Miracle-dz/UniFAR-FTUs huggingface dataset:
Streamlining the Placement of Spanish Heritage Language Learners

The rise in Spanish heritage language and Spanish native speaker (SHL/SNS) learners in language programs traditionally designed for second language (L2) learners has prompted an interest in the development of a more inclusive placement exam for diverse learners. Some practitioners opt for an instrument that compiles a comprehensive profile of the learner’s ability. Taking the perspective of the natural acquisition of SHL/SNS of select linguistic areas as well as background information on the learners’ exposure to Spanish, we developed a simplified placement exam that efficiently identifies learners of different backgrounds and abilities. A one-way ANOVA demonstrates that the small number of items developed in the test effectively index different levels of ability. Additional informal analyses indicate that once the parameters for course recommendations are applied, levels of ability can still be observed after the SHL/SNS and L2 learners are separated. The present article outlines the approach and outcomes of the placement test to illustrate how an in-house instrument can be designed to meet the needs of an institution’s programs and diverse learners.
Histopathological survey of cultured shrimps in Cochin, Kerala

A disease survey was conducted using histopathological tools in a cross section of shrimp farming area-in Cochin during March to June, 2001. Shrimp samples collected from 26 farms comprised of Penaeus indicus, P. monodon, and Metapenaeus dobsoni. The major pathological condition recorded was the presence of chronic inflammatory lesions characteristic of systemic bacterial infection and was observed in 42% of the farms surveyed. In 19% of the farms, hepatopancreatic pathology, typical of oral/enteric vibriosis was recorded. Mortality of P.rnonodon due to mixed infection of MBV and vibriosis was recorded in one farm. Fouling of shrimps by the ciliate protozoan, Zoothamnium could be detected only in 8% of the cases. Abnormal conditions such as spontaneous muscle necrosis, dark coloured gills and brown discolouration of the shell were recorded in 36% of the farms. White spot viral inclusions were not recorded in the target tissues of the samples examined during the period of survey. No apparent pathological conditions were recorded in 27% of the farms surveyed.
Visual and Semantic Knowledge Transfer for Large Scale Semi-Supervised Object Detection

Deep CNN-based object detection systems have achieved remarkable success on several large-scale object detection benchmarks. However, training such detectors requires a large number of labeled bounding boxes, which are more difficult to obtain than image-level annotations. Previous work addresses this issue by transforming image-level classifiers into object detectors. This is done by modeling the differences between the two on categories with both image-level and bounding box annotations, and transferring this information to convert classifiers to detectors for categories without bounding box annotations. We improve this previous work by incorporating knowledge about object similarities from visual and semantic domains during the transfer process. The intuition behind our proposed method is that visually and semantically similar categories should exhibit more common transferable properties than dissimilar categories, e.g. a better detector would result by transforming the differences between a dog classifier and a dog detector onto the cat class, than would by transforming from the violin class. Experimental results on the challenging ILSVRC2013 detection dataset demonstrate that each of our proposed object similarity based knowledge transfer methods outperforms the baseline methods. We found strong evidence that visual similarity and semantic relatedness are complementary for the task, and when combined notably improve detection, achieving state-of-the-art detection performance in a semi-supervised setting.
The Difference Secretory Immunoglobulin A between Faeces Sample of Full Breastfeeding and Mixed Feeding Infant

Newborn infants immune systems are immature and inadequate at birth. Infants have limited abilities to infectious challenges to respond effectively and quickly, which explain infants ongoing susceptibility to infections. Passive immunity is provided for infants through IgA and other antimicrobial peptides in breast milk, particularly colostrum. The purpose of this study is to determine the differences between the levels of secretory immunoglobulin A from faeces of full breastfeeding and mixed feeding infants. Design of this study was pre-test and post-test design. The sample size was 38 neonates in community health center work areas, selected by consecutive sampling. The level of secretory IgA was measured by ELISA method, then analyzed using t-test. The results showed that no significant differences levels of secretory IgA on 0 day from faeces of full breastfeeding and mixed feeding (p-value=0.141), the levels of secretory IgA on 28 days was higher in infants of full breastfeeding than mixed feeding (p-value=0.000), delta levels of secretory IgA was higher full breastfeeding infants than mixed feeding (p=0.000). Levels of secretory immunoglobulin A of full breastfeeding infants faeces was higher than mixed feeding infants. Keywords: Secretory immunoglobulin A, Full breastfeeding, Mixed feeding
NCEN 27(8).book(NCEN_A_51048.fm)

Deficient learning and memory are frequently reported as a consequence of traumatic brain injury (TBI). Because of the diffuse nature of the injury, patients with TBI are not the ideal group for studying brain-behavior relations. Nevertheless, characterization of the memory breakdown following TBI could contribute to the assessment and rehabilitation of this patient population. It is well documented that memory is not a unitary system. Accordingly, in this article I review studies that have investigated the long-term effect of moderate to severe TBI on different memory aspects, including explicit and implicit tests of memory. This review demonstrates that TBI affects a large range of memory aspects. One of the conclusions is that the memory impairment observed in TBI patients could be viewed, at least to some degree, as a consequence of a more general cognitive deficit. Thus, unlike patients suffering from global amnesia, memory in patients with TBI is not selectively impaired. Nevertheless, it is possible to detect a subgroup of patients that do meet the criteria of amnesia. However, the most common vulnerable memory processes following TBI very much resemble the memory deficits reported in patients following frontal lobe damage, e.g., difficulties in applying active or effortful strategy in the learning or retrieval process. The suggested similarity between patients with TBI and those suffering from frontal lobe injury should be viewed cautiously; considering the nature of TBI, patients suffering from such injuries are not a homogeneous group. In view of this limitation, the future challenge in this field will be to identify subgroups of patients, either a priori according to a range of factors such as severity of injury, or a posteriori based on their specific memory deficit characteristics. Such a research approach has the potential of explaining much of the variability in findings reported in the literature on the effect of TBI on memory.
Genomic and Molecular Analysis of the Exopolysaccharide Production in the Bacterium Thauera aminoaromatica MZ1T

To identify and verify the genes responsible for thaueran formation and export, transposon mutagenesis, gene cloning and expression as well as reverse transcriptase quantitative real time PCR were performed in this study. Through transposon mutagenesis, 5 floc-deficient mutants were generated and their interrupted genes were identified by arbitrary PCR. Putative thaueran genes were also physically localized in fosmid library clones and induced for expression in non-EPS producing host cells. Immunoblotting tests showed a possible but not significant expression of thaueran in its original host cells. The mutants were further used in reverse transcriptase quantitative real time PCR to investigate the relationship between the abundance of EPS or floc in the liquid culture and the expression level of putative thaueran genes identified from genome annotation. The data of the treatments under different conditions, such as different temperatures, different media and among the mutants, a nice correlation of the amount of floc in the liquid culture and the expression level of the tested genes was revealed.
Influence of film wrapping on quality maintenance of Salustiana oranges under shelf-life conditions

"Salustiana" oranges were dipped in a 500 mg/L imazalil emulsion and wrapped in polystyrene trays using three different plastic films: Omni, MR and MY, with high, medium and low permeability to gases and water vapour, respectively. Fruits were then stored at ambient temperature (18°-20°C) and relative humidity (60-65%) for 6 weeks. MY film created in-package anaerobic conditions (about 10% CO 2 and 8% 0 2 , increased the rate of deterioration of internal quality parameters and promoted pathogen development, even in fruits treated with imazalil (28% decayed fruits after 6 weeks). Omni film did not alter the nutritional composition, but had little influence in reducing the transpiration rate as fruits at the end of storage lost about 14% weight, compared to 18% for non-wrapped fruits and 2% for those wrapped with the other two films. Very positive results were obtained with the imazalil-MR film combination, which let the fruits maintain their initial chemical and aesthetical characteristics during the entire storage time and limited loss due to decay to about 1%.
Growth of structures and spherical collapse in the Galileon Ghost Condensate model

We present a detailed study of the collapse of a spherical matter overdensity and the non-linear growth of large scale structures in the Galileon ghost condensate (GGC) model. This model is an extension of the cubic covariant Galileon (G3) which includes a field derivative of type $(\nabla_\mu\phi\nabla^\mu\phi)^2$ in the Lagrangian. We find that the cubic term activates the modifications in the main physical quantities and the additional term has an extended impact in the evolution of the latter. The GGC model shows largely mitigated effects in the linearised critical density contrast, non-linear effective gravitational coupling and the virial overdensity with respect to G3 but still preserves peculiar features with respect to the standard $\Lambda$CDM cosmological model, e.g., both the linear critical density contrast and the virial overdensity are larger than the ones in $\Lambda$CDM. The results of the spherical collapse model are then used to predict the evolution of the halo mass function, non-linear matter and lensing power spectra. While at low masses the GGC model presents about 10% fewer objects with respect to $\Lambda$CDM, at higher masses for $z>0$ it predicts 10% ($z=0.5$)-20% ($z=1$) more objects per comoving volume. Using a phenomenological approach to include the screening effect in the matter power spectrum, we show that the difference induced by the modifications of gravity are strongly dependent on the screening scale and that differences can be up to 60% with respect to $\Lambda$CDM. These differences translate to the lensing power spectrum where qualitatively the largest differences with respect to the standard cosmological model are for $\ell<10^3$. Depending on the screening scale, they can reach 60% on larger angular scales and then decrease for growing $\ell$. The results are obtained for the best fit parameters from cosmological data for each model.
Scan-based Semantic Segmentation of LiDAR Point Clouds: An Experimental Study

Autonomous vehicles need to have a semantic understanding of the three-dimensional world around them in order to reason about their environment. State of the art methods use deep neural networks to predict semantic classes for each point in a LiDAR scan. A powerful and efficient way to process LiDAR measurements is to use two-dimensional, image-like projections. In this work, we perform a comprehensive experimental study of image-based semantic segmentation architectures for LiDAR point clouds. We demonstrate various techniques to boost the performance and to improve runtime as well as memory constraints. First, we examine the effect of network size and suggest that much faster inference times can be achieved at a very low cost to accuracy. Next, we introduce an improved point cloud projection technique that does not suffer from systematic occlusions. We use a cyclic padding mechanism that provides context at the horizontal field-of-view boundaries. In a third part, we perform experiments with a soft Dice loss function that directly optimizes for the intersection-over-union metric. Finally, we propose a new kind of convolution layer with a reduced amount of weight-sharing along one of the two spatial dimensions, addressing the large difference in appearance along the vertical axis of a LiDAR scan. We propose a final set of the above methods with which the model achieves an increase of 3.2% in mIoU segmentation performance over the baseline while requiring only 42% of the original inference time.
Differential Network Learning Beyond Data Samples

Learning the change of statistical dependencies between random variables is an essential task for many real-life applications, mostly in the high dimensional low sample regime. In this paper, we propose a novel differential parameter estimator that, in comparison to current methods, simultaneously allows (a) the flexible integration of multiple sources of information (data samples, variable groupings, extra pairwise evidence, etc.), (b) being scalable to a large number of variables, and (c) achieving a sharp asymptotic convergence rate. Our experiments, on more than 100 simulated and two real-world datasets, validate the flexibility of our approach and highlight the benefits of integrating spatial and anatomic information for brain connectome change discovery and epigenetic network identification.
Reflection Features in the X-Ray Spectrum of Fairall 9 and Implications for Tests of General Relativity

X-ray reflection spectroscopy is potentially a powerful tool to probe the spacetime geometry around astrophysical black holes and test general relativity in the strong field regime. However, precision tests of general relativity are only possible if we employ the correct astrophysical model and if we can limit the systematic uncertainties. It is thus crucial to select the sources and the observations most suitable for these tests. In this work, we analyze simultaneous observations of XMM-Newton and NuSTAR of the supermassive black hole in Fairall 9. This source has a number of properties that make it a promising candidate for tests of general relativity using X-ray reflection spectroscopy. Nevertheless, we find that with the available data there is not a unique interpretation of the spectrum of Fairall 9, which prevents, for the moment, the use of this source for robust tests of general relativity. This issue may be solved by future X-ray missions with a higher energy resolution near the iron line.
Dropout as an Implicit Gating Mechanism For Continual Learning

In recent years, neural networks have demonstrated an outstanding ability to achieve complex learning tasks across various domains. However, they suffer from the "catastrophic forgetting" problem when they face a sequence of learning tasks, where they forget the old ones as they learn new tasks. This problem is also highly related to the "stability-plasticity dilemma". The more plastic the network, the easier it can learn new tasks, but the faster it also forgets previous ones. Conversely, a stable network cannot learn new tasks as fast as a very plastic network. However, it is more reliable to preserve the knowledge it has learned from the previous tasks. Several solutions have been proposed to overcome the forgetting problem by making the neural network parameters more stable, and some of them have mentioned the significance of dropout in continual learning. However, their relationship has not been sufficiently studied yet. In this paper, we investigate this relationship and show that a stable network with dropout learns a gating mechanism such that for different tasks, different paths of the network are active. Our experiments show that the stability achieved by this implicit gating plays a very critical role in leading to performance comparable to or better than other involved continual learning algorithms to overcome catastrophic forgetting.1
Sparse Array Selection Across Arbitrary Sensor Geometries With Deep Transfer Learning

Sparse sensor array selection arises in many engineering applications, where it is imperative to obtain maximum spatial resolution from a limited number of array elements. Recent research shows that computational complexity of array selection is reduced by replacing the conventional optimization and greedy search methods with a deep learning network. However, in practice, sufficient and well-calibrated labeled training data are unavailable and, more so, for arbitrary array configurations. To address this, we adopt a deep transfer learning (TL) approach, wherein we train a deep convolutional neural network (CNN) with data of a source sensor array for which calibrated data are readily available and reuse this pre-trained CNN for a different, data-insufficient target array geometry to perform sparse array selection. Numerical experiments with uniform rectangular and circular arrays demonstrate enhanced performance of TL-CNN on the target model than the CNN trained with insufficient data from the same model. In particular, our TL framework provides approximately 20% higher sensor selection accuracy and 10% improvement in the direction-of-arrival estimation error.
Automatic low-bit hybrid quantization of neural networks through meta learning

Model quantization is a widely used technique to compress and accelerate deep neural network (DNN) inference, especially when deploying to edge or IoT devices with limited computation capacity and power consumption budget. The uniform bit width quantization across all the layers is usually sub-optimal and the exploration of hybrid quantization for different layers is vital for efficient deep compression. In this paper, we employ the meta learning method to automatically realize low-bit hybrid quantization of neural networks. A MetaQuantNet, together with a Quantization function, are trained to generate the quantized weights for the target DNN. Then, we apply a genetic algorithm to search the best hybrid quantization policy that meets compression constraints. With the best searched quantization policy, we subsequently retrain or finetune to further improve the performance of the quantized target network. Extensive experiments demonstrate the performance of searched hybrid quantization scheme surpass that of uniform bitwidth counterpart. Compared to the existing reinforcement learning (RL) based hybrid quantization search approach that relies on tedious explorations, our meta learning approach is more efficient and effective for any compression requirements since the MetaQuantNet only needs be trained once.
Tuning photon statistics with coherent fields

Photon correlations, as measured by Glauber's $n\mathrm{th}$-order coherence functions ${g}^{(n)}$, are highly sought to be minimized and/or maximized. In systems that are coherently driven, so-called blockades can give rise to strong correlations according to two scenarios based on level repulsion (conventional blockade) or interferences (unconventional blockade). Here, we show how these two approaches relate to the admixing of a coherent state with a quantum state such as a squeezed state for the simplest and most recurrent case. The emission from a variety of systems, such as resonance fluorescence, the Jaynes-Cummings model, or microcavity polaritons, as a few examples of a large family of quantum optical sources, are shown to be particular cases of such admixtures, that can further be doctored up externally by adding an amplitude- and phase-controlled coherent field with the effect of tuning the photon statistics from exactly zero to infinity. We show how such an understanding also allows to classify photon statistics throughout platforms according to conventional and unconventional features, with the effect of optimizing the correlations and with possible spectroscopic applications. In particular, we show how configurations that can realize simultaneously conventional and unconventional antibunching bring the best of both worlds: huge antibunching (unconventional) with large populations and being robust to dephasing (conventional).
Facial Expression Recognition with Deep Learning

One of the most universal ways that people communicate is through facial expressions. In this paper, we take a deep dive, implementing multiple deep learning models for facial expression recognition (FER). Our goals are twofold: we aim not only to maximize accuracy, but also to apply our results to the real-world. By leveraging numerous techniques from recent research, we demonstrate a state-of-the-art 75.8% accuracy on the FER2013 test set, outperforming all existing publications. Additionally, we showcase a mobile web app which runs our FER models on-device in real time.
Deep Learning-Based Packet Detection and Carrier Frequency Offset Estimation in IEEE 802.11ah

Wi-Fi systems based on the IEEE 802.11 standards are the most popular wireless interfaces that use Listen Before Talk (LBT) method for channel access. The distinctive feature of a majority of LBT-based systems is that the transmitters use preambles that precede the data to allow the receivers to perform packet detection and carrier frequency offset (CFO) estimation. Preambles usually contain repetitions of training symbols with good correlation properties, while conventional digital receivers apply correlation-based methods for both packet detection and CFO estimation. However, in recent years, data-based machine learning methods are disrupting physical layer research. Promising results have been presented, in particular, in the domain of deep learning (DL)-based channel estimation. In this paper, we present a performance and complexity analysis of packet detection and CFO estimation using both the conventional and the DL-based approaches. The goal of the study is to investigate under which conditions the performance of the DL-based methods approach or even surpass the conventional methods, but also, under which conditions their performance is inferior. Focusing on the emerging IEEE 802.11ah standard, our investigation uses both the standard-based simulated environment, and a real-world testbed based on Software Defined Radios.