Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Reduplication in Bengali Corpus and their Semantic Analysis: A Rule Based Approach

In linguistic studies, reduplication generally means the repetition of any linguistic unit such as a phoneme, morpheme, word, phrase, clause or the utterance as a whole. The identification of reduplication is a part of general task of identification of multiword expressions (MWE). In the present work, reduplications have been identified from the Bengali corpus of the articles of Rabindranath Tagore. The present rule-based approach is divided into two phases. In the first phase, identification of reduplications has been done mainly at general expression level and in the second phase, their structural and semantics classifications are analyzed. The system has been evaluated with average Precision, Recall and FScore values of 92.82%, 91.50% and 92.15% respectively.
Learning Spatio-Temporal Features with Two-Stream Deep 3D CNNs for Lipreading

We focus on the word-level visual lipreading, which requires recognizing the word being spoken, given only the video but not the audio. State-of-the-art methods explore the use of end-to-end neural networks, including a shallow (up to three layers) 3D convolutional neural network (CNN) + a deep 2D CNN (e.g., ResNet) as the front-end to extract visual features, and a recurrent neural network (e.g., bidirectional LSTM) as the back-end for classification. In this work, we propose to replace the shallow 3D CNNs + deep 2D CNNs front-end with recent successful deep 3D CNNs --- two-stream (i.e., grayscale video and optical flow streams) I3D. We evaluate different combinations of front-end and back-end modules with the grayscale video and optical flow inputs on the LRW dataset. The experiments show that, compared to the shallow 3D CNNs + deep 2D CNNs front-end, the deep 3D CNNs front-end with pre-training on the large-scale image and video datasets (e.g., ImageNet and Kinetics) can improve the classification accuracy. Also, we demonstrate that using the optical flow input alone can achieve comparable performance as using the grayscale video as input. Moreover, the two-stream network using both the grayscale video and optical flow inputs can further improve the performance. Overall, our two-stream I3D front-end with a Bi-LSTM back-end results in an absolute improvement of 5.3% over the previous art on the LRW dataset.
Attack signature matching using graphics processors in high-performance intrusion detection systems

Network Intrusion Detection Systems (NIDS) which should perform time-consuming evaluation of every packet received from network have faced throughput challenge as a result of the increase in the speed of network communications and the high volume of Internet threats. In an NIDS, the most important and time-consuming processes are pattern matching and deep inspection of the header and the body of packets. Several analyses show that this process can take up to 75% of the time of processing packets. In this paper, relying on the processing power of general purpose graphics cards - which seem to be a better option compared to other hardware technologies like FPGAs with regard to speed, scalability, flexibility, ease of programming and price - and with the idea of having the signature-based detection engine of NIDS systems run on GPU rather than CPU, it is tried to present an efficient method to increase the speed of intrusion detection systems such as Snort. The proposed method provides a means to perform payload matching and non-payload matching of packets in a parallel platform on GPU, which can speed up the signature-based detection engine of Snort 3.6.
Occupational Exposure to Ethylene Oxide and Risk of Lymphoma

Background: Ethylene oxide, a high-volume commodity, is an established human carcinogen, although the relevant epidemiologic evidence is limited. Methods: We explored the association between occupational exposure to ethylene oxide and risk of lymphoma in a case-control study, including 2347 lymphoma cases first diagnosed in 1998–2004 and 2463 controls, from 6 European countries. The diagnosis of lymphoma was based on the 2001 World Health Organization Classification of lymphoma. Occupational exposure to ethylene oxide was retrospectively assessed by industrial hygienists and occupational physicians based on detailed self-reported information. We modeled risk of lymphoma with unconditional logistic regression analysis as a function of various exposure measures, adjusting for age, sex, and participating center. Results: Thirty-one cases and 27 controls (1.2% of the total study population) were defined as ever having been exposed to ethylene oxide (odds ratio = 1.3 [95% confidence interval [CI] = 0.7–2.1]). Lymphoma risk showed a 4.3-fold increase associated with medium-high frequency of exposure to ethylene oxide (95% CI = 1.4–13). Among major subtypes, chronic lymphocytic leukemia was consistently associated with ethylene oxide exposure, related in a dose-response manner to probability, frequency, and duration of exposure, as well as to cumulative exposure and (less definitively) with exposure intensity. Conclusions: Our results add to the evidence that ethylene oxide is a human carcinogen.
Hybrid 1D Plasmonic/Photonic Crystals are Responsive to Escherichia Coli

Photonic crystal-based biosensors hold great promise as valid and low-cost devices for real-time monitoring of a variety of biotargets. Given the high processability and easiness of read-out even for unskilled operators, these systems can be highly appealing for the detection of bacterial contaminants in food and water. Here, we propose a novel hybrid plasmonic/photonic device that is responsive to Escherichia coli, which is one of the most hazardous pathogenic bacterium. Our system consists of a thin layer of silver, a metal that exhibits both a plasmonic behavior and a well-known biocidal activity, on top of a solution processed 1D photonic crystal. We attribute the bio-responsivity to the modification of the dielectric properties of the silver film upon bacterial contamination, an effect that likely stems from the formation of polarization charges at the Ag/bacterium interface within a sort of bio-doping mechanism. Interestingly, this triggers a blue-shift in the photonic response. This work demonstrates that our hybrid plasmonic/photonic device can be a low-cost and portable platform for the detection of common contaminants in food and water.
High‐fat diet before and during pregnancy causes marked up‐regulation of placental nutrient transport and fetal overgrowth in C57/BL6 mice

Maternal overweight and obesity in pregnancy often result in fetal overgrowth, which increases the risk for the baby to develop metabolic syndrome later in life. However, the mechanisms underlying fetal overgrowth are not established. We developed a mouse model and hypothesized that a maternal high‐fat (HF) diet causes up‐regulation of placental nutrient trans‐port, resulting in fetal overgrowth. C57BL/6J female mice were fed a control (11% energy from fat) or HF (32% energy from fat) diet for 8 wk before mating and throughout gestation and were studied at embryonic day 18.5. The HF diet increased maternal adiposity, as assessed by fat pad weight, and circulating maternal leptin, decreased serum adiponectin concentrations, and caused a marked increase in fetal growth (+43%). The HF diet also increased transplacental transport of glucose (5‐fold) and neutral amino acids (10‐fold) in vivo. In microvillous plasma membranes (MVMs) isolated from placentas of HF‐fed animals, protein expression of glucose transporter 1 (GLUT1) was increased 5‐fold, and protein expression of sodium‐coupled neu‐tral amino acid transporter (SNAT) 2 was elevated 9‐fold. In contrast, MVM protein expression of GLUT 3 or SNAT4 was unaltered. These data suggest that up‐regulation of specific placental nutrient transporter isoforms constitute a mechanism linking maternal high‐fat diet and obesity to fetal overgrowth. —Jones, H. N. , Woollett, L. A. , Barbour, N. , Prasad, P. D. , Powell, T. L. , Jansson, T. High‐fat diet before and during pregnancy causes marked up‐regulation of placental nutrient transport and fetal overgrowth in C57/BL6 mice. FASEBJ. 23, 271‐278 (2009)
The relationship between low maternal serum 25-hydroxyvitamin D levels and gestational diabetes mellitus according to the severity of 25-hydroxyvitamin D deficiency

OBJECTIVE: To assess the relationship between low maternal serum 25-hydroxyvitamin D levels and gestational diabetes mellitus in Turkish pregnant women according to the severity of 25-hydroxyvitamin D deficiency and assess intact parathyroid hormone levels in women with gestational diabetes mellitus and controls with low and sufficient 25-hydroxyvitamin D levels. METHODS: We analyzed serum 25-hydroxyvitamin D and intact parathyroid hormone levels in 234 women with gestational diabetes mellitus and 168 controls. To define the deficiency status, 25-hydroxyvitamin D levels were further classified into severely deficient, deficient, insufficient and sufficient groups. RESULTS: Women with gestational diabetes mellitus had significantly lower 25-hydroxyvitamin D levels compared to controls (30.8±16.3 vs. 36.0±16.2 nmol/L). However, when subgroups of 25-hydroxyvitamin D were analyzed, gestational diabetes mellitus was significantly more common only in women with severely deficient 25-hydroxyvitamin D levels. After adjusting for covariates, only severely deficient 25-hydroxyvitamin D levels were independently associated with an increased relative risk of gestational diabetes mellitus. The relative risk of gestational diabetes mellitus in women with insufficient and deficient 25-hydroxyvitamin D levels was not statistically significant. Intact parathyroid hormone concentrations were also significantly higher in women with gestational diabetes mellitus compared to the controls (45.3±26.2 vs. 38.7±27.6 pg/ml). CONCLUSIONS: The results obtained from this study provide novel data indicating that only severely deficient maternal serum 25-hydroxyvitamin D levels are significantly associated with an elevated relative risk of gestational diabetes mellitus, even after adjusting for established risk factors of gestational diabetes mellitus.
Feature-Fused Context-Encoding Network for Neuroanatomy Segmentation

Automatic segmentation of fine-grained brain structures remains a challenging task. Current segmentation methods mainly utilize 2D and 3D deep neural networks. The 2D networks take image slices as input to produce coarse segmentation in less processing time, whereas the 3D networks take the whole image volumes to generated fine-detailed segmentation with more computational burden. In order to obtain accurate fine-grained segmentation efficiently, in this paper, we propose an end-to-end Feature-Fused Context-Encoding Network for brain structure segmentation from MR (magnetic resonance) images. Our model is implemented based on a 2D convolutional backbone, which integrates a 2D encoding module to acquire planar image features and a spatial encoding module to extract spatial context information. A global context encoding module is further introduced to capture global context semantics from the fused 2D encoding and spatial features. The proposed network aims to fully leverage the global anatomical prior knowledge learned from context semantics, which is represented by a structure-aware attention factor to recalibrate the outputs of the network. In this way, the network is guaranteed to be aware of the class-dependent feature maps to facilitate the segmentation. We evaluate our model on 2012 Brain Multi-Atlas Labelling Challenge dataset for 134 fine-grained structure segmentation. Besides, we validate our network on 27 coarse structure segmentation tasks. Experimental results have demonstrated that our model can achieve improved performance compared with the state-of-the-art approaches.
Reachability Analysis of Linear Hybrid Systems via Block Decomposition

Reachability analysis aims at identifying states reachable by a system within a given time horizon. This task is known to be computationally expensive for linear hybrid systems. Reachability analysis works by iteratively applying continuous and discrete post operators to compute states reachable according to continuous and discrete dynamics, respectively. In this article, we enhance both of these operators and make sure that most of the involved computations are performed in low-dimensional state space. In particular, we improve the continuous-post operator by performing computations in high-dimensional state space only for time intervals relevant for the subsequent application of the discrete-post operator. Furthermore, the new discrete-post operator performs low-dimensional computations by leveraging the structure of the guard and assignment of a considered transition. We illustrate the potential of our approach on a number of challenging benchmarks.
Can We Monitor Breathing During Sleep via Wi-Fi on Smartphone?

Over the last decade there has been high interest in home sleep monitoring among research community and society. Smartphone-based sleep monitoring seems to be especially attractive. However, smartphone should be able to register physiological processes for that, such as breathing or heartbeats. In this study, we tried to detect breathing based on the analysis of channel state information (CSI) of Wi-Fi connection between a smartphone and an access point. We collected data of 5 subjects with different body mass index (17–33) in base sleep postures: prone, supine, and side. The obtained CSI-based respiratory signal was compared with ground truth from a contact belt sensor. The mean correlation coefficient between the signals of 0.85 was achieved. The mean absolute difference between respiratory rates (breaths per epoch) is 0.39. The results show that it is possible to use smartphones without additional sensors for registering breathing based on CSI and potentially for sleep monitoring.
A Modular Deep Learning Approach for Extreme Multi-label Text Classification

Extreme multi-label classification (XMC) aims to assign to an instance the most relevant subset of labels from a colossal label set. Due to modern applications that lead to massive label sets, the scalability of XMC has attracted much recent attention from both academia and industry. In this paper, we establish a three-stage framework to solve XMC efficiently, which includes 1) indexing the labels, 2) matching the instance to the relevant indices, and 3) ranking the labels from the relevant indices. This framework unifies many existing XMC approaches. Based on this framework, we propose a modular deep learning approach SLINMER: Semantic Label Indexing, Neural Matching, and Efficient Ranking. The label indexing stage of SLINMER can adopt different semantic label representations leading to different configurations of SLINMER. Empirically, we demonstrate that several individual configurations of SLINMER achieve superior performance than the state-of-the-art XMC approaches on several benchmark datasets. Moreover, by ensembling those configurations, SLINMER can achieve even better results. In particular, on a Wiki dataset with around 0.5 millions of labels, the precision@1 is increased from 61% to 67%.
A Systems Engineering Approach to Viticulture On-Farm Irrigation

Abstract Water resources management presents an important research topic, because our planet is facing a serious water crisis. About 70% of all fresh water usage goes towards agriculture. Moreover, low water application efficiency are well reported in the literature. Improving on-farm irrigation efficiency can make a substantial contribution to a more sustainable utilization of the world's fresh water resources. It is argued that systems engineering principles can assist to realize the goal of improving water efficiency or produce quality in on-farm irrigation whilst maintaining productivity and quality of service. In approaching this resource management problem, wireless sensor network technologies and automation ideas are combined to improve economic productivity in dairy, horticulture and viticulture industries in such a way as to support continued growth in these major food industries in the face of a competitive water market. This paper reports the early progress of the project on smart irrigation system for viticulture and initial attempt in modeling the viticulture soil-water dynamics is briefly discussed. The results obtained are encouraging indicating that water automation is a promising technology.
Representation of white- and black-box adversarial examples in deep neural networks and humans: A functional magnetic resonance imaging study

The recent success of brain-inspired deep neural networks (DNNs) in solving complex, high-level visual tasks has led to rising expectations for their potential to match the human visual system. However, DNNs exhibit idiosyncrasies that suggest their visual representation and processing might be substantially different from human vision. One limitation of DNNs is that they are vulnerable to adversarial examples, input images on which subtle, carefully designed noises are added to fool a machine classifier. The robustness of the human visual system against adversarial examples is potentially of great importance as it could uncover a key mechanistic feature that machine vision is yet to incorporate. In this study, we compare the visual representations of white- and black-box adversarial examples in DNNs and humans by leveraging functional magnetic resonance imaging (fMRI). We find a small but significant difference in representation patterns for different (i.e. white- versus black-box) types of adversarial examples for both humans and DNNs. However, human performance on categorical judgment is not degraded by noise regardless of the type unlike DNN. These results suggest that adversarial examples may be differentially represented in the human visual system, but unable to affect the perceptual experience.
Regulation of accretion by its outflow in a symbiotic star: the 2016 outflow fast state of MWC 560

How are accretion discs affected by their outflows? To address this question for white dwarfs accreting from cool giants, we performed optical, radio, X-ray, and ultraviolet observations of the outflow-driving symbiotic star MWC 560 (≡V694 Mon) during its 2016 optical high state. We tracked multi-wavelength changes that signalled an abrupt increase in outflow power at the initiation of a months-long outflow fast state, just as the optical flux peaked: (1) an abrupt doubling of Balmer absorption velocities; (2) the onset of a 20 μJy per month increase in radio flux; and (3) an order-of-magnitude increase in soft X-ray flux. Juxtaposing to prior X-ray observations and their coeval optical spectra, we infer that both high-velocity and low-velocity optical outflow components must be simultaneously present to yield a large soft X-ray flux, which may originate in shocks where these fast and slow absorbers collide. Our optical and ultraviolet spectra indicate that the broad absorption-line gas was fast, stable, and dense (≳106.5 cm−3) throughout the 2016 outflow fast state, steadily feeding a lower density (≲105.5 cm−3) region of radio-emitting gas. Persistent optical and ultraviolet flickering indicate that the accretion disc remained intact. The stability of these properties in 2016 contrasts to their instability during MWC 560’s 1990 outburst, even though the disc reached a similar accretion rate. We propose that the self-regulatory effect of a steady fast outflow from the disc in 2016 prevented a catastrophic ejection of the inner disc. This behaviour in a symbiotic binary resembles disc/outflow relationships governing accretion state changes in X-ray binaries.
InVitro characterization of colistin resistance and transfer of neomycin resistance in Escherichia coli O149 strains

Neomycin, an aminoglycoside, is use at farm level for Escherichia coli treatment of piglets postweaning diarrhea, with 40% of unsuccessfully treatment due to the antimicrobial resistance. To overcome this situation, veterinarians use colistin also call polymyxin E, but this antibiotic is not homologated in Canada, even if it seems to be effective against E.coli. The described resistance to polymyxin is associated to a modification of the LPS core and the lipid A regions in the bacteria. For Salmo nella, those modifications are associated to the two components system PmrA-PmrB. This system is known in E.coli but not yet reported to be implicated in colistin resistance. The neomycin resistance is linked to enzymatic modifications associated to genes located on plasmids. This study has for objectives to investigate the acquisition of colistin resistance and study the transfer of neomycin resistance between E.coli strains and other enterobacteria. E.coli O149 strains isolated from clinical cases (2008 to 2011) were used and susceptibility testing of strains was performed by the disk diffusion method. E-test and a micro-dilution method were used to determine the minimal inhibitory concentration (MIC) of colistin and neomycin respectively. All tested strains had a MIC higher than 128 ppm for neomycin whereas MICs were between 0.064 to 0.128 ppm for colistin, indicating that all strains were resistance to neomycin but all susceptible to colistin. In these isolates, neomycin antimicrobial resistance genes aac(3)-IV, apkA1 and aphA2 have been detected using PCR. Conjugation experiments are presently being performed. Colistin natural mutants (n=22) were created through serial passages on LB agar with 25xMIC. Sequencing of the PmrA-PmrB region of these mutants was performed to identify mutations. Three PmrA and seven PmrB mutations were for the first time reported in E.coli O149. Others mutants are still under investigation for other possible resistance mechanism.
Charmonium-nucleon interactions from 2+1 flavor lattice QCD

The charmonium-nucleon interaction is studied by the time-dependent HAL QCD method. We use a larger lattice volume and the relativistic heavy quark action for charm quark to obtain less systematic errors than those in our previous study. As a result, the sizable J/\(\psi\)N hyperfine split- ting is observed, indicating that the spin-spin interaction is important to understand this system quantitatively. No J/\(\psi\)N or \(\eta_c\)N bound state is observed below the thresholds as in the previous results.
Two short children born small for gestational age with insulin-like growth factor 1 receptor haploinsufficiency illustrate the heterogeneity of its phenotype.

CONTEXT Small for gestational age (SGA)-born children comprise a heterogeneous group in which only few genetic causes have been identified. OBJECTIVE To determine copy number variations in 18 growth-related genes in 100 SGA children with persistent short stature. METHODS Copy number variations in 18 growth-related genes (SHOX, GH1, GHR, IGF1, IGF1R, IGF2, IGFBP1-6, NSD1, GRB10, STAT5B, ALS, SOCS2, and SOCS3) were determined by an "in house" multiplex ligation-dependent probe amplification kit. The deletions were further characterized by single-nucleotide polymorphism array analysis. RESULTS Two heterozygous de novo insulin-like growth factor 1 receptor (IGF1R) deletions were found: a deletion of the complete IGF1R gene (15q26.3, exons 1-21), including distally flanking sequences, and a deletion comprising exons 3-21, extending further into the telomeric region. In one case, serum IGF-I was low (-2.78 sd score), probably because of a coexisting growth hormone (GH) deficiency. Both children increased their height during GH treatment (1 mg/m(2) per day). Functional studies in skin fibroblast cultures demonstrated similar levels of IGF1R autophosphorylation and a reduced activation of protein kinase B/Akt upon a challenge with IGF-I in comparison with controls. CONCLUSIONS IGF1R haploinsufficiency was present in 2 of 100 short SGA children. GH therapy resulted in moderate catch-up growth in our patients. A review of the literature shows that small birth size, short stature, small head size, relatively high IGF-I levels, developmental delay, and micrognathia are the main predictors for an IGF1R deletion.