an accuracy of around 75.82% and a BF score of 77.25%, thus outperforming the current state-of-the-art methods.
Compact copper microstrip filters with spiral resonators

Copper microstrip filters using spiral resonators, with a centre frequency of about 1 GHz and bandwidths of 5% and 16%, are designed and tested. Their advantages over parallel‐coupled filters are smaller size and more convenient shape. The spirals alleviate deterioration in simulation accuracy, over‐etch tolerance, and losses due to the size reduction. © 2004 Wiley Periodicals, Inc. Microwave Opt Technol Lett 42: 460–463, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20336
Dismantling The Cold War Economy

Based on extensive interviews with defence industry executives, Pentagon officials and community and union leaders, this book shows in detail how Cold War technologies have distorted and drained the economy. It is a comprehensive reassessment of the military-industrial complex now that the Cold War has ended. Ann Markusen is the author of "The Rise of the Gunbelt".
An examination of customers' adoption of restaurant search mobile applications

Restaurant search mobile apps are applications that can be downloaded and operated in mobile phones, and normally feature in information searching, restaurant reviewing, reservation making, location navigating, and other restaurant related services. The primary purpose of this research is to investigate customers' motivations in using restaurant search mobile applications (i.e., apps). This study in particular examines a research model that extends Technology Acceptance Model (TAM) incorporating motivational factors that link to customers' intention to use restaurant search apps. Questionnaires will be distributed to dining customers in New Zealand and China to determine whether the hypothesized relationships are stable regardless of national differences.
L-band transmitter using Kahn EER technique

This paper describes a 20-W peak-envelope power linear L-band transmitter based upon the Kahn envelope-elimination-and-restoration technique. A double envelope-feedback loop assures high linearity. The radio-frequency (RF) power amplifier employs a two-stage monolithic-microwave integrated-circuit driver amplifier and a 20-W power amplifier biased for class-AB operation. The class-S modulator includes a high-speed comparator and 1/2-/spl mu/m heterojunction field-effect transistors in its output stage. A double envelope-feedback loop assures both high linearity and time-delay equalization for RF bandwidths to 150 kHz. With a two-tone signal, the transmitter achieves an efficiency of 56% at full power (41 dBm), and 35% at 18 dB into back-off. The third-order intermodulation distortions for a two-tone signal vary from -30 to -40 dBc over a 20-dB range of back-off. For quaternary phase-shift keying, the first and second adjacent-channel powers are -48 and -57 dBc.
MANUFACTURING AND CONTINUOUS IMPROVEMENT AREAS USING PARTIAL LEAST SQUARE PATH MODELING WITH MULTIPLE REGRESSION COMPARISON

Structural equation modeling (SEM) has traditionally been deployed in areas of marketing, consumer satisfaction and preferences, human behavior, and recently in strategic planning. These areas are considered their niches; however, there is a remarkable tendency in empirical research studies that indicate a more diversified use of the technique. This paper shows the application of structural equation modeling using partial least square (PLS-SEM), in areas of manufacturing, quality, continuous improvement, operational efficiency, and environmental responsibility in Mexico’s medium and large manufacturing plants, while using a small sample (n = 40). The results obtained from the PLS-SEM model application mentioned, are highly positive, relevant, and statistically significant. Also shown in this paper, for purposes of validity, reliability, and statistical power confirmation of PLS-SEM, is a comparative analysis against multiple regression showing very similar results to those obtained by PLS-SEM. This fact validates the use of PLS-SEM in areas of untraditional scientific research, and suggests and invites the use of the technique in diversified fields of the scientific research
Fully printed organic solar modules with bottom and top silver nanowire electrodes

One of the advantages of organic photovoltaics (OPV) over other contemporary technologies is its relative ease of processing. There are, however, very few works that have realized fully printed devices, including the bottom electrode, let alone with a scalable process in a reasonable device size (>1 cm2). In this work, design steps and optimization processes towards fully printed OPV modules with scalable processes are demonstrated for the first time. An overview on issues related to upscaling with printed electrodes is first provided. The various issues are then addressed by a rational design process supported by measurements and calculations. Finally, a set of fully printed OPV modules are fabricated using these optimized parameters that have over 3.5‐cm2 active area with 5% efficiency. For the first time, this work has also demonstrated the process compatibility of fully printed device structures with non‐fullerene acceptor systems, which enables more design opportunities for the current generation of high‐performance OPV materials.
Efficient power coupling from a 980-nm, broad-area laser to a single-mode fiber using a wedge-shaped fiber endface

A theoretical and experimental study of an efficient technique for coupling a broad-area laser to a single-mode fiber without the use of bulk optical components was carried out. The technique uses a wedge-shaped fiber endface to approximate a cylindrical lens action to correct for the phase mismatch between the curved laser beam wavefront and the planar fiber beam. A theoretical formula for the coupling efficiency in the absence of both angular and transverse misalignments is derived. By optimizing the wedge angle and the axial separation between the laser facet and the fiber endface, a maximum coupling efficiency of 47%, compared to 20% for the square endface, achieved, resulting in 15.2 mW of power in the single-mode fiber. The fabrication process consists of a simple polishing procedure requiring a wedge-shaped polishing tool and is highly reproducible. >
Existence and Global Attractivity of Pseudo Almost Periodic Solutions for Clifford-Valued Fuzzy Neural Networks with Proportional Delays

In this paper, Clifford-valued fuzzy neural networks with proportional delays, whose leakage term coefficients are also Clifford numbers, are considered. Based on the Banach fixed point theorem and differential inequality technique, we use a direct method to obtain the existence, uniqueness, and global attractivity of pseudo almost periodic solutions for the considered networks. Finally, we provide a numerical example to illustrate the feasibility of our results. Our results are new.
Human TRMT112-Methyltransferase Network Consists of Seven Partners Interacting with a Common Co-Factor

Methylation is an essential epigenetic modification mainly catalysed by S-Adenosyl methionine-dependent methyltransferases (MTases). Several MTases require a cofactor for their metabolic stability and enzymatic activity. TRMT112 is a small evolutionary conserved protein that acts as a co-factor and activator for different MTases involved in rRNA, tRNA and protein methylation. Using a SILAC screen, we pulled down seven methyltransferases—N6AMT1, WBSCR22, METTL5, ALKBH8, THUMPD2, THUMPD3 and TRMT11—as interaction partners of TRMT112. We showed that TRMT112 stabilises all seven MTases in cells. TRMT112 and MTases exhibit a strong mutual feedback loop when expressed together in cells. TRMT112 interacts with its partners in a similar way; however, single amino acid mutations on the surface of TRMT112 reveal several differences as well. In summary, mammalian TRMT112 can be considered as a central “hub” protein that regulates the activity of at least seven methyltransferases.