Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of the enzyme network involved in cattle milk production using graph theory

Understanding cattle metabolism and its relationship with milk products is important in bovine breeding. A systemic view could lead to consequences that will result in a better understanding of existing concepts. Topological indices and quantitative characterizations mostly result from the application of graph theory on biological data. In the present work, the enzyme network involved in cattle milk production was reconstructed and analyzed based on available bovine genome information using several public datasets (NCBI, Uniprot, KEGG, and Brenda). The reconstructed network consisted of 3605 reactions named by KEGG compound numbers and 646 enzymes that catalyzed the corresponding reactions. The characteristics of the directed and undirected network were analyzed using Graph Theory. The mean path length was calculated to be4.39 and 5.41 for directed and undirected networks, respectively. The top 11 hub enzymes whose abnormality could harm bovine health and reduce milk production were determined. Therefore, the aim of constructing the enzyme centric network was twofold; first to find out whether such network followed the same properties of other biological networks, and second, to find the key enzymes. The results of the present study can improve our understanding of milk production in cattle. Also, analysis of the enzyme network can help improve the modeling and simulation of biological systems and help design desired phenotypes to increase milk production quality or quantity.
Learning Term Rewriting Systems from Entailment

Learning from entailment is a learning model suitable for learning (cid:12)rst-order formulas, in which examples are formulas in the (cid:12)rst-order language which are implied or not implied. In this paper, we study the learnability of term rewriting systems in this framework. A term rewriting system is a simple model of tree transduction used for semi-structured data and Web, which can be considered as a fragment of (cid:12)rst-order equational logic with a binary predicate). First, we present a generic algorithm using entailment equivalence queries and request for hint queries, and show that the class of terminating term rewriting systems is exactly learnable by this algorithm. Next, we present a polynomial time learning algorithm using entailment equivalence and entailment membership queries for another subclass k-variable linear tree translations with top-down rewriting strategy.
The law of proportionate effect and OECD bank sectors

The paper investigates the growth dynamics of the bank sectors in the OECD area over the period 1985–1994 and examines whether the structural financial reforms of the late 1980s have affected their growth path. Based on a test of Gibrat's law of proportionate effect, it is found that the 1985–89 period was characterized by size convergence, implying that smaller bank sectors were expanding more rapidly. However, in the 1990–1994 period the pattern reversed to proportionate growth. The analysis of the determinants of bank market growth reveals that macroeconomic growth, operational bank efficiency, credit quality, and capitalization are the main drivers of bank industry growth.
Obstacle Avoidance for Autonomous Mobile Robots based on Position Prediction using Fuzzy Inference

In the future, it’s not difficult to image that we will often come across many autonomous mobile robots traversing densely populated place we live in. In such situation, because the autonomous mobile robots need to carry out their tasks in a place with unknown obstacles, the obstacle avoidance is one of the important functions of the robots. With a view to implementation of autonomous mobile robot working in doors, we employ an omnidirectional platform as shown in Fig. 1 (left). For experimental verification, an omnidirectional mobile robot shown in Fig. 1 (right) is developed. The robot has an omnidirectional camera for environmental recognition, and can move to all directions by four omni wheels. While there are many studies about obstacle avoidance method focusing attention on possibility of avoidance, this paper presents the method focusing on not only possibility but also safer trajectory of avoidance. Even if there are the same situations that the robot needs to avoid a static obstacle, timing of beginning avoidance behaviour should vary according to the robot speed. If the obstacles are moving also, the timing should vary according to the velocities of the obstacles. To cite a case, in a situation that a robot and an obstacle go by each other as shown in Fig. 2, the robot should avoid along the curved line like (iii) according to the speeds of the obstacle and own speed. In order to get to the goal with efficient and safe avoidance behaviour in the unknown environment for the robots, predicting the future obstacles’ positions by their current motions is needed. This paper introduces a real-time obstacle avoidance method introducing the velocity of obstacle relative to the robot. By means of considering predicted positions of the robot and the obstacle calculated from the time and the relative velocity, the robot can begin the avoidance behaviour at an appropriate time according to the velocity of the obstacle and the robot. Some researches focus attention on the velocity of obstacle (Ko & Lee, 1996) to avoid moving obstacles efficiently. In this research, virtual distance function is defined based on distance from the obstacle and speed of obstacle, however, only projection of the obstacle velocity on the unit vector from the obstacle to the robot is considered. In other words, the velocity of the robot is not considered.
Path Updating Tree based fast path planner for unpredictable changing environments

For changing environments, although lots of planning algorithms have focused on how to get a valid and short path, seldom planning algorithms can be employed to extract a sustaining valid path. Especially for large-scale environments, getting a sustaining valid path is required to make intelligent decisions for robots. In this paper, a method of Path Updating Tree (PUT) is proposed to get such a sustainingly valid region path, which approximately estimates the environment using extended nodes in the projected Cspace. Each extended node is used to reflect local crowding information in this paper. An approximate path, which connected goal and start, is generated based on each region's collision possibility, reachable possibility and path length. With environment changing, nodes of PUT are updated and new region paths are generated by regrowing if necessary. Then a two-level path planner is introduced with PUT and local path generator to distribute the computational resource properly. The proposed method has shown to be efficient in large-scale scenarios with crowded obstacles.
Parent-adolescent Attachment, Bullying and Victimization, and Mental Health Outcomes

Traditional and cyber bullying have been identified as universal problematic issues facing adolescents, and research is needed to understand correlates associated with these phenomena. Structural equation modeling analyses examined associations between attachment to parents, traditional and cyber bullying or victimization, and mental health outcomes among 257 high school students (Average age 15.9 years). Key patterns emerged, including associations between maternal attachment and mental health outcomes; victimization and mental health concerns; and bullying and victimization in both traditional and cyber contexts. The role of attachment to mothers and fathers varied by context. Findings extend the literature by identifying risk factors in adolescence associated with bullying and victimization, as well as suggesting appropriate prevention and intervention strategies to increase adolescent well-being.
Improved MDA, a case for de-garbling SSR mode S replies

Multilateration (MLAT) and Automatic Dependent Surveillance - Broadcast (ADS-B) systems exploiting the Secondary Surveillance Radar (SSR) channel suffer from garbling. This means that if two or more mode S signals impinge on the receiver at the same or very near time they could not be decoded. To alleviate this problem, many solutions have been proposed, one in particular [1] is effective for a large variety of scenarios, but excluding the cases when the replies are too much separated in time. Recently a paper [2] focused on linear algebra presented a potential solution for this case. In this work we present a practical application for the case of two mode S signals.
Error Resilient Video Coding Techniques Using Spare Pictures

ABSTRACT In error prone environments not all the picture data can be received correctly during video transmission. Error propagation introduced by employing predictive coding may degrade the sequence quality severely.A novel method, called spare pictures, is proposed to indicate the similarity between a reference picture andother pictures. With the help of thesignaled spare pictures information, receivers may avoid unnecessary picture freezing, feedback andcomplex error concealment, which are normally done as a response to missing picture data. Furthermore, spare pictures may also help 1) to improve the quality of some decoded pictures by replacing the reference picturesby better ones, and 2) to improve error concealment by concealing a lost block as a corresponding block in one of the spare pictures.An efficient coding method for the spare macroblock maps is also proposed. The mechanismfor signaling the spare pictureswas adopted toH.263 ve rsion 3 andITU -T H.264 | MPEG-4 AVC as Supplemental Enhancement Information [1], [2].
VLSI Design of a Fully-Parallel High-Throughput Decoder for Turbo Gallager Codes

The most powerful channel coding schemes, namely those based on turbo codes and low-density parity-check (LDPC) Gallager codes, have in common the principle of iterative decoding. However, the relative coding structures and decoding algorithms are substantially different. This paper presents a 2048-bit, rate-1/2 soft decision decoder for a new class of codes known as Turbo Gallager Codes. These codes are turbo codes with properly chosen component convolutional codes such that they can be successfully decoded by means of the decoding algorithm used for LDPC codes, i.e., the belief propagation algorithm working on the code Tanner graph. These coding schemes are important in practical terms for two reasons: (i) they can be encoded as classical turbo codes, giving a solution to the encoding problem of LDPC codes; (ii) they can also be decoded in a fully parallel manner, partially overcoming the routing congestion bottleneck of parallel decoder VLSI implementations thanks to the locality of the interconnections. The implemented decoder can support up to 1 Gbit/s data rate and performs up to 48 decoding iterations ensuring both high throughput and good coding gain. In order to evaluate the performance and the gate complexity of the decoder VLSI architecture, it has been synthesized in a 0.18 μm standard-cell CMOS technology.
Stability of Disability Among PACE Enrollees: Financial and Programmatic Implications

This article examines the experience of the first 11 Program of All-inclusive Care for the Elderly (PACE) programs. It investigates changes in functional status of participants in relation to length of enrollment in the program and individual risk characteristics. Our findings indicate that mature programs experience stable disability mix over time, supporting the rationale for the current PACE payment method. However, significant differences exist between programs, suggesting that payment rates could be more program specific. Analysis of the effect of patient characteristics at admission on the likelihood of improvement in functional status identified areas for quality improvement. The implications of this study have increasing importance in light of the expected expansion of PACE to approximately 100 sites by the year 2000.
On Optimization Algorithms for Recurrent Networks with Long Short-Term Memory

Motivation and contribution Recurrent neural networks (RNN) with long short-term memory (LSTM) are recently proposed to model sequences without prior domain knowledge [3, 6]. In these work, the authors empirically observed that RNN-LSTMs trained with vanilla optimization algorithms, such as stochastic gradient descent (SGD) with a simple learning rate annealing schedule, achieves good performances on a wide range of tasks. While this observation makes it easy to train RNN-LSTMs, it remains a question whether more advanced optimization techniques, such as AdaGrad [1] or momentum and its variations [2], can improve their performances. We attempt to answer this question. We consider two benchmark problems: (1) language modeling on Penn TreeBank and (2) named entity recognition (NER) on OntoNotes (v5.0) [5]. On these tasks, we compare the performances of RNN-LSTMs trained with various optimization algorithms such as SGD, AdaGrad and momentum. We further propose a novel optimization technique that achieves better performance on both tasks. Our preliminary results show that our method is robust against different scales of data, hence requires minimal tuning effort. For language model in particular, it leads to 7% improvement for single model perplexity. A new optimization algorithm We propose a novel training algorithm for RNN-LSTMs. Suppose we have a function f : R d → R and we want to ﬁnd a vector θ ∈ R d to minimize f ( θ ) . We randomly initialize θ 0 ∈ R d , then at time step t ≥ 1 we perform the following update
Characterizing traits of coordination

How can one recognize coordination languages and technologies? As this report shows, the common approach that contrasts coordination with computation is intellectually unsound: depending on the selected understanding of the word "computation", it either captures too many or too few programming languages. Instead, we argue for objective criteria that can be used to evaluate how well programming technologies offer coordination services. Of the various criteria commonly used in this community, we are able to isolate three that are strongly characterizing: black-box componentization, which we had identified previously, but also interface extensibility and customizability of run-time optimization goals. These criteria are well matched by Intel's Concurrent Collections and AstraKahn, and also by OpenCL, POSIX and VMWare ESX.
Advances in Cell Culture: More than a Century after Cultivating Cells

To elucidate and understand complex physiological mechanisms, in vivo research is the gold standard. However, in 1907, Harrison started the in vitro cell culture as we know today, opening a path for new assays and techniques. This was a major advance in the scientific field. The possibility to monitor cell growth, differentiation and response to any number of stimuli was a leap for drug trials and screening. More than 100 years has passed, and various cell cultures techniques were developed and perfected. Diverse culture mediums and culture conditions were elaborated to attend the scientist needs. Among those advances, three-dimensional cell culture was a major breakthrough that enables a better representation of the in vivo microenvironmental characteristics. With those continuous advances in cell culture, in vitro assays are getting more reliable providing results that better represent in vivo responses.
Valuation and hedging of OTC contracts with funding costs, collateralization and counterparty credit risk: Part 1

The research presented in this work is motivated by some recent papers regarding hedging and valuation of financial securities subject to funding costs, collateralization and counterparty credit risk. Our goal is to provide a sound theoretical underpinning for some results presented in these papers by developing a unified martingale framework for the non-linear approach to hedging and pricing of OTC financial contracts. The impact that various funding bases and margin covenants exert on the values and hedging strategies for OTC contracts is examined.
A friction-model-based framework for Reinforcement Learning of robotic tasks in non-rigid environments

Learning motion tasks in a real environment with deformable objects requires not only a Reinforcement Learning (RL) algorithm, but also a good motion characterization, a preferably compliant robot controller, and an agent giving feedback for the rewards/costs in the RL algorithm. In this paper, we unify all these parts in a simple but effective way to properly learn safety-critical robotic tasks such as wrapping a scarf around the neck (so far, of a mannequin).
An Optimized Degree Strategy for Persistent Sensor Network Data Distribution

Nodes of wireless sensor networks (WSN) may fail easily due to the lack of energy or disaster scenarios. Such failures can severely reduce the persistence and collection efficiency of sensed data. Network coding technology can be employed to enhance the data persistence of wireless sensor network, but it may cause serious "cliff effect" in decoding process. In this paper, the influence of prioritized coding degree distribution strategy on cliff effect is observed, and a distributed storage algorithm PLTD-Alpha is proposed. With PLTD-Alpha, the data in sensor network nodes present a trend that their degree distribution increase along with the degree level predefined, and the persistent data packets can be submitted to the sink node according to its degree in order. Experiment results show that PLTD-Alpha can greatly improve the data collection and decoding efficiency of sensor network while data persistence is not notably affected.
Cognitive‐Behavioral Therapy for Obsessive‐Compulsive Disorder

&NA; Two treatments have demonstrated efficacy in OCD, cognitive‐behavioral therapy and pharmacotherapy with serotonin reuptake inhibitors (SRIs). In this article, which is the second in a three‐part series, the authors discuss the role of exposure and ritual prevention (EX/RP) and cognitive therapy (CT) in the treatment of adults with OCD. They first describe EX/RP and review the studies that have demonstrated that it is a very efficacious treatment for OCD, both alone and in combination with pharmacotherapy with SRIs. The limitations of treatment with EX/RP are also described. These include high rates of patient refusal and drop‐out, the continuing presence of some residual symptoms, the subsequent loss of treatment gains, possibly reduced effectiveness in certain clinical subtypes of OCD and in OCD accompanied by other comorbid psychiatric disorders (e.g., major depressive disorder), and the shortage of therapists trained in EX/RP for OCD. Recent research to develop even more effective modifications of EX/RP therapy for OCD are also described. Finally, the authors discuss when to refer patients with OCD for EX/RP treatment. In the second half of the article, the authors describe CT for OCD and discuss how helpful it may be for OCD. They conclude that there is no convincing evidence that any particular set of cognitive procedures is as good or better than potent EX/RP, but they note problems in the research, especially in disentangling the two types of therapy, since EX/RP often involves a number of CT procedures, while CT often includes some interventions that are closely related to EX/RP. The authors describe recent efforts to refine the cognitive theory of OCD and to determine when and how to use CT in OCD. They also describe presentations of OCD for which it may be helpful to consider a referral for CT. The next and final article in this series will cover psychopharmacological treatment for OCD. (Journal of Psychiatric Practice 2000;6:59‐68)
Elementary School Office Managers' Positive Extra-role Behaviours

Elementary School Office Managers represent a workforce of over 5000 individuals within the province of Ontario. While their impact on the school is known to the principals, teachers, students, and parents with whom they interact on a regular basis, their contributions have scarcely attracted the attention of educational scholars. Through the lens of Public Service Motivation theory and Extra-Role Behaviour theory, this research sought to identify the personal and work environment influences that impact Elementary Office Managers’ Positive Extra-Role Behaviours in Ontario’s elementary schools. Through face-to-face interviews with retired elementary office managers, this research found that principal/leader supportiveness, job satisfaction, and individual characteristics were key influences on research participants’ positive extra-role behaviour. Possible implications of this research for school districts include: giving consideration to office manger applicants’ pre-employment experiences; targeted training for principals on how to nurture office managers’ extra-role behavior; the role of the union as detractor to extra-role behaviour, and rethinking the office manager’s job description.
Efficacy of Damage Detection Measures from Satellite Images

Estimating the extent of damage caused by natural disasters is necessary for implementing effective recovery measures. Aerial images of affected areas are easily obtained through satellite or aerial sensors. A careful analysis of images from before and after an event facilitates rapid detection and assessment of damage. Significant previous research has been done on developing measures to quantify the damage. In this study we evaluate the efficacy of existing change measures used to estimate damage. Determining the efficacy of these damage measures in definitive characterization of damage states is necessary for accurate automated assessment of windstorm damage.
RC-MES: a novel speaker modeling technique based on regression class for speaker identification

The speaker modeling technique is an essential problem in robust speaker recognition, especially when enrolment data is sparse. This paper presents a novel modeling approach named multi-eigenspace modeling technique based on regression class (RC-MES), which integrates the common eigenspace technique and the regression class (RC) idea of maximum likelihood linear regression (MLLR). RC-MES not only solves the problem of prior knowledge limitation of Gaussian mixture models (GMM) but also remedies the shortcomings of the common eigenspace that confuses speaker differences and phoneme differences. The eigenvoice analysis in RC can provide better discrimination ability between different speakers. The experimental results on speaker identification of 75 males show that, when enrolment data is sparse, RC-MES provides significant improvement over GMM, and the number of eigenvoices in RC-MES is fewer than that in the common eigenspace.
ffi cient Estimation of the SUR Cointegration Regression Model and Testing for Purchasing Power Parity ∗

This paper studies the efficient estimation of seemingly unrelated linear models with integrated regressors and stationary errors. We consider two cases. The first one has no common regressor among the equations. In this case, we show that by adding leads and lags of the first differences of the regressors and estimating this augmented dynamic regression model by generalized least squares using the long-run covariance matrix, we obtain an efficient estimator of the cointegrating vector that has a limiting mixed normal distribution. In the second case we consider, there is a common regressor to all equations, and we discuss efficient minimum distance estimation in this context. Simulation results suggests that our new estimator compares favorably with others already proposed in the literature. We apply these new estimators to the testing of the proportionality and symmetry conditions implied by purchasing power parity (PPP) among the G-7 countries. The tests based on the efficient estimates easily reject the joint hypotheses of proportionality and symmetry for all countries with either the United States or Germany as numeraire. Based on individual tests, our results suggest that Canada and Germany are the most likely countries for which the proportionality condition holds, and that Italy and Japan for the symmetry condition relative to the United States.