Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a Unified Framework of Efficient Algorithms for Numerical Optimal Robot Control

In the recent past, the wide availability of digital technologies has strongly disrupted many well-established manufacturing techniques and initiated a rapid transformation process in the corresponding industry sectors. However, there are some domains which to date seem to have less profited from digitization. One such domain is building construction and civil engineering. The emerging research field digital fabrication promises a revolution in the construction industry through digitization and robotization of design and manufacturing processes and exhibits a great potential for novel construction technologies and architectural approaches. In this thesis, we introduce the concept of an In situ Fabricator, a versatile and flexible mobile robot dedicated to on-site fabrication. We present a prototype system, show several example applications and motivate the necessity of high-performance optimal control and estimation algorithms for achieving desired construction goals. Such methods enable non-expert users to operate complex robotic systems and are an important building block for advanced autonomous capabilities. For this reason, the main objective of this work is the development of efficient optimal control algorithms and software for high-dimensional robotic systems. We introduce a family of multiple shooting algorithms which exploit the sparsity of the optimal control problem and offer interesting properties for motion planning and nonlinear model predictive control. We derive equalityconstrained versions of these algorithms and demonstrate their potential for motion planning and real-time control of non-holonomic vehicles. To facilitate our implementations and speed up our algorithms, in particular to minimize the time required for computing gradient information, we introduce automatic differentiation and code generation for rigid body systems. Our customized solvers combined with derivative code generation and state-of-the-art software engineering form a framework which allows us to perform nonlinear model predictive control for longer time horizons or at higher update rates than other approaches.
Pengembangan Situs E-Learning Dengan Moodle Versi 3.1 Sebagai Media Pembelajaran Pada Program Studi Pendidikan Guru Madrasah Ibtidaiyah

Penelitian ini bertujuan untuk membuat situs e-learning dengan Moodle versi 3.1. dan mengkaji hasil penggunaannya sebagai media pembelajaran pada Program Studi Pendidikan Guru Madrasah Ibtidaiyah. Penelitian ini menggunakan desain penelitian dan pengembangan (research and development) untuk menghasilkan sebuah situs e-learning. Dosen dan mahasiswa mendaftar pada situs e-learning dilakukan secara mandiri dengan e-mail. Pengguna yang berhasil mendaftar pada situs e-learning akan mendapatkan peran authentical user (pengguna terdaftar). Authentical user dapat mengikuti sebuah mata kuliah dengan cara mendaftar menggunakan sandi mata kuliah. Administrator memberikan peran Manager kepada akun dosen agar ia dapat mandiri dalam membuat dan mendaftarkan dirinya sebagai pengajar pada sebuah halaman mata kuliah. Fitur-fitur untuk menyajikan bahan ajar terdiri dari format buku, halaman tunggal, file, folder, dan URL. Aktivitas pembelajaran yang tersedia adalah forum diskusi, penugasan online, ujian online, feedback, dan kuesioner online. Rata-rata skor ahli media menyatakan bahwa kualitas e-learning berada pada kategori baik dan para pengguna percaya bahwa e-larning efektif digunakan sebagai media pembelajaran. Produk yang dikembangkan telah memenuhi saran ahli media dan para pengguna e-learning.
Diverse Economic Patterns in the North Baltic Sea Region in the Late Neolithic and Early Metal Periods

Over 120 prehistoric pottery sherds from mainland Finland and the Åland Islands in the north Baltic region were studied for their organic residue content. Preserved fat residues found in these vessels indicated that the food procurement pattern was broad during the Neolithic and Early Metal periods. Based on previous research and these results, it appears that animal husbandry came to Finland with the Corded Ware culture. Groups using the succeeding Late Neolithic Kiukainen Ware did not, however, practice animal husbandry to any great extent, as there is an indication of dairy fats in only a single sherd. In general, even after dairy farming arrived in the area, prehistoric groups in southern and south-western Finland continued or returned to a hunter-gatherer lifestyle. During the Early Metal period, animal husbandry increased in importance among the groups living in the area, and the level of dairying then intensified.
Leveraging Overhead Imagery for Localization, Mapping, and Understanding

OF DISSERTATION Leveraging Overhead Imagery for Localization, Mapping, and Understanding Ground-level and overhead images provide complementary viewpoints of the world. This thesis proposes methods which leverage dense overhead imagery, in addition to sparsely distributed ground-level imagery, to advance traditional computer vision problems, such as ground-level image localization and fine-grained urban mapping. Our work focuses on three primary research areas: learning a joint feature representation between ground-level and overhead imagery to enable direct comparison for the task of image geolocalization, incorporating unlabeled overhead images by inferring labels from nearby ground-level images to improve image-driven mapping, and fusing ground-level imagery with overhead imagery to enhance understanding. The ultimate contribution of this thesis is a general framework for estimating geospatial functions, such as land cover or land use, which integrates visual evidence from both ground-level and overhead image viewpoints.
Hidden Markov Models

A hidden Markov model (HMM) is a system that evolves over time with probabilistic Markov dynamics, and whose states are hidden (unobservable). One of the most studied applications of hidden Markov models is the recognition of patterns in spoken language to identify words. This is the main purpose of speech recognition theory. HMM theory has evolved to include many other fields such as biosciences, computer vision, communications, acoustics, and finance. This chapter presents an introduction to hidden Markov models and is organized as follows. The first section introduces the fundamentals of hidden Markov models. The next section discusses three fundamental problems and their applications: 1. The evaluation problem: given a model and a sequence of observations, how do we compute the probability of the sequence (likelihood of the sequence given the model)? 2. The decoding problem (most probable explanation): given a model and a sequence of observations, what is the optimal, in some meaningful sense, sequence of states that produced the observations? 3. The estimation problem: given a sequence of observations, how do we adjust the model parameters to maximize the probability of getting the sequence? The third section presents an application of hidden Markov models in computational biology. Keywords: hidden Markov model; probabilistic Markov dynamics; emission matrix; probabilistic dependence assumptions
Improving Optical Myography via Convolutional Neural Networks

In order to improve the accuracy and reliability of myocontrol (control of prosthetic devices using signals gathered from the human body), novel kinds of sensors able to detect muscular activity are being explored. In particular, Optical Myography (OMG) consists of optically tracking and decoding the deformations happening at the surface of the body whenever muscles are activated. OMG potentially requires no devices to be worn, but since it is an advanced problem of computer vision, it incurs a number of other drawbacks, e.g., changing illumination, identification of markers, frame tear and drop. In this work we propose an improvement to OMG as it has been recently introduced, namely we relax the need of precise positioning and orientation of the markers on the body surface. The small size of the markers and their curvature while adhering to the surface of the forearm can lead to missed detections and misdetections in their orientation; here we rather detect the deformations by applying a Convolutional Neural Network to the region of interest around the feature source segmented, from the forearm. The classification-based approach yields results similar to those obtained by other classification based modalities, reaching accuracies in the range of 96.21% to 99.30% when performed on 10 intact subjects.
Detecting new Chinese words from massive domain texts with word embedding

Textual information retrieval (TIR) is based on the relationship between word units. Traditional word segmentation techniques attempt to discern the word units accurately from texts; however, they are unable to appropriately and efficiently identify all new words. Identification of new words, especially in languages such as Chinese, remains a challenge. In recent years, word embedding methods have used numerical word vectors to retain the semantic and correlated information between words in a corpus. In this article, we propose the word-embedding-based method (WEBM), a novel method that combines word embedding and frequent n-gram string mining for discovering new words from domain corpora. First, we mapped all word units in a domain corpus to a high-dimension word vector space. Second, we used a frequent n-gram word string mining method to identify a set of candidates for new words. We designed a pruning strategy based on the word vectors to quantify the possibility of a word string being a new word, thereby allowing the evaluation of candidates based on the similarity of word units in the same string. In a comparative study, our experimental results revealed that WEBM had a great advantage in detecting new words from massive Chinese corpora.
Analyzing Heterogeneity in Neuroimaging with Probabilistic Multivariate Clustering Approaches

Automated quantitative neuroimaging analysis methods have been crucial in elucidating normal and pathological brain structure and function, and in building in vivo markers of disease and its progression. Commonly used methods can identify and precisely quantify subtle and spatially complex imaging patterns of brain change associated with brain diseases. However, the overarching premise of these methods is that the disease group is a homogeneous entity resulting from a single, unifying pathophysiological process that has a single imaging signature. This assumption ignores ample evidence for the heterogeneous nature of neurodegenerative diseases and neuropsychiatric disorders, resulting in incomplete or misleading descriptions. Accurate characterization of heterogeneity is important for deepening our understanding of neurobiological processes, thus leading to improved disease diagnosis and prognosis. In this thesis, we leveraged machine learning techniques to develop novel tools that can analyze the heterogeneity in both cross-sectional and longitudinal neuroimaging studies. Specifically, we developed a semi-supervised clustering method for characterizing heterogeneity in cross-sectional group comparison studies, where normal and patient populations are modeled as high-dimensional point distributions, and heterogeneous disease effects are captured by estimating multiple transformations that align the two distributions, while accounting for the effect of nuisance covariates. Moreover, toward dissecting the heterogeneity in longitudinal cohorts, we proposed a method which simultaneously fits multiple population longitudinal multivariate trajectories and clusters subjects into subgroups. Longitudinal trajectories are modeled using spatiotemporally regularized cubic splines, while clustering is performed by assigning subjects to the subgroup whose population trajectory best fits their data. The proposed tools were extensively validated using synthetic data. Importantly, they were applied to study the heterogeneity in large clinical neuroimaging cohorts. We identified four disease subtypes with distinct imaging signatures using data from Alzheimer’s Disease Neuroimaging Initiative, and revealed two subgroups with different longitudinal patterns using data from Baltimore Longitudinal Study on Aging. Critically, we were able to further characterize the subgroups in each of the studies by performing statistical analyses evaluating subgroup differences with additional information such as neurocognitive data. Our results demonstrate the strength of the developed methods, and may pave the road for a broader understanding of the complexity of brain aging and Alzheimer’s disease. Degree Type Dissertation This dissertation is available at ScholarlyCommons: https://repository.upenn. edu/edissertations/3041 Degree Name Doctor of Philosophy (PhD) Graduate Group Electrical & Systems Engineering First Advisor Christos Davatzikos
A CORDIC based QR Decomposition Technique for MIMO Detection

CORDIC based improved real and complex QR Decomposition (QRD) for channel pre-processing operations in (Multiple-Input Multiple-Output) MIMO detectors are presented in this paper. The proposed design utilizes pipelining and parallel processing techniques and reduces the latency and hardware complexity of the module respectively. Computational complexity analysis report shows the superiority of our module by 16% compared to literature. The implementation results reveal that the proposed QRD takes shorter latency compared to literature. The power consumption of 2x2 real channel matrix and 2x2 complex channel matrix was found to be 12mW and 44mW respectively on the state-of-the-art Xilinx Virtex 5 FPGA.
Autonomy in Rehabilitation Robotics: An Intersection

Within the field of human rehabilitation, robotic machines are used both to rehabilitate the body and to perform functional tasks. Robotics autonomy able to perceive the external world and reason about high-level control decisions, however, seldom is present in these machines. For functional tasks in particular, autonomy could help to decrease the operational burden on the human and perhaps even to increase access-and this potential only grows as human motor impairments become more severe. There are however serious, and often subtle, considerations to introducing clinically-feasible robotics autonomy to rehabilitation robots and machines. Today the fields of robotics autonomy and rehabilitation robotics are largely separate. The topic of this article is at the intersection of these fields: the introduction of clinically-feasible autonomy solutions to rehabilitation robots, and opportunities for autonomy within the rehabilitation domain.
Spt5 C-terminal repeat domain phosphorylation and length negatively regulate heterochromatin through distinct mechanisms

Heterochromatin is a condensed chromatin structure that represses transcription of repetitive DNA elements and developmental genes, and is required for genome stability. Paradoxically, transcription of heterochromatic sequences is required for establishment of heterochromatin in diverse eukaryotic species. As such, components of the transcriptional machinery can play important roles in establishing heterochromatin. How these factors coordinate with heterochromatin proteins at nascent heterochromatic transcripts remains poorly understood. In the model eukaryote Schizosaccharomcyes pombe (S. pombe), heterochromatin nucleation can be coupled to processing of nascent transcripts by the RNA interference (RNAi) pathway, or to other post-transcriptional mechanisms that are RNAi-independent. Here we show that the RNA polymerase II processivity factor Spt5 negatively regulates heterochromatin in S. pombe through its C-terminal domain (CTD). The Spt5 CTD is analogous to the CTD of the RNA polymerase II large subunit, and is comprised of multiple repeats of an amino acid motif that is phosphorylated by Cdk9. We provide evidence that genetic ablation of Spt5 CTD phosphorylation results in aberrant RNAi-dependent nucleation of heterochromatin at an ectopic location, as well as inappropriate spread of heterochromatin proximal to centromeres. In contrast, truncation of Spt5 CTD repeat number enhanced RNAi-independent heterochromatin formation and bypassed the requirement for RNAi. We relate these phenotypes to the known Spt5 CTD-binding factor Rtf1. This separation of function argues that Spt5 CTD phosphorylation and CTD length restrict heterochromatin through unique mechanisms. More broadly, our findings argue that Spt5 CTD repeat length and phosphorylation have distinct regulatory effects on transcription.
Identification and validation of immunotherapy for four novel clusters of colorectal cancer based on the tumor microenvironment

The incidence and mortality of colorectal cancer (CRC) are increasing year by year. The accurate classification of CRC can realize the purpose of personalized and precise treatment for patients. The tumor microenvironment (TME) plays an important role in the malignant progression and immunotherapy of CRC. An in-depth understanding of the clusters based on the TME is of great significance for the discovery of new therapeutic targets for CRC. We extracted data on CRC, including gene expression profile, DNA methylation array, somatic mutations, clinicopathological information, and copy number variation (CNV), from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) (four datasets—GSE14333, GSE17538, GSE38832, and GSE39582), cBioPortal, and FireBrowse. The MCPcounter was utilized to quantify the abundance of 10 TME cells for CRC samples. Cluster repetitive analysis was based on the Hcluster function of the Pheatmap package in R. The ESTIMATE package was applied to compute immune and stromal scores for CRC patients. PCA analysis was used to remove batch effects among different datasets and transform genome-wide DNA methylation profiling into methylation of tumor-infiltrating lymphocyte (MeTIL). We evaluated the mutation differences of the clusters using MOVICS, DeconstructSigs, and GISTIC packages. As for therapy, TIDE and SubMap analyses were carried out to forecast the immunotherapy response of the clusters, and chemotherapeutic sensibility was estimated based on the pRRophetic package. All results were verified in the TCGA and GEO data. Four immune clusters (ImmClust-CS1, ImmClust-CS2, ImmClust-CS3, and ImmClust-CS4) were identified for CRC. The four ImmClusts exhibited distinct TME compositions, cancer-associated fibroblasts (CAFs), functional orientation, and immune checkpoints. The highest immune, stromal, and MeTIL scores were observed in CS2, in contrast to the lowest scores in CS4. CS1 may respond to immunotherapy, while CS2 may respond to immunotherapy after anti-CAFs. Among the four ImmClusts, the top 15 markers with the highest mutation frequency were acquired, and CS1 had significantly lower CNA on the focal level than other subtypes. In addition, CS1 and CS2 patients had more stable chromosomes than CS3 and CS4. The most sensitive chemotherapeutic agents in these four ImmClusts were also found. IHC results revealed that CD29 stained significantly darker in the cancer samples, indicating that their CD29 was highly expressed in colon cancer. This work revealed the novel clusters based on TME for CRC, which would guide in predicting the prognosis, biological features, and appropriate treatment for patients with CRC.
The Cultivation of Entrepreneurship Values in Children Aged 5-6 Years at TK Khalifah Samarinda

This study aims to determine the learning of the cultivation of entrepreneurial values in children aged 5-6 years in TK Khalifah Samarinda and the obstacles experienced in the learning strategy of planting entrepreneurial values in children aged 5-6 years in kindergarten. Khalifah Samarinda. This type of research is qualitative research with a descriptive approach. The data collected comes from interview scripts, field notes and documentation. The focus of this research relates to the activities of teachers and students in the TK Khalifah Samarinda. The results of this study indicate that the cultivation of entrepreneurial values in children aged 5-6 years is good enough and in accordance with children's needs. Embedded educational values are (1) market day (bazaar) activities (2) the values of independence that are embedded in children are very helpful to children in their daily habits, (3) discipline values, success here is very visible in children with seriousness in performing prayers and reciting, (4) the values of self-confidence, success in the form of courageous attitude to appear in public and not inferior to others show their leadership attitude, and (5) values of responsibility, success in the form of attitude that is always answer to what is done and done by each child. the conclusion in this study is that the teacher's approach to instilling entrepreneurial values in children aged 5-6 years in the TK Khalifah Samarinda has referred to the development of children, where they were not used to being independent, disciplined, confident and responsible now becoming accustomed to the values values that have been instilled by teachers at school and at home and the surrounding environment.
EFFECTIVENESS OF TEACHING METHODS IN INVOLVING STUDENTS: PRACTICAL DIGITAL EXPERIENCES AND GAMIFICATION

The Bologna Process was a European initiative to bring about transparency and compatibility of higher education across Europe. It involved the establishment of a European Higher Education Area (EHEA) by 2010. Although much has been said about the virtues and drawbacks of this profound reform of the entire academic ecosystem, there’s still a feature which has deserved little attention: the involvement of students in their entire process of teaching and learning at college stage. The objective of this study is to investigate the differential effectiveness of teaching methods on students' academic performance. Research shows that Quality teaching tends necessarily to be student-centred. It aims to help most -if not all-students learning. Therefore, focus should not only be on pedagogical skills, but rather on the available tools in the teaching-learning process, as well as the way teenagers think and conduct themselves nowadays. There is a great need of new teaching methods and pedagogies to meet these circumstances. Hence, we can say that there is also a need of change in the learner and teachers’ means of interaction. This paper addresses the role of professors in this process, a process requires a whole shift in the way teaching has traditionally been understood. Based upon our current experience in Centro Universitario Villanueva, we present a comprehensive research-driven perspective on what we think digital technologies and gamification can achieve in Law studies. Specifically, and given the teacher plays a vital role in this scenario, we show that a new “teaching style” can contribute to academic excellence in three ways: 1) by enhancing students’ attention in class as well as motivating their interest for the topics discussed; 2) by making the experience of learning something that, if it may be not be funny, doesn’t necessarily has to be boring; 3) by explaining why, given the world is changing, and so are our students, teachers need to be technology savvies as well, in order to meet new global emerging demands.