Miracle-dz/UniFAR-FTUs huggingface dataset:
Attention and intelligence: A factor analytic study.

Carroll (1993) found few factor-analytic studies that addressed attentional abilities. We reviewed and reanalyzed some of these studies and concluded that an exploratory approach to the study of the relationships between tests of attention and cognitive abilities was warranted. We sampled N = 147 adults from the general community and administered 17 tests of attention, including well-known neuropsychological tests along with tests drawn from the differential and experimental literatures on attention. We also administered 14 tests of cognitive ability designed to measure constructs described in Carrolls taxonomy of intelligence, including a higher-order general ability factor. Regression of a general factor from the abilities battery onto a general factor from the attentional battery showed these two latent variables to be near identical (β = .98). Exploratory structural equation modeling, which allowed a model wherein the abilities part of the model was a confirmatory measurement model but the attention .
Spinal osteotomy for thoracolumbar tuberculosis complicated with kyphosis does not further shorten existing shortened spinal cord

Objective: This study aims to observe the efficacy of single posterior vertebral osteotomy and the debridement treatment of the active stage of thoracolumbar tuberculosis complicated with kyphosis deformity, and to determine whether the current shortened spinal cord would be further shortened after osteotomy. Methods: A retrospective analysis was conducted on 32 patients who were treated from 2009 to 2012. Patients were treated with single posterior vertebral osteotomy, debridement and internal fixation. VAS and ODI were used to evaluate lower back pain and nerve function. The TraumaCadTM system was used to detect local kyphotic angle and spinal height changes. Results: VAS and ODI scores were improved from preoperative 5.81±1.15 and 47.47±9.813 to postoperative 1.75±0.67 and 89.80±3.59%, respectively (P<0.01). Preoperative local kyphotic angle was 38.38°±13.60°, and was 7.47°±2.9° upon followed-up (P<0.01). Furthermore, the rate of shrinkage of the height of the spinal cord increased from preoperative 49.48±17.67% to 88.32±20.74%, with an increase of 37.32±12.77%. Correlations of the newly increased postoperative shrinkage rate between the preoperative shrinkage rate and local kyphotic angle were not remarkable, and R values were 0.535 and 0.512, respectively. Conclusion: Under the control of normative anti-tuberculosis drugs, single posterior debridement and spinal shortened osteotomic internal fixation can effectively treat patients with active thoracic and lumbar spinal tuberculosis complicated with kyphosis. Furthermore, the spinal cord was able to tolerate further vertebral shrinkage, and its length was not affected by the existing spinal cord shrinkage and kyphotic angle.
Observation of reduced nonradiative current in 1.3-μm AlGaInAs-InP strained MQW lasers

We investigated experimentally the temperature dependence of the threshold current in 1.3-/spl mu/m AlGaInAs-InP strained multiple-quantum-well lasers. We find that radiative recombination constitutes almost 100% of the threshold current up to 220 K and remains more than 70% even at 300 K. This results in a high characteristic temperature T/sub 0/.
ZoomIn: Discovering Failures by Detecting Wrong Assertions

Automatic testing, although useful, is still quite ineffective against faults that do not cause crashes or uncaught exceptions. In the majority of the cases automatic tests do not include oracles, and only in some cases they incorporate assertions that encode the observed behavior instead of the intended behavior, that is if the application under test produces a wrong result, the synthesized assertions will encode wrong expectations that match the actual behavior of the application. In this paper we present Zoom In, a technique that extends the fault-revealing capability of test case generation techniques from crash-only faults to faults that require non-trivial oracles to be detected. Zoom In exploits the knowledge encoded in the manual tests written by developers and the similarity between executions to automatically determine an extremely small set of suspicious assertions that are likely wrong and thus worth manual inspection. Early empirical results show that Zoom In has been able to detect 50% of the analyzed non-crashing faults in the Apache Commons Math library requiring the inspection of less than 1.5% of the assertions automatically generated by EvoSuite.
Tournament Particle Swarm Optimization

This paper introduces tournament particle swarm optimization (PSO) as a method to optimize weights of game tree evaluation functions in a competitive environment using particle swarm optimization. This method makes use of tournaments to ensure a fair evaluation of the performance of particles in the swarm, relative to that of other particles. The empirical work presented compares the performance of different tournament methods that can be applied to the tournament PSO, with application to Checkers.
Exploration and Implementation of a Pre-Impact Fall Recognition Method Based on an Inertial Body Sensor Network

The unintentional injuries due to falls in elderly people give rise to a multitude of health and economic problems due to the growing aging population. The use of early pre-impact fall alarm and self-protective control could greatly reduce fall injuries. This paper aimed to explore and implement a pre-impact fall recognition/alarm method for free-direction fall activities based on understanding of the pre-impact lead time of falls and the angle of body postural stability using an inertial body sensor network. Eight healthy Asian adult subjects were arranged to perform three kinds of daily living activities and three kinds of fall activities. Nine MTx sensor modules were used to measure the body segmental kinematic characteristics of each subject for pre-impact fall recognition/alarm. Our analysis of the kinematic features of human body segments showed that the chest was the optimal sensor placement for an early pre-impact recognition/alarm (i.e., prediction/alarm of a fall event before it happens) and post-fall detection (i.e., detection of a fall event after it already happened). Furthermore, by comparative analysis of threshold levels for acceleration and angular rate, two acceleration thresholds were determined for early pre-impact alarm (7 m/s/s) and post-fall detection (20 m/s/s) under experimental conditions. The critical angles of postural stability of torso segment in three kinds of fall activities (forward, sideway and backward fall) were determined as 23.9 ± 3.3, 49.9 ± 4.1 and 9.9 ± 2.5 degrees, respectively, and the relative average pre-impact lead times were 329 ± 21, 265 ± 35 and 257 ± 36 ms. The results implied that among the three fall activities the sideway fall was associated with the largest postural stability angle and the forward fall was associated with the longest time to adjust body angle to avoid the fall; the backward fall was the most difficult to avoid among the three kinds of fall events due to the toughest combination of shortest lead time and smallest angle of postural stability which made it difficult for the self-protective control mechanism to adjust the body in time to avoid falling down.
Load-sharing based on relay-aided cooperative modeling in uplink two-tier cellular networks

In this paper, we study the relay-aided cooperative modeling that supports the load-sharing in uplink two-tier cellular networks. In our model, users in heavily loaded macrocell are shifted to lightly loaded smallcells with the assistance of relay users to mitigate Signal to Interference plus Noise Ratio (SINR) degradation problem in conventional direct handover. In order to promote relaying data of users which are selfish and rational, a trading exchange model based on Stackelberg game is proposed to optimize strategies of users. Relay users have pricing-based strategies on theirs power unit while shifted heavily loaded macrocell users have strategies to buy power levels of relay users. Optimal strategies are investigated using the backward induction analysis. Specifically, problems of NP-hard combinatorial optimization in relay user selections in the game are solved with a distributed algorithm based on matching theory. We intensively evaluate our proposed model by simulating it in Matlab which shows the efficiency of our proposal.
Soybean Meal Induces Intestinal Inflammation in Zebrafish Larvae

The necessary replacement of fish meal with other protein source in diets of commercially important fish has prompted the study of the effect of the inclusion of different vegetable proteins sources on growth performance and on the gastro-intestinal tract. Currently, soybean meal is the primary protein source as a fish meal replacement because of its low price and high availability. Likewise, it is been documented that the ingestion of soybean meal by several fish species, such as salmonids and carp, triggers a type of intestinal inflammation called enteritis. In this paper, we analyzed the effects of the ingestion of soybean meal and two of its components, soy protein and soy saponin, on zebrafish to establish the basis for using zebrafish larvae as a model for fish nutrition. We took advantage of the existence of different transgenic lines, which allowed us to perform in vivo analysis. Our results indicated that larvae that were feed with soybean meal developed a clear intestinal inflammation as early as two day after beginning the diet. Moreover, we determined that is not the soy protein present in the diet but the soy saponin that is primarily responsible for triggering the immune response. These findings support the use of zebrafish screening assays to identify novel ingredients that would to improved current fish diets or would formulate new ones.
Moving In Time: Neurons, Clocks, and Rhythmic Movements

When tapping a desired frequency, subjects tend to drift away from this target frequency. This compromises the estimate of the correlation between inter-tap intervals (ITIs) as predicted by the two-level model of Wing and Kristofferson which consists of an internal timer (‘clock’) and motor delays. Whereas previous studies on the timing of rhythmic tapping attempted to eliminate drift, we compared the production of three constant frequencies (1.5, 2.0, and 2.5 Hz) to the production of tapping sequences with a linearly decreasing inter-tap interval (ITI) (corresponding to an increase in tapping frequency from 1.5 to 2.5 Hz). For all conditions a synchronization-continuation paradigm was used. Tapping forces and electromyograms of the index-finger flexor and extensor were recorded and ITIs were derived yielding interval variability and model parameters, i.e. clock and motor variances. Electromyographic recordings served to study the influence of tapping frequency on the peripheral part of the tap event. The condition with an increasing frequency was more difficult to perform, as evidenced by an increase in deviation from the intended ITIs. In general, tapping frequency affected force level, inter-tap variability, model parameters, and muscle co-activation. Parameters for the condition with a decreasing ITI were comparable to those found in the constant frequency conditions. That is, although tapping with an intentional drift is different from constant tapping and more difficult to perform, the timing properties of both forms of tapping are remarkably similar and described well by the Wing-Kristofferson model. Adapted from: A. N. Vardy, A. Daffertshofer, and P. J. Beek, Tapping with intentional drift. Exp Brain Res, 192(4):615-625, 2009.
MANAGEMENT FACTORS INFLUENCING FERTILITY IN SYNCHRONIZED AND NATURAL BREEDING PROGRAMS

Artificial insemination provides a method to inseminate a large number of females to a single sire that has been selected and proven to be an industry leader for economically relevant traits. Thus, genetic change in a herd can occur quickly through the use of artificial insemination. With natural service, herd bulls are also selected for economically relevant traits but are limited on the number of cows/heifers they can service during the breeding season. During the breeding season, a herd bull’s job is to detect cows/heifers in standing estrus and breed them at the appropriate time. For successful artificial insemination (AI) of cattle to occur, the producer (herd manager) must take the place of the herd bull in detecting the cows/heifers that are ready to be inseminated.
A Simplified Min-Sum Decoding Algorithm for Non-Binary LDPC Codes

Non-binary low-density parity-check codes are robust to various channel impairments. However, based on the existing decoding algorithms, the decoder implementations are expensive because of their excessive computational complexity and memory usage. Based on the combinatorial optimization, we present an approximation method for the check node processing. The simulation results demonstrate that our scheme has small performance loss over the additive white Gaussian noise channel and independent Rayleigh fading channel. Furthermore, the proposed reduced-complexity realization provides significant savings on hardware, so it yields a good performance-complexity tradeoff and can be efficiently implemented.
Application for load balancing in SDN

This paper presents an application that implements three load balancers using a Software Defined Network (SDN). The application was developed for the Ryu controller. First, the principles of three queueing algorithms are presented; the selected algorithms are: FIFO, Round Robin and Deficit Round Robin. Then, the main aspects of the design and implementation of load balancing modules based on each of the aforementioned algorithms are outlined. The application is able to assign a client request to a server based on the criteria associated to a given queuing algorithm. Additionally, the tests that were carried out with the application using both a virtual SDN implemented with Mininet and a physical SDN using an OpenFlow switch are discussed. Finally, the results of the tests on the physical SDN that focused on proofing the functionality and estimating the response time of the application are included.
Patchy Populations in Stochastic Environments: Critical Number of Patches for Persistence

We introduce a model for the dynamics of a patchy population in a stochastic environment and derive a criterion for its persistence. This criterion is based on the geometric mean (GM) through time of the spatial‐arithmetic mean of growth rates. For the population to persist, the GM has to be ≥1. The GM increases with the number of patches (because the sampling error is reduced) and decreases with both the variance and the spatial covariance of growth rates. We derive analytical expressions for the minimum number of patches (and the maximum harvesting rate) required for the persistence of the population. As the magnitude of environmental fluctuations increases, the number of patches required for persistence increases, and the fraction of individuals that can be harvested decreases. The novelty of our approach is that we focus on Malthusian local population dynamics with high dispersal and strong environmental variability from year to year. Unlike previous models of patchy populations that assume an infinite number of patches, we focus specifically on the effect that the number of patches has on population persistence. Our work is therefore directly relevant to patchily distributed organisms that are restricted to a small number of habitat patches.
A Simulation Budget Allocation Procedure for Enhancing the Efficiency of Optimal Subset Selection

Selecting the optimal subset is highly beneficial to numerous developments in simulation optimization. This paper studies the problem of maximizing the probability of correctly selecting the top- $m$ designs out of $k$ designs under a computing budget constraint. We develop a new procedure which is more efficient and robust than currently existing procedures in the literature. We also provide an analysis on its asymptotic convergence rate. Based on this analysis, we show that our new procedure achieves a higher convergence rate than other procedures under certain conditions. Numerical testing supports our analytical analysis and shows that the new procedure is significantly more efficient and robust.
Cerebrospinal fluid dynamics

Hydrocephalus is far more complicated than a simple disorder of CSF circulation. Historically, it has been diagnosed using clinical and psychomotor assessment plus brain imaging. The role of physiological measurement to aid diagnosis becomes more appreciated in current clinical practice. This has been reflected by recently formulated guidelines for the management of normal pressure hydrocephalus. Clinical measurement in hydrocephalus is mainly related to intracranial pressure (ICP) and cerebral blood flow. This review lists and discusses most common forms of the methods: CSF infusion study, overnight ICP monitoring, assessment of slow ICP waves, testing pressure reactivity, cerebral autoregulation, CO2 reactivity and PET-CBF studies combined with MRI co-registration. The basics of CSF dynamics modelling are presented and the principles of the assessment of functioning of the implanted hydrocephalus shunts are also discussed. The descriptions of multiple forms of measurement along with clinical illustrations are mainly based on in-house experience of a multidisciplinary group of scientists and clinicians from Cambridge, UK.
Improved Dense Trajectory with Cross Streams

Improved dense trajectories (iDT) have shown great performance in action recognition, and their combination with the two-stream approach has achieved state-of-the-art performance. It is, however, difficult for iDT to completely remove background trajectories from video with camera shaking. Trajectories in less discriminative regions should be given modest weights in order to create more discriminative local descriptors for action recognition. In addition, the two-stream approach, which learns appearance and motion information separately, cannot focus on motion in important regions when extracting features from spatial convolutional layers of the appearance network, and vice versa. In order to address the above mentioned problems, we propose a new local descriptor that pools a new convolutional layer obtained from crossing two networks along iDT. This new descriptor is calculated by applying discriminative weights learned from one network to a convolutional layer of the other network. Our method has achieved state-of-the-art performance on ordinal action recognition datasets, 92.3% on UCF101, and 66.2% on HMDB51.
The Relationship Between Epistemological Beliefs and Self-regulated Learning Skills in the Online Course Environment

Epistemological beliefs and self-regulated learning skills, as meta-cognitive constructs, have been suggested as being associated with positive learning outcomes. Results from research literature, however, have been mixed as to the impact of epistemological beliefs, self-regulated learning skills, or some combination of the two meta-cognitive constructs as being associated with academic achievement. The relationship between epistemological beliefs and self-regulated learning skills as associated with academic achievement among online learners was examined in this study.
Conceptual Focus in Social Encounters: the Case of Directives

Conceptual Focus in Social Encounters: the Case of Directives The present analysis focuses on highlighting the conceptual focus on groups as a specific property of discourse in young peer groups of Polish speakers. Its alternative is the conceptual focus on individual interlocutors; the latter is implicitly assumed to be an interactive norm in a vast body of pragmatic studies dealing with the performance of speech acts in both monolingual and comparative setups. The analysis is based on empirical material comprising interaction in young peer groups in Poland, Great Britain and Germany under constant experimental conditions, made possible by the international commercial project "Big Brother" belonging to the genre of reality T.V.
Community Detection in Evolving Networks

Community detection is a well-studied problem in social networks. However most of the research so far has been on static networks. In this paper, we address the problem of community detection in evolving social networks. As social networks evolve, the community structure of the network can change. How can the community structure be updated in an efficient way? How often should community structure be updated? We give two methods based on the Louvain algorithm, to determine when to update the community structure. The first method, called the Edge-Distribution-Analysis algorithm, analyzes the newly added edges in order to make this decision. The second method, called the Modularity-Change-Rate algorithm, finds the rate of modularity change in a given network, and uses it to predict whether or not an update is required.Due to the sparsity of real datasets of evolving networks, we propose three models to generate evolving networks: a Random model, a model based on the well-known phenomenon of homophily in social networks, and another based on the phenomenon of triadic and cyclic closure. Starting with real-world data sets, we used these models to generate evolving networks. We evaluated the Edge-Distribution-Analysis algorithm and Modularity-Change-Rate algorithm on these data sets. Our results show that both our methods predict quite well when the community structure should be updated. They result in significant computational savings compared to approaches that would update the community structure after a fixed number of edge additions, while ensuring that the quality of the community structure is comparable.
The Memory of the Modern

A multidisciplinary work, Memory of the Modern examines stock markets, tango dancers, vagabond murderers, neurology, monument destruction, and colonial policies to document how individuals and institutions shaped memory in the late nineteenth and early twentieth centuries. Wonderfully written, the book studies these diverse "memory-sites" to show how memory and history are fought over, shaped, and put to personal and ideological use.
Calcium influx pathways in breast cancer: opportunities for pharmacological intervention

Ca2+ influx through Ca2+ permeable ion channels is a key trigger and regulator of a diverse set of cellular events, such as neurotransmitter release and muscle contraction. Ca2+ influx is also a regulator of processes relevant to cancer, including cellular proliferation and migration. This review focuses on calcium influx in breast cancer cells as well as the potential for pharmacological modulators of specific Ca2+ influx channels to represent future agents for breast cancer therapy. Altered expression of specific calcium permeable ion channels is present in some breast cancers. In some cases, such changes can be related to breast cancer subtype and even prognosis. In vitro and in vivo models have now helped identify specific Ca2+ channels that play important roles in the proliferation and invasiveness of breast cancer cells. However, some aspects of our understanding of Ca2+ influx in breast cancer still require further study. These include identifying the mechanisms responsible for altered expression and the most effective therapeutic strategy to target breast cancer cells through specific Ca2+ channels. The role of Ca2+ influx in processes beyond breast cancer cell proliferation and migration should become the focus of studies in the next decade.