Miracle-dz/UniFAR-FTUs huggingface dataset:
Ambulatory Activity in People with Early Parkinson's Disease

People with Parkinson's disease (PD) are encouraged to participate in physical activity levels equivalent to those recommended for the general population. Understanding factors that influence this activity is important for facilitating this participation. This study examined factors associated with participation in moderate and high intensity daily ambulatory activity in people with mild to moderate PD. Fifty community-dwelling people with mild-moderate PD were monitored with accelerometers over three days to characterise their daily ambulatory activity levels. Personal factors, disease characteristics, gait and cognitive capacity were measured. Prediction models were created to identify factors influencing ambulation activity. People with PD spent approximately 77 minutes walking per day, mostly at a moderate intensity resulting in a median of 6300 steps/day. Disease severity predicted time spent in moderate ambulation bouts (R2 = 0.116, p = .017). Gait (Timed Up and Go (TUG) Test) and executive function together predicted engagement in high intensity ambulatory activity (R2 > 0.170, p < .022). While disease severity, gait performance and executive function were predictive of engagement in moderate and high intensity walking activity, additional personal and social factors should be considered and are likely to also strongly impact on activity levels.
Endocrine therapy is associated with low performance on some estrogen-sensitive cognitive tasks in postmenopausal women with breast cancer

This study investigated the effects of endocrine therapy (i.e., tamoxifen and anastrozole) on cognitive functioning by comparing 28 postmenopausal women with breast cancer to 37 healthy age-equivalent controls. Participants completed neuropsychological tests previously shown to be estrogen sensitive (e.g., verbal memory, letter fluency). A significant treatment effect was observed on speeded measures of letter fluency, complex visuomotor attention, and manual dexterity, but not on measures of verbal or object-location memory, or on tests presumed to be estrogen insensitive (e.g., spatial ability). In partial support of previous research, these results indicate that endocrine therapy can have detrimental effects on speeded higher brain functions but not necessarily on memory.
Infertility Stress: The Role of Coping Strategies, Personality Trait, and Social Support

Objective: The aim of our study was to investigate the effect of coping strategies, personality trait and social support as the main social and psychological factors on infertility stress. Materials and methods: This study was conducted on 201 infertile Iranian women referred to the Vali-e-Asr Reproductive health Research Center, and completed the following questionnaires: The fertility problem inventory, measuring perceived infertility related stress (Newton CR, 1999), big five factor personality questionnaire (Farahani, 2009), multidimensional scale of perceived social support MSPS (Zimmet 1988), and multidimensional assessment of coping (Endler, 1990). The results were then analyzed using the Pearson Correlation and stepwise regression. Results: Infertility stress has negative and significant relation with emotion-oriented coping method, perceived social support and bring extrovert. It has a positive, significant relation with emotion-oriented coping method, obsessive compulsive disorder (OCD). The results of the stepwise regression showed that emotion-oriented coping method, OCD and being extrovert are suitable predictors of infertility stress. Conclusion: About 22% of the infertility stress variance was explained by coping strategies and personality trait. Therefore our result demonstrates the importance of social and psychological factors on experiencing the infertility stress.
The 2001 GMTK-based SPINE ASR system

This paper provides a detailed description of the University of Washington automatic speech recognition (ASR) system for the 2001 DARPA SPeech In Noisy Environments (SPINE) task. Our system makes heavy use of the graphical modeling toolkit (GMTK), a general purpose graphical modeling-based ASR sys- tem that allows arbitrary parameter tying, ﬂexible deterministic and stochastic dependencies between variables, and a gener- alized maximum likelihood parameter estimation algorithm. In our SPINE system, GMTK was used for acoustic model training whereas feature extraction, speaker adaptation, and ﬁrst-pass decoding were performed by HTK. Our integrated GMTK/HTK sys- tem demonstrates the relative merits provided by each tool. Novel aspects of our SPINE system include the capturing of correlations among feature vectors via a globally-shared factored sparse inverse covariance matrix and generalized EM training.
Polynomial approximations for Bit Error Probability for 4-DPSK transmission

In this paper we propose an accurate approximation for the Bit Error Probability (BEP) for the case of four-phase differential phase shift keying (4-DPSK). The BER can be expressed in terms of the first order Marcum Q-function, which have no closed form. By approximating the modified Bessel function and the integrand of the Marcum Q-function with a polynomial function, new simple approximate expression for the BER can be derived. Comparison with existing approximations shows that our new estimator can provide more accurate results.
A Study of Five Parallel Approaches to a Genetic Algorithm for the Traveling Salesman Problem

Abstract This paper presents a comparative study of five different coarse-grained parallel genetic algorithms (PGAs) using the traveling salesman problem as the case application. All of these PGAs are based on the same baseline serial genetic algorithm, implemented on the same parallel machine (IBM SP2), tested on the same problem instances, and started from the same set of initial populations. Based on these experiments, a PGA that combines a new subtour technique with a known migration approach is identified to be the best for the traveling salesman problem among the five PGAs being compared.
Software-implemented EDAC protection against SEUs

In many computer systems, the contents of memory are protected by an error detection and correction (EDAC) code. Bit-flips caused by single event upsets (SEU) are a well-known problem in memory chips; EDAC codes have been an effective solution to this problem. These codes are usually implemented in hardware using extra memory bits and encoding/decoding circuitry. In systems where EDAC hardware is not available, the reliability of the system can be improved by providing protection through software. Codes and techniques that can be used for software implementation of EDAC are discussed and compared. The implementation requirements and issues are discussed, and some solutions are presented. The paper discusses in detail how system-level and chip-level structures relate to multiple error correction. A simple solution is presented to make the EDAC scheme independent of these structures. The technique in this paper was implemented and used effectively in an actual space experiment. We have observed that SEU corrupt the operating system or programs of a computer system that does not have any EDAC for memory, forcing the system to be reset frequently. Protecting the entire memory (code and data) might not be practical in software. However this paper demonstrates that software-implemented EDAC is a low-cost solution that provides protection for code segments and can appreciably enhance the system availability in a low-radiation space environment.
Consequences of history-cued and means–end strategies in problem solving.

There are two broad processes that people can use when attempting to solve a problem. The first of these is a means-ends strategy in which attempts are made to reduce differences between a given problem state and a goal or subgoal. Moves are generated by the goal or subgoals. The second is a history-cued process in which people use previous moves to generate subsequent moves. It is suggested that a means-ends strategy tends to reduce transfer effects. A history-cued strategy may facilitate rule induction, which in turn may be an important contributing factor to transfer. A series of four experiments using hybrid problems that are soluble either by rule induction or by means-ends analysis supported the above suggestion. Two additional experiments indicated that with respect to the insoluble problem effect, the use of history-cued strategy was, of itself, insufficient to induce transfer effects. In order for transfer to occur, the structure of the problems and the manner in which they were presented had to be such as to ensure that problem solvers perceived a close relation between problems.
Performance analysis of hierarchical caching systems with bandwidth constraints

The vast majority of Internet traffic is carried by content delivery networks. A high potential to bring content even closer to consumers and to reduce energy cost is achieved by the nano data center (NaDa) concept that has been proposed in recent work. In this approach the capacity available on small devices such as home gateways is used to support content delivery in an hierarchical caching system. In this work, we present a method to determine the efficiency of hierarchical content delivery networks with limited bandwidth, such as in the NaDa approach, analytically. We analyze the Internet Census Dataset to assess the number of available devices on autonomous system level. We evaluate the potential of hierarchical cache networks with bandwidth constraints to assist content delivery and determine its limits.
Power allocation over two identical Gilbert-Elliott channels

We study the problem of power allocation over two identical Gilbert-Elliot communication channels. Our goal is to maximize the expected discounted number of bits transmitted over an infinite time horizon. This is achieved by choosing among three possible strategies: (1) betting on channel 1 by allocating all the power to this channel, which results in high data rate if channel 1 happens to be in good state, and zero bits transmitted if channel 1 is in bad state (even if channel 2 is in good state) (2) betting on channel 2 by allocating all the power to the second channel, and (3) a balanced strategy whereby each channel is allocated half the total power, with the effect that each channel can transmit a low data rate if it is in good state. We assume that each channel's state is only revealed upon transmission of data on that channel. We model this problem as a partially observable Markov decision processes (MDP), and derive key threshold properties of the optimal policy. Further, we show that by formulating and solving a relevant linear program the thresholds can be determined numerically when system parameters are known.
Legged Motion Planning in Complex Three-Dimensional Environments

While legged robots are well suited to navigating complex three-dimensional (3-D) environments, their practical applicability is still hampered by the time taken to plan manoeuvres to traverse these challenging environments. We present contact dynamic roadmaps (CDRM), which extend dynamic roadmaps with contact information. The CDRM is precomputed offline to generate a discretized mapping from each leg's workspace to its configuration space, and then adapted online to the environment to rapidly identify collision-free foothold positions. The concept behind this is to perform the expensive foothold candidate generation and collision checking phases offline and store the data for use in the online planner. The CDRM is coupled with a rapidly-exploring random tree planner to generate acyclic full-body motion plans in complex 3-D environments. The performance of the approach is validated and compared in simulation in a wide variety of scenarios that require full-body planning to successfully navigate.
MALFORMATIONS OF THE SKULL BASE ASSOCIATED WITH CLEFT PALATE DEFORMITY

Practical clinical matters are foremost in the minds of those treating patients with cleft palate deformity. The urgency of adequate functional repair has caused a vast body of literature to arise. Although it is recognized that cleft palate deformity is associated with other congenital malformations, inevitably consideration of this condition by the several interested specialties has become overly restricted to the affected region, the facial skull. The other portion of the cranium, the neural skull, has been tacitly regarded as normal, or left out of consideration altogether (1, 2). This particulate viewpoint has received theoretical support from the concept that each human malformation is essentially an isolated phenomenon capable of distinct and separate classification. More recently, students of human teratology have formulated a functional synthesis of congenital cephalic malformations. For example, a formerly discrete series of congenital facial abnormalities has been shown to be a group of rather closely related conditions, with continuous gradations between them. The supposedly clinically separate malformations have been shown to differ not in their kind but rather in their degree; they differ in their combinations of individual manifestations (3). In a similar fashion, congenital malformations of the neural skull have also been shown to be a continuous series (4). Indeed, when we consider the many case reports of children suffering from simultaneous manifestations in both the neural and the facial skeleton a newer synthesis arises (5, 6). This concept encompasses a graduated series of manifestations within both skull components, having some common etiological denominator. The skull base, formed by the basi-occipital, sphenoid and ethmoid bones is a functional region in common to both components of the skull. A congenital cephalic malformation of either, or both of the skull components, would be expected to also exhibit a concomitant malformation of the skull base. Recent investigations have established reliable quantitative methods of measuring the skull base (7, 8, 9). It has been shown by these methods that significant alterations of the skull base occurred in a variety of congenital cephalic malformations. In view of this, it seemed worthwhile to similarly examine a series of cleft lip and cleft palate cases for coexistent skull base malformations; to quantitate them if they existed and to further ascertain if malformations of the neural skull also existed in these cases. 1 Work performed under the tenure of a Lederle Medical Faculty Award, and aided, in part, by a research grant (B-965) from the National Institute of Neurological Diseases and Blindness, of the National Institutes of Health, Public Health Service.
College Student Identity

Applying the Burke-Tully procedure to the measurement of the college student role, this article explores the consensus of college students on role meanings, the correspondence between college student role and identity meanings, and the implications of variations in identity on role performance. Data on the meanings of the college student role and four counter-roles, college student identity, and three role performances were obtained from a questionnaire administered to 640 college undergraduates. Results indicate that: (1) there is a general consensus on the meanings of the college student role, (2) college student identities are not limited to the range of meanings which characterize the college student role, and (3) differences in the college student identity are related to variations in role performances. The findings suggest that individuals may be motivated to excel in activities that support the meanings of their identities.
Evolving process simulators by using validated learning

Software process simulation has been evolved towards a mature technology for analyzing the behavior of software processes: Methods for systematically developing simulators exist, tools for modeling and execution are available, models for nearly all kinds of processes have been created, and empirical evidence on the accuracy of many models has been gathered. However, software process simulation is still waiting for a breakthrough success. Although simulation is a technology that has been successfully established in many domains, software process simulation is not widely used in software engineering practice. Should we pivot or persevere? This article argues that it is necessary to use a rigorous approach for discovering “customers” of process simulators and finding out what they consider to be value. One mechanism to do this is to apply so-called “validated learning”, i.e., to apply an actionable learning approach to identify what is relevant and what is irrelevant by systematically testing value hypotheses. Doing this promises that simulation efforts can be concentrated on value-creation and that wrong avenues can be avoided. Besides this, the article sketched prerequisites and lessons learned that need to be considered when applying simulators in practice as well as upcoming opportunities for making software process simulation a success.
Dentist 2:00On codes with low peak-to-average power ratio for multicode CDMA

Codes which reduce the peak-to-average power (PAPR) in multicode code-division multiple-access (MC-CDMA) communications systems are systematically studied. The problem of designing such codes is reformulated as a new coding-theoretic problem: codes with low PAPR are ones in which the codewords are far from the first-order Reed-Muller code. Bounds on the tradeoff between rate, PAPR, and error-correcting capability of codes for MC-CDMA follow. The connections between the code design problem, bent functions, and algebraic coding theory (in particular, the Kerdock codes and Delsarte-Goethals codes) are exploited to construct code families with flexible parameters for the small values of n of practical interest. In view of their algebraic structure, these codes enjoy efficient encoding and decoding algorithms. The correspondence concludes by listing open problems in algebraic coding theory and Boolean functions motivated by the correspondence.
The brain-derived neurotrophic factor Val66Met polymorphism is associated with reduced functional magnetic resonance imaging activity in the hippocampus and increased use of caudate nucleus-dependent strategies in a human virtual navigation task

Multiple memory systems are involved in parallel processing of spatial information during navigation. A series of studies have distinguished between hippocampus‐dependent ‘spatial’ navigation, which relies on knowledge of the relationship between landmarks in one’s environment to build a cognitive map, and habit‐based ‘response’ learning, which requires the memorization of a series of actions and is mediated by the caudate nucleus. Studies have demonstrated that people spontaneously use one of these two alternative navigational strategies with almost equal frequency to solve a given navigation task, and that strategy correlates with functional magnetic resonance imaging (fMRI) activity and grey matter density. Although there is evidence for experience modulating grey matter in the hippocampus, genetic contributions may also play an important role in the hippocampus and caudate nucleus. Recently, the Val66Met polymorphism of the brain‐derived neurotrophic factor (BDNF) gene has emerged as a possible inhibitor of hippocampal function. We have investigated the role of the BDNF Val66Met polymorphism on virtual navigation behaviour and brain activation during an fMRI navigation task. Our results demonstrate a genetic contribution to spontaneous strategies, where ‘Met’ carriers use a response strategy more frequently than individuals homozygous for the ‘Val’ allele. Additionally, we found increased hippocampal activation in the Val group relative to the Met group during performance of a virtual navigation task. Our results support the idea that the BDNF gene with the Val66Met polymorphism is a novel candidate gene involved in determining spontaneous strategies during navigation behaviour.
Incremental Nyström Low-Rank Decomposition for Dynamic Learning

Eigen-decomposition is a key step in spectral clustering and some kernel methods. The Nyström method is often used to speed up kernel matrix decomposition. However, it cannot effectively update eigenvectors of matrices when datasets dynamically increase with time. In this paper, we propose an incremental Nyström method for dynamic learning. Experimental results demonstrate the feasibility and effectiveness of the proposed method.
First Story Detection using a Composite Document Representation

In this paper, we explore the effects of data fusion on First Story Detection [1] in a broadcast news domain. The data fusion element of this experiment involves the combination of evidence derived from two distinct representations of document content in a single cluster run. Our composite document representation consists of a concept representation (based on the lexical chains derived from a text) and free text representation (using traditional keyword index terms). Using the TDT1 evaluation methodology we evaluate a number of document representation strategies and propose reasons why our data fusion experiment shows performance improvements in the TDT domain.
Analysis of Carbon Sequestration Pattern in Tropical Fruit Trees

Terrestrial carbon sequestration is an important pathway of minimizing CO2 concentration in the atmosphere. Tropical evergreen trees like cashew have some adaptive mechanism in an environment with strong seasonal variation of light and water.In this study, multilayer satellite images from the vegetation (VGT) sensors on board the Spot satellite (01/2006 to 12/2009) for Cashew plantations areas of Tamilnadu, India were analyzed for temporal variability. The temporal analysis of vegetation indices was done and the GPP (Gross Primary Productivity) was calculated using the satellite based vegetation photosynthesis model (VPM). The enhanced vegetation index (EVI) identified subtle changes in the seasonal dynamics of leaf phenology in Cashew plantation area, as supported by leaf moisture content and leaf area index. The land surface water index (LSWI), indicates that the plantation experienced water stress during the dry seasons. The VPM model which uses EVI, LSWI and site specific climate data for 2008-2009 predicted high GPP in the late wet season than in summer season. The GPP calculated from the remote sensing data are classified into three classes using Radial Basis Function Neural Network (RBFNN). The calculated GPP of different months in a year showed that the monthly GPP ranged from 50-128 g C/m2. The RBFNN is trained to provide an output value of 0, 1 and 2 for carbon sequestration which ranged from 50-75, 76-100 and 101-125 g C/m2, respectively. The experimental results shows that the RBFNN classifies the carbon sequestration with an accuracy of 95.2%.
Acoustic Feature-based Non-scorable Response Detection for an Automated Speaking Proficiency Assessment

This study provides a method that increases the robustness of automated speech scoring. Responses with sub-optimal characteristics such as background noises, volume problems, non-English speech, whispered speech, and non-responses make automated scoring more difﬁcult. For instance, loud background noises distort the spectral characteristics of speech, and the performance of the prosody and pronunciation features are signif-icantly degraded. Finally, the automated scores of these responses become less reliable. In order to address this problem, the automated scoring system in this study ﬁrst ﬁlters out non-scorable responses using a ﬁltering model and then predicts the proﬁciency scores of the remaining responses using a scoring model. In addition to automatic speech recognition-based (ASR) ﬁlter, which demonstrated promising performances in previous studies, a new ﬁlter was implemented in this study using acoustic features. The acoustic-based ﬁlter achieved a comparable performance to the ASR-based ﬁlter, and the combination of the two models achieved further improvement. The combined ﬁlter was evalu-ated on two actual test products and it achieved an accuracy rate of over 98% with an F-score of 86%.