Miracle-dz/UniFAR-FTUs huggingface dataset:
Star Formation in Space and Time: Taurus-Auriga

To understand the formation of stellar groups, one must first document carefully the birth pattern within real clusters and associations. In this study of Taurus-Auriga, we combine pre-main-sequence ages from our own evolutionary tracks with stellar positions from observational surveys. Aided by the extensive millimeter data on the molecular clouds, we develop a picture of the region's history. Star formation began, at a relatively low level and in a spatially diffuse manner, at least 107 yr in the past. Within the last few million years, new stars have been produced at an accelerating rate, almost exclusively within a confined group of striated cloud filaments. The gas both inside and around the filaments appears to be in force balance. Thus, the appearance of the filaments is due to global, quasi-static contraction of the parent cloud material. Gravity drives this contraction and shock dissipation mediates it, but the internal motion of the gas does not appear to be turbulent. The accelerating nature of recent star formation means that the condensation of cloud cores is a threshold phenomenon, requiring a minimum background density. Other, nearby cloud regions, including Lupus and Chamaeleon, contain some locales that have attained this density, and others that have not. In the latter, we find extensive and sometimes massive molecular gas that is still devoid of young stars.
Techniques applied in design optimization of parallel manipulators

There are some key advantages associated with parallel robots, which have warranted their continued research and wide application in both university laboratories and industry. To obtain a parallel manipulator with good properties, as customized by user specifications, the design parameters of a parallel manipulator must be optimized. The optimal design of the general parallel manipulator's kinematic parameters may be decomposed into two processes: structural synthesis and dimensional synthesis. Although both these processes will be described, the scope of this paper explores the dimensional synthesis aspect. Historical optimization methods adopted by researchers are discussed. This paper presents dimensional synthesis approaches based on performance requirements that have a potential to obtain almost all feasible design solutions that satisfy the requirements. The optimal design problem is a constrained nonlinear optimization problem with no explicit analytical expression. This makes the process of optimization a cumbersome and time-consuming endeavour, especially when the variables are diverse and objective functions are excessively complex. Thus, several techniques devised by researchers to solve the problem are reviewed in this paper.
Functional linear discriminant analysis for irregularly sampled curves

We introduce a technique for extending the classical method of linear discriminant analysis (LDA) to data sets where the predictor variables are curves or functions. This procedure, which we call functional linear discriminant analysis (FLDA), is particularly useful when only fragments of the curves are observed. All the techniques associated with LDA can be extended for use with FLDA. In particular FLDA can be used to produce classifications on new (test) curves, give an estimate of the discriminant function between classes and provide a one‐ or two‐dimensional pictorial representation of a set of curves. We also extend this procedure to provide generalizations of quadratic and regularized discriminant analysis.
Exploring Network Structure, Dynamics, and Function using NetworkX

NetworkX is a Python language package for exploration and analysis of networks and network algorithms. The core package provides data structures for representing many types of networks, or graphs, including simple graphs, directed graphs, and graphs with parallel edges and self-loops. The nodes in NetworkX graphs can be any (hashable) Python object and edges can contain arbitrary data; this flexibility makes NetworkX ideal for representing networks found in many dierent scientific fields. In addition to the basic data structures many graph algorithms are implemented for calculating network properties and structure measures: shortest paths, betweenness centrality, clustering, and degree distribution and many more. NetworkX can read and write various graph formats for easy exchange with existing data, and provides generators for many classic graphs and popular graph models, such as the Erdos-Renyi, Small World, and Barabasi-Albert models. The ease-of-use and flexibility of the Python programming language together with connection to the SciPy tools make NetworkX a powerful tool for scientific computations. We discuss some of our recent work studying synchronization of coupled oscillators to demonstrate how NetworkX enables research in the field of computational networks.
School climate, victimization, and mental health outcomes among elementary school students in China

Although school climate has been identified as a protective factor for youth development in the United States, few longitudinal studies have examined the relationship between school climate and student outcomes in China. This study explored the relationship between school climate, victimization, covitality, internalizing symptoms, and academic achievement, and whether school climate moderated the relationship between victimization and mental health outcomes using longitudinal data. Survey data were collected from 1150 Chinese 3rd to 6th grade students (Mage = 10.27 years, SD = 1.03 years, 55% boys) from five elementary schools at two time points. Regression results showed that school climate factors, including student-teacher relationships, clear expectations, respect for diversity and fairness of rules, predicted victimization, mental health (both internalizing symptoms and covitality), and academic grades six months later. School climate did not moderate the relationship between victimization and mental health. Our results suggest that it is important to foster positive school climate in order to prevent bullying and promote positive youth development among elementary students in China.
Decoding Generalized Reed-Solomon Codes and Its Application to RLCE Encryption Schemes

This paper compares the efficiency of various algorithms for implementing quantum resistant public key encryption scheme RLCE on 64-bit CPUs. By optimizing various algorithms for polynomial and matrix operations over finite fields, we obtained several interesting (or even surprising) results. For example, it is well known (e.g., Moenck 1976 \cite{moenck1976practical}) that Karatsuba's algorithm outperforms classical polynomial multiplication algorithm from the degree 15 and above (practically, Karatsuba's algorithm only outperforms classical polynomial multiplication algorithm from the degree 35 and above ). Our experiments show that 64-bit optimized Karatsuba's algorithm will only outperform 64-bit optimized classical polynomial multiplication algorithm for polynomials of degree 115 and above over finite field $GF(2^{10})$. The second interesting (surprising) result shows that 64-bit optimized Chien's search algorithm ourperforms all other 64-bit optimized polynomial root finding algorithms such as BTA and FFT for polynomials of all degrees over finite field $GF(2^{10})$. The third interesting (surprising) result shows that 64-bit optimized Strassen matrix multiplication algorithm only outperforms 64-bit optimized classical matrix multiplication algorithm for matrices of dimension 750 and above over finite field $GF(2^{10})$. It should be noted that existing literatures and practices recommend Strassen matrix multiplication algorithm for matrices of dimension 40 and above. All our experiments are done on a 64-bit MacBook Pro with i7 CPU and single thread C codes. It should be noted that the reported results should be appliable to 64 or larger bits CPU architectures. For 32 or smaller bits CPUs, these results may not be applicable. The source code and library for the algorithms covered in this paper are available at this http URL.
A Dynamic Algorithm for the Longest Common Subsequence Problem using Ant Colony Optimization Technique

We present a dynamic algorithm for solving the Longest Common Subsequence Problem using Ant Colony Optimization Technique. The Ant Colony Optimization Technique has been applied to solve many problems in Optimization Theory, Machine Learning and Telecommunication Networks etc. In particular, application of this theory in NP-Hard Problems has a remarkable significance. Given two strings, the traditional technique for finding Longest Common Subsequence is based on Dynamic Programming which consists of creating a recurrence relation and filling a table of size . The proposed algorithm draws analogy with behavior of ant colonies function and this new computational paradigm is known as Ant System. It is a viable new approach to Stochastic Combinatorial Optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence and greedy heuristic helps find acceptable solutions in minimum number of stages. We apply the proposed methodology to Longest Common Subsequence Problem and give the simulation results. The effectiveness of this approach is demonstrated by efficient Computational Complexity. To the best of our knowledge, this is the first Ant Colony Optimization Algorithm for Longest Common Subsequence Problem.
On the Computational Power of RNNs

Recent neural network architectures such as the basic recurrent neural network (RNN) and Gated Recurrent Unit (GRU) have gained prominence as end-to-end learning architectures for natural language processing tasks. But what is the computational power of such systems? We prove that finite precision RNNs with one hidden layer and ReLU activation and finite precision GRUs are exactly as computationally powerful as deterministic finite automata. Allowing arbitrary precision, we prove that RNNs with one hidden layer and ReLU activation are at least as computationally powerful as pushdown automata. If we also allow infinite precision, infinite edge weights, and nonlinear output activation functions, we prove that GRUs are at least as computationally powerful as pushdown automata. All results are shown constructively.
Improving Web Page Prediction Using Default Rule Selection

Mining user patterns of web log files can provide significant and useful informative knowledge. A large amount of research has been done in trying to predict correctly the pages a user will most likely request next. Markov models are the most commonly used approaches for this type of web access prediction. Web page prediction requires the development of models that can predict a user’s next access to a web server. Many researchers have proposed a novel approach that integrates Markov models, association rules and clustering in web site access predictability. The low order Markov models provide higher coverage, but these are couched in ambiguous rules. In this paper, we introduce the use of default rule in resolving web access ambiguous predictions. This method could provide better prediction than using the individual traditional models. The results have shown that the default rule increases the accuracy and model-accuracy of web page access predictions. It also applies to association rules and the other combined models.
Analysis of pediatric obstructive nephropathy using protein antibody arrays and computational techniques

Obstructive Nephropathy (ON) is a renal disease quite frequent in newborns and children. Although it is caused by the improper flow of urine, the nephron destruction can be magnified by various molecular processes. In the current work, we aim to study 725 antibodies spotted on antibody arrays for a set of 20 pediatric subjects divided into four groups according to ON status: control, children with mild obstruction and two groups of children with severe obstruction. To this end, Gene ARMADA, an integrated platform for microarray data analysis, was employed to identify 43 differentially expressed antibodies in urine along the four subject groups and cluster these into groups of similarly expressed proteins. The differentially expressed proteins were then related to Gene Ontology terms (GOTs) and over-presented GOTs were identified.
Continuous Orchestration of Web Services via Planning

The service-oriented paradigm is rapidly emerging as the key approach to develop distributed business applications. Its enactment requires the ability to automatically coordinate existing services to realize novel and powerful desired functionalities, and planning-based solutions have proved to be strong candidates for this hard task. However, no current approach can satisfactorily coordinate stateful services that evolve continuously and indefinitely in an asynchronous way, such as e.g. notification services made increasingly available by business entities. This severely limits the practical applicability of current solutions. In this paper, we provide for the first time a full-fledged planning-based solution to the problem of continuously orchestrating stateful asynchronous services. To do so, we adopt a simple yet expressive requirement language, and we devise a novel planning algorithm that solves preference-ordered maintenability goals in the presence of exogenous events. Our approach is correct and complete, and our tests on a symbolic BDD-based implementation witness its ability and effectiveness in dealing with significant and realistic scenarios which no other current approach can tackle.
On Bandwidth Constrained Distributed Detection of a Deterministic Signal in Correlated Noise.

We consider a Neyman-Pearson (NP) distributed binary detection problem in a bandwidth constrained wireless sensor network, where the fusion center (FC) is responsible for fusing signals received from sensors and making a final decision about the presence or absence of a signal source in correlated Gaussian noises. Given this signal model, our goals are (i) to investigate whether or not randomized transmission can improve detection performance, under communication rate constraint, and (ii) to explore how the correlation among observation noises would impact performance. To achieve these goals, we propose two novel schemes that combine the concepts of censoring and randomized transmission (which we name CRT-I and CRT-II schemes) and compare them with the pure censoring scheme. In CRT (pure censoring) schemes, we map randomly (deterministically) a sensor's observation to a ternary transmit symbol $u_k \in \{-1,0,1\}$ where ``$0$'' corresponds to no transmission (sensor censors). Assuming sensors transmit $u_k$'s over orthogonal fading channels, we formulate and address two system-level constrained optimization problems: in the first problem we minimize the probability of miss detection at the FC, subject to constraints on the probabilities of transmission and false alarm at the FC; in the second (dual) problem we minimize the probability of transmission, subject to constraints on the probabilities of miss detection and false alarm at the FC. Based on the expressions of the objective functions and the constraints in each problem, we propose different optimization techniques to address these two problems. Through analysis and simulations, we explore and provide the conditions (in terms of communication channel signal-to-noise ratio, degree of correlation among sensor observation noises, and maximum allowed false alarm probability) under which CRT schemes outperform pure censoring scheme.
The laserROM project: a case study in document processing systems

Each year, Hewlett-Packard maintains, updates, and distributes over 8000 unique publications which are used by thousands of customers worldwide. This translates into hundreds of thousands of pages. Documentation distribution is now the province of an entire operation within HP. Because the effective use of HP products depends on the effective use of supporting publications and manuals, HP is constantly trying to improve their documentation distribution. Early in 1987, the compact disc audio (CD-Audio) market was quickly maturing. At the same time, an offshoot of this technology was just beginning, called Compact Disc-Read Only Memory (CD-ROM). The Application Support Division at Hewlett-Packard realized that this new technology for data storage might make a significant impact on the problem of documentation distribution. It could provide large amounts of documentation and support information to HP's customers in a timely, reliable, and consistent manner. This would, in turn, increase the usefulness of HP's documentation, thereby helping the customer make better support decisions and solidifying our relationship with them. Thus, the LaserROM project was created to determine how to put the thousands of publications produced by Hewlett-Packard onto CD-ROM. CD-ROM was seen as a viable delivery vehicle for a number of reasons. Mastering costs run $3,000 to $5,000 for the first few discs, and then pressing of additional discs costs approximately three to ten dollars per disc.The storage capacity of a CD-ROM is greater than
Integrating, Execution, Planning, and Learning in Soar for External Environments

Three key components of an autonomous intelligent system are planning, execution, and learning. This paper describes how the Soar architecture supports planning, execution, and learning in unpredictable and dynamic environments. The tight integration of these components provides reactive execution, hierarchical execution, interruption, on demand planning, and the conversion of deliberate planning to reaction. These capabilities are demonstrated on two robotic systems controlled by Soar, one using a Puma robot arm and an overhead camera, the second using a small mobile robot with an arm.
Reading Anxiety: An Early Affective Impediment to Children’s Success in Reading

ABSTRACT Learning to read is a critical but often challenging academic task for young children. In the current study, we explore the relation between children’s reading affect-–particularly anxiety-–and reading achievement in the early years of reading acquisition. We hypothesized that reading anxiety would relate to reading achievement across the school year and that boys and girls might show differential patterns in the relation between reading anxiety and achievement due to the common stereotype that boys underperform in reading. A sample of first and second grade students completed measures of reading anxiety, positive reading affect, math achievement, and reading achievement in the fall and spring. Results show that reading anxiety and reading achievement share a bi-directional relation in which fall reading anxiety predicts spring reading achievement and vice versa. Furthermore, the pattern of relation between reading anxiety and achievement differs by gender: boys appear more susceptible to the reciprocal damaging effects of reading anxiety on reading achievement across the school year. Finally, reading achievement shares a stronger relation with reading anxiety than with positive reading affect, perhaps because of the phenomenon in which negative relative to positive experiences have a greater psychological impact.
Who Is a Part-Time Worker Around the World and Why Does It Matter? Examining the Quality of Employment Measures and Workers’ Perceived Job Quality

ABSTRACT Theory and research do not fully account for the cross-national variation in part-time work definitions and measures, which may affect conclusions. Using the 2004 to 2009 International Social Survey Programme (ISSP) as a special case, this study analyzes the reliability of different part-time work measures for 47 countries, finding high levels of consistency between what resembles a country-specific measure and measures based on 30- and 35-hr thresholds. Bivariate analyses reveal that sex is consistently related to—and suggestive of the construct validity of—part-time measures, except in some non–Organisation for Economic Co-operation and Development (OECD) countries. Analyses of selected countries in 1 year indicate that the choice of part-time work measure matters for understanding workers’ perceptions of their earnings, advancement opportunities, and job security in a few countries. Our results yield good news for ISSP users but highlight the need for the more exhaustive conceptualization of part-time work we offer, research outside of the OECD, and care in interpreting data in some countries.
Development and Pilot Evaluation of a Novel Dignity-Conserving End-of-Life (EoL) Care Model for Nursing Homes in Chinese Societies.

PURPOSE OF THE STUDY The provision of end-of-life (EoL) care in long-term-care settings remains largely underdeveloped in most Chinese societies, and nursing home residents often fail to obtain good care as they approach death. This paper systematically describes the development and implementation mechanisms of a novel Dignity-Conserving EoL Care model that has been successfully adopted by three nursing homes in Hong Kong and presents preliminary evidence of its effectiveness on enhancing dignity and quality of life (QoL) of terminally ill residents. DESIGN AND METHODS Nine terminally ill nursing home residents completed the McGill Quality of Life Questionnaire and the Nursing Facilities Quality of Life Questionnaire at baseline and 6 months post-EoL program enrollment. Wilcoxon signed rank test was used to detect significance changes in each QoL domains across time. RESULTS Although significant deterioration was recorded for physical QoL, significant improvement was observed for social QoL. Moreover, a clear trend toward significant improvements was identified for the QoL domains of individuality and relationships. IMPLICATIONS A holistic and compassionate caring environment, together with the core principles of family-centered care, interagency and interdisciplinary teamwork, as well as cultural-specific psycho-socio-spiritual support, are all essential elements for optimizing QoL and promoting death with dignity for nursing home residents facing morality. This study provides a useful framework to facilitate the future development of EoL care in long-term-care settings in the Chinese context.
Real-time grasp planning based on motion field graph for human-robot cooperation

We present a real-time framework for planning natural and smooth grasping motions in an online manner based on the interaction with human. The proposed framework is able to change its grasp strategies agilely according to the interaction with human. Given human demonstrations, we develop a motion field graph consisting of nodes and edges where the nodes contains reference finger poses and their time derivatives, and the edges indicates the similarity between a pair of nodes. Based on the graph, a new grasping motion can be planned that adapts to the changes of the environment and the interaction. The motion field guarantees smooth motions by integrating the velocities obtained from the demonstrations. To validate the framework, we build a demo system where a human can hand a cup over to a tele-operated robot or a virtual humanoid avatar which are controlled by an another person at a remote location. Also, the virtual avatar can grasp and manipulate a cola can.