Miracle-dz/UniFAR-FTUs huggingface dataset:
On efficiency gain of joint pilot and data adaptation for OFDM based systems

In OFDM-based systems, data symbols are usually transmitted together with pilots, which are used for channel estimation or prediction. More pilots in use can further improve the accuracy of channel estimation or prediction. However, this can also result in the efficiency loss of channel gain allocated for data transmission. In this paper, we investigate the joint pilot and data adaptation regarding pilot allocation, payload power and bits. Three different implementation approaches are proposed, the performance is evaluated and shows joint pilot and data adaptation can considerably improve system overall performance, and the efficiency gain related to the pilot-allocation adaptation can be significant.
JavaSplit: a runtime for execution of monolithic Java programs on heterogenous collections of commodity workstations

This paper presents JavaSplit, a portable runtime for distributed execution of multithreaded Java programs. Java-Split transparently distributes threads and objects of an application among the participating nodes. Thus, it gains augmented computational power and increased memory capacity without modifying the Java multithreaded programming conventions. Java-Split works by rewriting the bytecodes of a given parallel application, transforming it into a distributed application that incorporates all the runtime logic. Each runtime node carries out its part of the resulting distributed computation using nothing but its local standard (unmodified) Java virtual machine (JVM). This is unlike previous Java-based distributed runtime systems, which use a specialized JVM or utilize unconventional programming constructs. Since Java-Split is orthogonal to the implementation of a local JVM, it achieves portability across any existing platform and allows each node to locally optimize the performance of its JVM, e.g., via a just-in-time compiler (JIT).
Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks

The complex language of eukaryotic gene expression remains incompletely understood. Despite the importance suggested by many noncoding variants statistically associated with human disease, nearly all such variants have unknown mechanism. Here, we address this challenge using an approach based on a recent machine learning advance—deep convolutional neural networks (CNNs). We introduce an open source package Basset (https://github.com/davek44/Basset) to apply CNNs to learn the functional activity of DNA sequences from genomics data. We trained Basset on a compendium of accessible genomic sites mapped in 164 cell types by DNaseI-seq and demonstrate far greater predictive accuracy than previous methods. Basset predictions for the change in accessibility between variant alleles were far greater for GWAS SNPs that are likely to be causal relative to nearby SNPs in linkage disequilibrium with them. With Basset, a researcher can perform a single sequencing assay in their cell type of interest and simultaneously learn that cell’s chromatin accessibility code and annotate every mutation in the genome with its influence on present accessibility and latent potential for accessibility. Thus, Basset offers a powerful computational approach to annotate and interpret the noncoding genome.
Experience, represent, apply (ERA): A heuristic for digital engagement in the early years

This paper presents an heuristic that underpinned the development of a range of early years applications (apps) as part of the Early Learning Science, Technology, Engineering and Mathematics (STEM) Australia (ELSA) project (2016? 19). The heuristic was developed to address a range of challenges that accompany the use of digital technologies in the year before formal schooling. These include the role of digital technologies in play-based learning environments, screen time and the importance of agency when young children are using digital technologies. In an increasingly digital world, the issue of screen time looms large as a particularly contested aspect of technology use by young children. The ELSA project responded to this challenge by developing a pedagogical framework that embedded digital technologies into context-rich, play-based STEM learning. The Experience, Represent, Apply (ERA) heuristic discussed in this paper is a component of the project's overall pedagogical framework and provides early years educators with the opportunity, and the know-how, to integrate digital technologies into STEM activities through intentional teaching and play-based engagement. It was evident from educator feedback at the four days of workshops we delivered, and in other comments throughout the year long trial, that the educators were able to embrace the cyclic nature of the ERA heuristic and ensure that the time spent on the tablet (Represent) was connected to contextualised learning opportunities (Experience and Apply). Importantly, the tablets provided affordances for the children to utilise User Generated Content (UGC) to support active engagement when using the tablets.
The Incremental Ratio Based Causal fractional Calculus

The generalized incremental ratio fractional derivative is revised and its main properties deduced. It is shown that in the case of analytic functions, it enjoys some interesting properties like: linearity and causality and has a semi-group structure. Some simple examples are presented. The enlargement of the set of functions for which the group properties of the fractional derivative are valid is done. With this, it is shown that some well-known results are valid in a more general set-up. Some examples are presented.
Wireless Image Transmission Using Maximum Power Adaptation Algorithm

In this Paper, Optimization of error is considered using power adaptation algorithm while transmission is carried out The Maximum Power Adaptation algorithm(MAPAA) is based on updating the amount of power transmitted for each bit according to its importance in the image quality as measured by the mean-square error. This Maximum Power Adaptation Algorithm is used to find the optimum power distribution vector such that the RootMeanSquareError (RMSE) is minimized subject to the constraints that the power per bit is kept constant considering Maximum Root Mean Square Error. The above optimization is done for RMSE regardless of the average probability of bit error and the PAPR is kept below a certain limit. Maximum Power Adaptation Algorithm shows better performance compared to the case of Conventional power adaptation while reducing the Peak-to-Average Power (PAPR) Ratio.
Determination of material parameters of quasi-linear viscoelastic rheological model for thermoplastics and resins

In this study, an algorithm for identification of elastic and viscoelastic constants of a recently developed constitutive equation for thermoplastics and resins is presented. The equation has been applied to model the viscoelastic response of ultra high molecular weigth polyethylene. In order to determine the material parameters, a series of rheological tests has been performed. A set of equations describing one-dimensional processes has been derived and is utilized to determine the material constants. The material parameter identification leads to a set of 9 constants, i.e. 3 constants of elasticity and 6 constants of viscoelasticity. For the determined values of parameters, several validation tests have been performed.
The Effect of Preceding Context on Inhibition: An Event-Related fMRI Study

In this study we combined event-related fMRI with a parametric manipulation of the go nogo paradigm to examine the effect of preceding context on inhibitory processes. Nogo trials were preceded by either 1, 3, or 5 go trials and then compared to one another. Two distinct patterns of activation were associated with behavioral inhibition: First, the ventral prefrontal cortex, cingulate gyrus, and superior parietal regions showed a context effect with an increase in MR signal to nogo trials with increasing number of preceding go trials. Second, anterior regions in the supplementary and premotor cortex showed an increase in MR signal on the nogo condition after 5 preceding go trials, but not after only 1 or 3. A model using the BOLD response in our data was used to verify that the effect of context was not an artifact of the randomization scheme used in the design.
THE EFFECT OF STUDY TIME ON PRIMING POSSIBLE AND IMPOSSIBLE FIGURES IN THE OBJECT DECISION TEST

Previous research demonstrated object decision priming for possible, but not impossible threedimensional objects (e.g., Schacter et al. 1990; 1991). Subsequent research by Carrasco and Seamon (1996) found that when possible and impossible objects were equated for complexity, priming was observed for both object types. The present research extended the complexity results. Possible objects demonstrated object decision priming with greater classification accuracy for studied than nonstudied objects, following exposure durations of 900 ms to 30 s. The pattern for impossible objects was a function of their complexity. Highly complex impossible objects showed greater classification accuracy for nonstudied than studied objects, whereas moderately complex impossible objects showed no difference in classification accuracy, except following the longest duration where studied objects were classified more accurately than nonstudied objects. The conditions under which priming was observed for possible and impossible objects was discussed in terms of stimulus complexity and the ease of generating structural representations of the stimuli and the presence of a general response bias.
Neurofilaments and Orthograde Transport Are Reduced in Ventral Root Axons of Transgenic Mice that Express Human SOD1 with a G93A Mutation

Mice engineered to express a transgene encoding a human Cu/Zn superoxide dismutase (SOD1) with a Gly93 → Ala (G93A) mutation found in patients who succumb to familial amyotrophic lateral sclerosis (FALS) develop a rapidly progressive and fatal motor neuron disease (MND) similar to amyotrophic lateral sclerosis (ALS). Hallmark ALS lesions such as fragmentation of the Golgi apparatus and neurofilament (NF)-rich inclusions in surviving spinal cord motor neurons as well as the selective degeneration of this population of neurons were also observed in these animals. Since the mechanism whereby mutations in SOD1 lead to MND remains enigmatic, we asked whether NF inclusions in motor neurons compromise axonal transport during the onset and progression of MND in a line of mice that contained ∼30% fewer copies of the transgene than the original G93A (Gurney et al., 1994). The onset of MND was delayed in these mice compared to the original G93A mice, but they developed the same neuropathologic abnormalities seen in the original G93A mice, albeit at a later time point with fewer vacuoles and more NF inclusions. Quantitative Western blot analyses showed a progressive decrease in the level of NF proteins in the L5 ventral roots of G93A mice and a concomitant reduction in axon caliber with the onset of motor weakness. By ∼200 d, both fast and slow axonal transports were impaired in the ventral roots of these mice coincidental with the appearance of NF inclusions and vacuoles in the axons and perikarya of vulnerable motor neurons. This is the first demonstration of impaired axonal transport in a mouse model of ALS, and we infer that similar impairments occur in authentic ALS. Based on the temporal correlation of these impairments with the onset of motor weakness and the appearance of NF inclusions and vacuoles in vulnerable motor neurons, the latter lesions may be the proximal cause of motor neuron dysfunction and degeneration in the G93A mice and in FALS patients with SOD1 mutations.
Shared genomic outliers across two divergent population clusters of a highly threatened seagrass

The seagrass, Zostera capensis, occurs across a broad stretch of coastline and wide environmental gradients in estuaries and sheltered bays in southern and eastern Africa. Throughout its distribution, habitats are highly threatened and poorly protected, increasing the urgency of assessing the genomic variability of this keystone species. A pooled genomic approach was employed to obtain SNP data and examine neutral genomic variation and to identify potential outlier loci to assess differentiation across 12 populations across the ∼9,600 km distribution of Z. capensis. Results indicate high clonality and low genomic diversity within meadows, which combined with poor protection throughout its range, increases the vulnerability of this seagrass to further declines or local extinction. Shared variation at outlier loci potentially indicates local adaptation to temperature and precipitation gradients, with Isolation-by-Environment significantly contributing towards shaping spatial variation in Z. capensis. Our results indicate the presence of two population clusters, broadly corresponding to populations on the west and east coasts, with the two lineages shaped only by frequency differences of outlier loci. Notably, ensemble modelling of suitable seagrass habitat provides evidence that the clusters are linked to historical climate refugia around the Last Glacial Maxi-mum. Our work suggests a complex evolutionary history of Z. capensis in southern and eastern Africa that will require more effective protection in order to safeguard this important ecosystem engineer into the future.
Design of low power CMOS LC VCO for direct conversion transceiver

A low power 1.2 GHz LC differential voltage-controll ed oscillator (VCO) is designed for a direct conversion transceiver in 130 nm CMOS processes. It consists of a complementary current reuse structure and LC tank circuit. To produce a good tuning range and reduce losses in the tank circuit, modied inversion mode PMOS varactors and an on-chip inductor are designed with a better quality factor. The icker noise is reduced by the PMOS tail current source connected in parallel with the capacitance. The 1.2 GHz LC VCO achieves a phase noise value of {132 dBc/Hz at 1 MHz offset and the tuning range of the oscillator is from 1.128 to 1.27 GHz for the control voltage, tuning from 0 to 1.5 V. It dissipates 245 W power at 0.8 V supply voltage and thegure of merit is calculated as {199.7 dBc/Hz.
Bayesian duration modeling and learning for speech recognition

We present Bayesian duration modeling and learning for speech recognition under nonstationary speaking rates and noise conditions. In this study, the Gaussian, Poisson and gamma distributions are investigated, to characterize duration models. The maximum a posteriori (MAP) estimate of the gamma duration model is developed. To exploit the sequential learning, we adopt the Poisson duration model, incorporated with gamma prior density, which belongs to the conjugate prior family. When the adaptation data are sequentially observed, the gamma posterior density is produced for twofold advantages. One is to determine the optimal quasi-Bayes (QB) duration parameter, which can be merged in HMM's for speech recognition. The other one is to build the updating mechanism of gamma prior statistics for sequential learning. An expectation-maximization algorithm is applied to fulfill parameter estimation. In the experiments, the proposed Bayesian approaches significantly improve the speech recognition performance of Mandarin broadcast news. Batch and sequential learning are investigated for MAP and QB duration models, respectively.
Diffusion-Weighted Imaging with Sensitivity Encoding (SENSE) for Detecting Cranial Bone Marrow Metastases: Comparison with T1-Weighted Images

Objective This study was designed to determine whether diffusion-weighted imaging (DWI) with sensitivity encoding (SENSE) could detect bone marrow involvement in patients with cranial bone marrow (CBM) metastases. DWI results obtained were compared with T1-weighted imaging (T1WI) findings. Materials and Methods DWI with sensitivity encoding (SENSE; b value = 1,000) was performed consecutively in 13 patients with CBM metastases diagnosed pathologically and radiologically. CBM lesions were dichotomized according to the involved site, i.e., skull base or calvarium. Two radiologists qualitatively evaluated the relative conspicuousness of CBM lesions and image qualities in B0 and in isotropic DWI and in T1WI. According to region of interest analysis of normal and pathologic marrow for these three sequences, absolute signal difference percentages (SD%) were calculated to quantitatively analyze lesion contrast. Results All 20 lesions in 13 patients with CBM metastases revealed abnormal DWI signals in areas corresponding to T1WI abnormalities. Both skull base and calvarial lesions provided better lesion conspicuousness than T1WI and B0 images. Although the image quality of DWI was less satisfactory than that of T1WI, relatively good image qualities were obtained. Quantitatively, B0 images (SD%, 82.1 ±7.9%) showed better lesion contrast than isotropic DWI (SD%, 71.4 ±13.7%) and T1WI (SD%, 65.7 ±9.3%) images. Conclusion For scan times of less than 30 seconds, DWI with SENSE was able to detect bone marrow involvement, and was superior to T1WI in terms of lesion conspicuity. DWI with SENSE may be helpful for the detection of cranial bone/bone marrow metastases when used in conjunction with conventional MR sequences.
Faculteit Economie En Bedrijfskunde Tweekerkenstraat 2 B-9000 Gent a Hybrid Scatter Search Heuristic for Personalized Crew Rostering in the Airline Industry

The crew scheduling problem in the airline industry is extensively investigated in the operations research literature since efficient crew employment can drastically reduce operational costs of airline companies. Given the flight schedule of an airline company, crew scheduling is the process of assigning all necessary crew members in such a way that the airline is able to operate all its flights and constructing a roster line for each employee minimizing the corresponding overall cost for personnel. In this paper, we present a scatter search algorithm for the airline crew rostering problem. The objective is to assign a personalized roster to each crew member minimizing the overall operational costs while ensuring the social quality of the schedule. We combine different complementary meta-heuristic crew scheduling combination and improvement principles. Detailed computational experiments of all characteristics of the procedure are presented. Moreover, we compare the proposed scatter search algorithm with an exact branch-and-price procedure and a steepest descent variable neighborhood search.
The BBN RT04 English broadcast news transcription system

This paper describes the BBN English Broadcast News transcription system developed for the EARS Rich Transcription 2004 (RT04) evaluation. In comparison to the BBN RT03 system, we achieved around 22% relative reduction in word error rate for all EARS BN development test sets. The use of additional acoustic training data acquired through Light Supervision based on thousands of hours of found data made the biggest contribution to the improvement. Better audio segmentation, through the use of an online speaker clustering algorithm and chopping speaker turns into moderately long utterances, also contributed substantially to the improvement. Other contributions, even of modest size but adding up nicely, include using discriminative training for all acoustic models, using word duration as an additional knowledge source during N-best rescoring, and using updated lexicon and language models.
Design and Development of the Hardware for a Vision-based UAV Autopilot

Vision-based control of unmanned aerial vehicles has potential to increase their autonomy and safety during flight and landing, thus increasing their prevalence in various industries. This paper presents the design and development of the hardware for a vision-based autopilot. The aim was to develop a modular, low-cost system that consists of nonproprietary components. The main components are a custom switching module that interfaces between the autopilot and the RC receiver, a single-board computer for image analysis and control, and an autopilot for low-level servomotor control. Hardware-in-the-loop tests show that the switching module is capable of switching between the manual and vision-based control input. It can also return the control of the UAV to the operator when needed. Keywords-unmanned aerial vehicle; autopilot; UAV; fixedwing; vision-based control; visual servoing
How Faculty Members Develop Views on Internationalization: The Case of a Top-Ranked Indian Business School

Internationalization of higher education is considered to be among the most widely researched as well as one of the most misunderstood topics. In this study, we take a phenomenological approach to better understand what internationalization means to faculty members at a leading business school in India, as the country has emerged as one of the largest providers of management education in the world today. This is important because faculty members are considered to be the key drivers of internationalization at their institutions. We find that internationalization means different things to different faculty members, and that these views are strongly shaped by each faculty member’s unique set of past international experiences. We are able to link these views to De Meyer’s three strategic drivers of globalization/internationalization, and find that for Indian management faculty, the desire to enrich the home base emerges as the dominant driver of internationalization, followed by the desire for global learning. Although not a prominent driver, the desire to leverage India’s unique knowledge base was also evident. We expect that these views will drive the future internationalization endeavors of this and other similar leading Indian business schools.
Explaining emergent behavior in a swarm system based on an inversion of the fluctuation theorem

A grand challenge in the field of artificial life is to find a general theory of emergent self-organizing systems. In this paper we try to explain the emergent behavior of a simulated swarm by applying methods based on the fluctuation theorem. Empirical results indicate that the swarm is able to produce negative entropy within an isolated sub-system due to ‘frozen accidents’. Individuals of the swarm are able to locally detect fluctuations of the global entropy measure and store them, if they are negative entropy productions. By accumulating these stored fluctuations over time the swarm as a whole is producing negative entropy and the system ends up in an ordered state. We claim that this indicates the existence of an inverted fluctuation theorem for emergent self-organizing dissipative systems. This approach bears the potential of general applicability.
Social Influence Analysis based on Facial Emotions

In this paper, we propose a method to analyse the social correlation among the group of people in any small gathering; such as business meetings, group discussion, etc.; Within such networks, correlation is build based on the tracked facial emotions of all the individuals in the network. The facial emotional feature extraction is based on active appearance model; whereas the approach for emotion detection lies in the dynamic and probabilistic framework of deep belief networks. Combining active appearance model with deep belief networks for emotion recognition gives higher recognition performance level compared with other methods. The analysis of change in facial emotions of all the individuals in the group help us to understand the hidden correlation among them, which is not observable with the naked eyes. Finally, we evaluate the system by comparing the results with ground truth of a scripted discussion. Also, the results obtained by our system effectively reflects the emotion propagation in the scripted discussion.