Miracle-dz/UniFAR-FTUs huggingface dataset:
Auto-encoder pre-training of segmented-memory recurrent neural networks

The extended Backpropagation Through Time (eBPTT) learning algorithm for Segmented-Memory Recurrent Neural Networks (SMRNNs) yet lacks the ability to reliably learn long-term dependencies. The alternative learning algorithm, extended Real-Time Recurrent Learn- ing (eRTRL), does not suffer this problem but is computational very in- tensive, such that it is impractical for the training of large networks. The positive results reported with the pre-training of deep neural networks give rise to the hope that SMRNNs could also benefit of a pre-training proce- dure. In this paper we introduce a layer-local pre-training procedure for SMRNNs. Using the information latching problem as benchmark task, the comparison of random initialised and pre-trained networks shows the beneficial effect of the unsupervised pre-training. It significantly improves the learning of long-term dependencies in the supervised eBPTT training.
MDA-9/Syntenin-Slug transcriptional complex promote epithelial-mesenchymal transition and invasion/metastasis in lung adenocarcinoma

Melanoma differentiation-associated gene-9 (MDA-9)/Syntenin is a novel therapeutic target because it plays critical roles in cancer progression and exosome biogenesis. Here we show that Slug, a key epithelial-mesenchymal-transition (EMT) regulator, is a MDA-9/Syntenin downstream target. Mitogen EGF stimulation increases Slug expression and MDA-9/Syntenin nuclear translocation. MDA-9/Syntenin uses its PDZ1 domain to bind with Slug, and this interaction further leads to HDAC1 recruitment, up-regulation of Slug transcriptional repressor activity, enhanced Slug-mediated EMT, and promotion of cancer invasion and metastasis. The PDZ domains and nuclear localization of MDA-9/Syntenin are both required for promoting Slug-mediated cancer invasion. Clinically, patients with high MDA-9/Syntenin and high Slug expressions were associated with poor overall survival compared to those with low expression in lung adenocarcinomas. Our findings provide evidence that MDA-9/Syntenin acts as a pivotal adaptor of Slug and it transcriptionally enhances Slug-mediated EMT to promote cancer invasion and metastasis.
Spatial Entropy-Based Global and Local Image Contrast Enhancement

This paper proposes a novel algorithm, which enhances the contrast of an input image using spatial information of pixels. The algorithm introduces a new method to compute the spatial entropy of pixels using spatial distribution of pixel gray levels. Different than the conventional methods, this algorithm considers the distribution of spatial locations of gray levels of an image instead of gray-level distribution or joint statistics computed from the gray levels of an image. For each gray level, the corresponding spatial distribution is computed using a histogram of spatial locations of all pixels with the same gray level. Entropy measures are calculated from the spatial distributions of gray levels of an image to create a distribution function, which is further mapped to a uniform distribution function to achieve the final contrast enhancement. The method achieves contrast improvement in the case of low-contrast images; however, it does not alter the image if the image's contrast is high enough. Thus, it always produces visually pleasing results without distortions. Furthermore, this method is combined with transform domain coefficient weighting to achieve both local and global contrast enhancement at the same time. The level of the local contrast enhancement can be controlled. Several experiments on effects of contrast enhancement are performed. Experimental results show that the proposed algorithms produce better or comparable enhanced images than several state-of-the-art algorithms.
Effects of frequency and morphosyntactic structure on error detection, correction, and repetition in Swedish-speaking children

ABSTRACT Grammatical error detection and correction are often used to test explicit language knowledge. This study investigated effects of token frequency and error type in error detection, correction, and repetition, and performance on the three tasks were compared and related to models of metalinguistic awareness and development. Thirty Swedish-speaking 10-year-olds with typical language development participated in the study, which focused on four morphosyntactic errors: the infinitive instead of past tense for regular and irregular verbs, and the omission of the obligatory indefinite article in common and neuter gender noun phrases. Target verbs and nouns were of high or low frequency. Results showed significant effects of verb frequency in all tasks, and effects of noun gender for error detection, but not for correction and repetition. Children detected significantly more past-tense errors than they accurately corrected, but the opposite result was seen for noun phrase errors. The patterns of results both within and across tasks imply that implicit language knowledge affects performance, and that lexical frequency, even of familiar words, needs to be controlled when designing tasks for measuring grammatical knowledge. The particular challenge of the Swedish neuter noun phrase in language development and language processing needs to be further investigated.
NEURAL CONTROL OF BOOST CONVERTERS ' INPUT CURRENT

An application of neural networks in the cascade control structure of a boost converter is investigated. Inverse model of process realized using two-layered MLP neural network is used as the input current controller. A Levenberg–Marquardt learning algorithm is employed for off-line adjustment of the network weights and biases. This control structure ensures good performance in all operating regions and compensation of ripples in converter's input current caused by variations of the input voltage. Advantages of the proposed control structure are demonstrated through experimental comparison to the PI controller (with and without manually adjusted feed forward compensator).
Neural network based predictive control for nonlinear chemical process

The paper presents a neural network based predictive control (NPC) strategy to control nonlinear chemical process or system. Multilayer perceptron neural network (MLP) is chosen to represent a Nonlinear autoregressive with exogenous signal (NARX) model of a nonlinear process. Based on the identified neural model, a generalized predictive control (GPC) algorithm is implemented to control the composition in a continuous stirred tank reactor (CSTR), whose parameters are optimally determined by solving quadratic performance index using well known Levenberg-Marquardt and Quasi-Newton algorithm. Also an Instantaneous linearization based predictive control (IPC) strategy is discussed, in which an approximated linear model is extracted from nonlinear neural network by instantaneous linearization around operating points. The tracking performance of the NPC and IPC is tested using different amplitude step function as a reference signal on CSTR application and it is shown using simulation results, that the NPC strategy is more effective and robust than the IPC strategy.
N-Carbamylglutamate and l-Arginine Promote Intestinal Absorption of Amino Acids by Regulating the mTOR Signaling Pathway and Amino Acid and Peptide Transporters in Suckling Lambs with Intrauterine Growth Restriction.

BACKGROUND Previous studies have revealed that dietary N-carbamylglutamate (NCG) and l-arginine (Arg) improve intestinal integrity, oxidative state, and immune function in Hu suckling lambs with intrauterine growth restriction (IUGR). Whether these treatments alter intestinal nutrient absorption is unknown. OBJECTIVE The aim of this study was to determine the influence of dietary NCG and Arg treatment during the suckling period on intestinal amino acid (AA) absorption, alterations in the mechanistic target of rapamycin (mTOR) signaling pathway, and the abundance of AA and peptide transporters in IUGR lambs. METHODS On day 7 after birth, 48 newborn Hu lambs were selected from a cohort of 424 twin lambs. Normal-birth-weight and IUGR Hu lambs were allocated randomly (n = 12/group) to a control (4.09 ± 0.12 kg), IUGR (3.52 ± 0.09 kg), IUGR + 0.1% NCG (3.49 ± 0.11 kg), or IUGR + 1% Arg (3.53 ± 0.10 kg). RESULTS At day 28, compared with the IUGR group, the IUGR groups receiving NCG and Arg had 7.4% and 7.2% greater (P < 0.05) body weight, respectively. Compared with the IUGR group, the serum concentration of insulin was greater (P < 0.05) and the cortisol was lower (P < 0.05) in the IUGR groups receiving NCG and Arg. Compared with the IUGR group, the IUGR groups receiving NCG and Arg had 13.2%-62.6% greater (P < 0.05) serum concentrations of arginine, cysteine, isoleucine, and proline. Dietary NCG or Arg to IUGR lambs resulted in greater protein abundance (P < 0.05) of peptide transporter 1 (41.9% or 38.2%) in the ileum compared with the unsupplemented IUGR lambs, respectively. Furthermore, dietary NCG or Arg treatment normalized the IUGR-induced variation (P < 0.05) in the ileal ratio of phosphorylated mTOR to total mTOR protein. CONCLUSION Both NCG and Arg can help mitigate the negative effect of IUGR on nutrient absorption in neonatal lambs.
Learning of Compositional Hierarchies for the Modeling of Context Effects

Compositional, or part-whole, hierarchies underlie many forms of data, and representations involving these structures lie at the heart of much of the work in Artiicial Intelligence and Cognitive Science. However, despite their prevalence, general methods for learning such structures from data are scarce. This paper presents a learning and prediction system that learns compositional hierarchies and uses them to mediate context eeects in making predictions. The model is a hybrid system based on an early psychological neu-ral network system, the Interactive Activation model of context eeects in letter perception, and an elegant new symbolic hierarchy-generation algorithm called Sequitur. The composite system overcomes an important limitation in each of its parents.
Localized versus Locality-Preserving Subspace Projections for Face Recognition

Three different localized representation methods and a manifold learning approach to face recognition are compared in terms of recognition accuracy. The techniques under investigation are (a) local nonnegative matrix factorization (LNMF); (b) independent component analysis (ICA); (c) NMF with sparse constraints (NMFsc); (d) locality-preserving projections (Laplacian faces). A systematic comparative analysis is conducted in terms of distance metric used, number of selected features, and sources of variability on AR and Olivetti face databases. Results indicate that the relative ranking of the methods is highly task-dependent, and the performances vary significantly upon the distance metric used.
EÆcient Evaluation of Regular Path Expressions on Streaming XML Data

The adoption of XML promises to accelerate construction of systems that integrate distributed, heterogeneous data. Query languages for XML are typically based on regular path expressions that traverse the logical XML graph structure; the eAEcient evaluation of such path expressions is central to good query processing performance. Most existing XML query processing systems convert XML documents to an internal representation, generally a set of tables or objects; path expressions are evaluated using either index structures or join operations across the tables or objects. Unfortunately, the required index creation or join operations are often costly even with locally stored data, and they are especially expensive in the data integration domain, where the system reads data streamed from remote sources across a network, and seldom reuses results for subsequent queries. This paper presents the x-scan operator which eAEciently processes non-materialized XML data as it is being received by the data integration system. X-scan matches regular path expression patterns from the query, returning results in pipelined fashion as the data streams across the network. We experimentally demonstrate the bene ts of the x-scan operator versus the approaches used in current systems, and we analyze the algorithm's performance and scalability across a range of XML document types and queries.
Parcimonious time frequency quantization for phoneme and speaker classification

Some specificities of the speech signal may not well be addressed by the conventional speech processing. In this paper we focuses on a parcimonious representation of speech dynamics. We propose a novel coding strategy based on speech time-frequency quantization (TFQ) using simple Allen temporal interval algebra applied on subband voicing levels. Our compressed speech representation contains only 15 integers for a speech window up to 1 s long. We evaluate the discrimination power of these features for text independent speaker (60 hours, 62 speakers) or vowel recognition (1 hour, 6 vowels) on a referenced radio broadcast news used during evaluation campaign ESTER piloted by french intelligence agency. The 30 TFQ integers code ( feature compression factor (CF) of 26) classifies 62 speakers with an error reduction of 14% relatively to the random classifier, whereas the 390 float voicing features gives similar score. This illustrates the fact that TFQ may modelize co-articulation and speaking style. Preliminary model of independent speaker vowel identification using 15 integers TFQ features (CF of 6,4) gives an error reduction of 15,1% relatively to the random classifier, whereas the 48 float voicing level gives 31%. Further works to improve our parcimonious coding are then discussed.
Changes in Electroencephalogram Coherence in Adolescents With Autism Spectrum Disorder After a Social Skills Intervention

Autism spectrum disorder (ASD) is a developmental condition that affects social communication and behavior. There is consensus that neurological differences are present in ASD. Further, theories emphasize the mixture of hypo‐ and hyper‐connectivity as a neuropathologies in ASD [O'Reilly, Lewis, & Elsabbagh, 2017]; however, there is a paucity of studies specifically testing neurological underpinnings as predictors of success on social skills interventions. This study examined functional neural connectivity (electroencephalogram [EEG], coherence) of adolescents with ASD before and after the Program for the Education and Enrichment of Relational Skills (PEERS®) intervention, using a randomized controlled trial of two groups: an Experimental ASD (EXP) Group and a Waitlist Control ASD (WL) Group. The study had two purposes. First, the study aimed to determine whether changes in EEG coherence differed for adolescents that received PEERS® versus those that did not receive PEERS®. Results revealed a significant increase in connectivity in the occipital left to temporal left pair for the EXP group after intervention. Second, the study aimed to determine if changes in EEG coherence related to changes in behavior, friendships, and social skills measured by questionnaires. At post‐intervention, results indicated: (a) positive change in frontal right to parietal right coherence was linked to an increase in social skills scores; and (b) positive changes in occipital right to temporal right coherence and occipital left to parietal left coherence were linked to an increase in the total number of get‐togethers. Results of this study support utilizing neurobehavioral domains as indicators of treatment outcome.
Preservice physical education teacher socialization through an outdoor education field experience

ABSTRACT Outdoor education (OE) has an increased presence in physical education as it addresses the discipline’s national standards and promotes involvement lifetime physical activity. However, physical educators’ learning to teach OE is not well documented. The purpose of this study was to understand preservice physical educators’ experiences in and perspectives on an OE field experience. Participants included 13 third-year preservice physical educators (10 male, 3 female). Qualitative data were collected using focus group interviews, observations and guided reflections. Results indicated that preservice teachers (PSTs) perceived a controlling course environment that limited educational outcomes. They did, however, increase in confidence teaching OE, and applied skills learned in the teacher education program. With the exception of learning to be more adaptable, however, the PSTs did not perceive the experience as relevant to future teaching. Results are discussed within the framework of occupational socialization theory, and implications for practice are noted.
A design for 2-dimensional DCT transform kernel based on MicroBlaze soft core

The 2-dimensional DCT transform kernel based on the SOPC technique of Xilinx Company is designed by hardware software co-design, in which VitexII-pro FPGA is the physical carrier. Then, the transform kernel is integrated to the SOPC system, among which the device driver and the test program are written and compiled correspondingly. Finally, the programs are downloaded to the integrated board and, passed the board-level verification. The experiment results show that the above method meets the design requirements and has the potential application fields.
Individual and Perceptual Characteristics Influences On E-Learning Outcomes

This paper aims to study the influence of individual and perceptual characteristics on elearning outcomes. A conceptual model, based on the social cognitive theory, technology acceptance theory and training evaluation model, is developed and tested on 410 trainees, in Tunisian context. Data analysis conducted by structural equations shows the importance of motivation to learn, perceptual usefulness and ease of use as factors that influence e-learning outcomes.
Efficiency vs. Effectiveness in Terabyte-Scale Information Retrieval

We describe indexing and retrieval techniques that are suited to perform terabyte-scale information retrieval tasks on a standard desktop PC. Starting from an Okapi-BM25-based default baseline retrieval function, we explore both sides of the e(cid:11)ec-tiveness spectrum. On one side, we show how term proximity can be integrated into the scoring function in order to improve the search results. On the other side, we show how index pruning can be employed to increase retrieval e(cid:14)ciency { at the cost of reduced retrieval e(cid:11)ectiveness. We show that, although index pruning can harm the quality of the search results considerably, according to standard evaluation measures, the actual loss of precision, according to other measures that are more realistic for the given task, is rather small and is in most cases outweighed by the immense ef-(cid:12)ciency gains that come along with it.
Optical band gap and the Burstein–Moss effect in iodine doped PbTe using diffuse reflectance infrared Fourier transform spectroscopy

Optical absorption edge measurements are performed on I doped PbTe using diffuse reflectance infrared Fourier transform spectroscopy. The Burstein–Moss shift, an increase in the absorption edge (optical band gap) with increasing doping level, is explored. The optical gap increases on the order of 0.1 eV for doping levels ranging from 3 × 1018 to 2 × 1020 cm−3, relevant doping levels for good thermoelectric materials. Chemical potential is estimated from transport measurements—specifically, Hall effect and Seebeck coefficient—using a single band Kane model. In heavily doped semiconductors, it is well-known that the band gap shrinks with increasing doping level. This effect, known as band gap renormalization, is fit here using an n1/3 scaling law which reflects an electron–electron exchange interaction. The renormalization effect in these samples is shown to be more than 0.1 eV, on the same order of magnitude as the band gap itself. Existing models do not explain such large relative changes in band gap and are not entirely self-consistent. An improved theory for the renormalization in narrow gap semiconductors is required.
In search of suitable in vitro models to study germ cell movement across the blood-testis barrier

The movement of preleptotene/leptotene spermatocytes across the blood-testis barrier, also known as the Sertoli cell barrier, during stages VIII to XI of the seminiferous epithelial cycle is one of the most important cellular events taking place in the mammalian testis. Without the passage of spermatocytes, spermatogenesis would be halted, resulting in transient or permanent sterility. Unfortunately, we have very little knowledge on how preleptotene/leptotene spermatocytes cross the blood-testis barrier. While we know cytokines, proteases and androgens to mediate Sertoli cell junction restructuring, most data continue to be derived from experiments using Sertoli cells cultured alone in two dimensions. Thus, additional in vitro models which include germ cells must come into use. In this Commentary, we hope to shed new light on how we may better study spermatocyte movement across the BTB.
Pixel level multifocus image fusion based on variational decomposition

An image fusion Algorithm based on variational decomposition and energy of edge is presented in this paper. Firstly, each source image is decomposed into a geometrical component and a textured component by applying Rudin-Osher-Fatemi (ROF) model in combination with Chambolle's projection algorithm. Secondly, the corresponding components are fused separately. For fusing the geometrical components, a weighted average method is adopted. To construct the textured component, an indirect smoothing method is firstly applied to the composed textured component of each source image, and then a selection mode is used according to the local Energy of Laplacian measure. Finally, the sum of the fused components is calculated to obtain the fused resultant. Experiments show that the proposed algorithm works well in multi focus image fusion.
Single bit full adder design using 8 transistors with novel 3 transistors XNOR gate

In present work a new XNOR gate using three transistors has been presented, which shows power dissipation of 550. 7272µW in 0.35 µm technology with supply voltage of 3.3V. Minimum level for high output of 2.05V and maximum level for low output of 0.084V have been obtained. A single bit full adder using eight transistors has been designed using proposed XNOR cell, which shows power dissipation of 581. 542µW. Minimum level for high output of 1.97V and maximum level for low output of 0.24V is obtained for sum output signal. For carry signal maximum level for low output of 0.32V and minimum level for high output of 3.2V have been achieved. Simulations have been performed by using SPICE based on TSMC 0.35µm CMOS technology. Power consumption of proposed XNOR gate and full adder has been compared with earlier reported circuits and proposed circuit’s shows better performance in terms of power consumption and transistor count.
A constrained VFH algorithm for motion planning of autonomous vehicles

The Vector Field Histogram (VFH) is a classical motion planning algorithm which is widely used to handle the trajectory planning problem of mobile robots. However, the traditional VFH algorithm is rarely applied to autonomous vehicles due to the vehicle's well-known non-holonomic constraints, especially in urban environments. To address this problem, we propose a constrained VFH algorithm which takes both kinematic and dynamic constraints of the vehicle into consideration. The goal is achieved via two contributions that concern both kinematic and dynamic constraints of the vehicle. First, we develop a new active region for VFH to guarantee that all states within the region are reachable for the vehicle. Second, we improve the cost function to guide the search to favor feasible motion direction for the vehicle. The proposed algorithm is extensively tested in various simulated urban environments, and experimental results validate its efficiency.