Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of electrostatic Solvent Models to the

MNDO method has been carried out to determine the most stable structure of the two conforms of mesityl oxide. The final optimized geometry of the stable conform was used as input data for CNDO/S-CI solvent models. Four models depending on the effective virtual charge were used to study the effects of non-specific interactions between the mesityl oxide (solute) and solvent on electronic spectrum of the studied molecule. These models are Germer Model (GM), Tapia Model (TM), Modified Solvaton Model (MSM) & Modified Tapia Model (MTM). CNDO/S results showed that GM was not successful whereas TM has correctly predicted the blue shift on 1(nπ*) state but it failed for π→ π* transition. MSM & MTM gave good results for the effects of solvent on the electronic spectrum which indicate a blue shift for n→ π* and a red shift for π→ π* transitions when the polarity of the solvent increases, and these results are in excellent agreement with experimental data of solvent effects, but the results of MSM are more precise than that of MTM ). These results are also interpreted by the dipole moments values of the ground state ( so) and 1(nπ*) and 1(π π*) excited states. Introduction Electronic absorption spectra of α,β unsaturated ketones are characterized by an intense absorption band π→ π* in the 215250 nm region (molar absorption coefficient, max ε is usually in the range of 10,000-20,000 and a weak band n→ π* at 310-330 nm (Cooper, 1980). The most common α, β unsaturated ketone is the mesityl oxide (Figure 1). Brealey & Kasha (1955) concluded that the mesityl oxide have electronic transitions of n→ π* type that can be easily distinguished from π→ π* type by using solvent effect. It was found that if the non-polar solvent is replaced by a polar one, a measurable increase occurs in the energy of n → π* transitions of the mesityl oxide (blue shift) while π→ π* transitions are liable to a decrease in their energies (red shift). This effect is attributed to the specific and nonspecific interactions between the mesityl oxide (solute) and solvent (Eichardt, 1979). The non-specific interactions can be studied by quantum mechanics ( Tapia, 1980; Tapia, 1982), such studies comprise the effective charge (electrostatic) models. The main objective of the present work was therefore to investigate the non-specific interactions between the mesityl oxide and the solvent and the inclusion of solvent effects in the theoretical calculations of the electronic spectrum of mesityl oxide. The equilibrium geometry of the mesityl oxide (Figure 1) was deduced by minimization of
Vascular growth and remodeling mechanics

Elastin provides reversible elasticity to the large arteries during the cardiac cycle. Elastin is deposited during a period of developmental increases in blood pressure. Genetically-modified mice that do not express elastin (Eln-/-) have increased blood pressure, altered aortic mechanical behavior, and the smooth muscle cells (SMCs) overproliferate until they occlude the aortic lumen, causing Eln-/mice to die within a day of birth. We hypothesized that aortic remodeling in Eln-/mice could be ameliorated by limiting mechanically-stimulated remodeling through the reduction of blood pressure.
Differentially Private Chi-Squared Hypothesis Testing: Goodness of Fit and Independence Testing

Hypothesis testing is a useful statistical tool in determining whether a given model should be rejected based on a sample from the population. Sample data may contain sensitive information about individuals, such as medical information. Thus it is important to design statistical tests that guarantee the privacy of subjects in the data. In this work, we study hypothesis testing subject to differential privacy, specifically chi-squared tests for goodness of fit for multinomial data and independence between two categorical variables. We propose new tests for goodness of fit and independence testing that like the classical versions can be used to determine whether a given model should be rejected or not, and that additionally can ensure differential privacy. We give both Monte Carlo based hypothesis tests as well as hypothesis tests that more closely follow the classical chi-squared goodness of fit test and the Pearson chi-squared test for independence. Crucially, our tests account for the distribution of the noise that is injected to ensure privacy in determining significance. We show that these tests can be used to achieve desired significance levels, in sharp contrast to direct applications of classical tests to differentially private contingency tables which can result in wildly varying significance levels. Moreover, we study the statistical power of these tests. We empirically show that to achieve the same level of power as the classical non-private tests our new tests need only a relatively modest increase in sample size.
Cultivation of a wild strain of Auricularia cornea from Thailand

Auricularia (jelly fungi, ear mushroom) species are widely consumed, especially in Asia. Auricularia cornea is one of the cultivable species that was recently recorded from Thailand and is an edible mushroom used in Traditional Chinese Medicine. In this study, a strain of Auricularia cornea was collected from northern Thailand, confirmed with morphology, molecular data and was cultivated in the laboratory. Strain MFLUCC18-0346 was grown on PDA medium and spawn was prepared using Sorghum bicolor (sorghum) medium. Fruiting bodies were obtained by rubber sawdust bag cultivation. We found that the wild strain of A. cornea produced fruiting bodies at 25±1°C and 75–85% humidity. The first primordia of A. cornea was produced on day 76. The average yield of A. cornea was 242±37.52 g and the biological efficiency was 72.46±11.23% with six flushes in three months. The mushroom could be commercially cultivated; however, further research is needed to develop suitable agriculture wastes for increasing production yields and later the species could be introduced to Thai market for cultivation and medicinal use.
Sigma-if neural network as the use of selective attention technique in classification and knowledge discovery problems solving

The article presents the most important properties of Sigma-if neuron and neural network, which use a selective attention technique to solve classification problems. Abilities of Sigma-if neuron to perform active aggregation of input signals and to solve linearly inseparable problems are discussed. Variety of conducted experiments, during which Sigma-if network was compared with multilayer perceptron, are also presented. These experiments show benefits from using Sigma-if network instead of MLP, both in classification problems solving and in knowledge discovery from data.
The prevalence of four virulence genes in strains of Campylobacter jejuni isolated from broilers in Serbia

Campylobacter jejuni is a major cause of human diarrheal disease. The objective of this research study was to determine the prevalence of different virulence genes in isolates recovered from broiler meat at slaughterhouses in Serbia. Out of 115 Campylobacter spp. samples recovered, a total of 35 isolates were identified as C. jejuni on the basis of morphological, biochemical-based detection, multiplex PCR, and sequencing of the highly conserved region of the dnaJ gene encoding the DnaJ Hsp40 family protein. The isolates were screened for the presence of four pathogenic genes, namely flaA, cadF, cdtB, and cgtB, which are responsible for the expression of adherence, colonization, cytotoxin production in C. jejuni, and the onset of Guillain-Barre syndrome. The isolates showed a wide variation in the presence of these genes. All the isolates were positive for flaA. Furthermore, a high genetic heterogeneity in the C. jejuni population was found in this study, showing a pattern partially different from other reported virulence genes. Of the C. jejuni studied, 94.3%, 97.1% and 5.7% were positive for cadF, cdtB and cgtB, respectively. This study provides initial data on the prevalence and distribution of the flaA, cadF, cdtB, and cgtB genes in C. jejuni isolated from broiler meat in Serbia. key words: slaughterhouse; C. jejuni; virulence genes _____________________________________________________________________________________________ DOI: 10.24099/vet.arhiv. 0477
CELL CULTURE FROM TWO RED SEA BENTHIC INVERTEBRATES: A CASE STUDY ON THE SOFT OCTOCORALS DENDRONEPHTHYA KLUNZINGERI AND ANTHELIA GLAUCA

Primmorphs were obtained from single cells of both coral species Dendronephthya klunzingeri and Anthelia glauca following incubation for three or four days during 2001. Both ultraviolet light (UVB; peak of emission of 320 nm) and visible light (wavelength between 400 and 520 nm with a maximum at 480 nm) were applied to the primmorphs as stressors. The following incubation probes have been isolated from D. klunzingeri and were used to monitor the expression of the respective genes in the homologous cell system; the heat-shock protein HSP90 (to monitor general stress responses), the histone H4cDNA (to monitor the cell cycle/proliferation) and the UVS-related protein (response to ultraviolet light) from D. klunzingeri. The data show that the steady state level of HSP90 expression is only upregulated at low level of exposure to UVB (30 J/cm), while at stronger irradiation the expression level decreases. The expression of the histone H4 gene is blocked following exposure to UVS. Interestingly, in the absence of UVB no transcripts of UVS-related protein can be visualized. However, after exposure of the primmorphs with 30 to 300 J/cm a strong upregulation of the expression of the UVS-related gene is seen. The response of the primmorphs to visible light is distinct to that following exposure to UVB. The expression of the gene for histone H4 is significantly upregulated following exposure to UVB, while no expression of the gene UVS-related protein can be detected. It is concluded that, the primmorph system is useful to assess potential nature and anthropogenic disturbances on coral cells.
SAU-Net: Spatial Attention-based Densely Connected U-Net for 3D Spine MRI Segmentation

Accurate segmentation of spine Magnetic Resonance Imaging (MRI) is highly demanded in morphological research, quantitative analysis, and diseases identification, such as spinal canal stenosis, disc herniation and degeneration. However, accurate spine segmentation is challenging because of the irregular shape, artifacts and large variability between slices. To alleviate these problems, spatial information is used for more continuous and accurate segmentation such as by 3D convolutional neural networks (CNN). However, 3D CNN suffers from higher computational cost, memory cost and risk of over-fitting, especially for medical images where the number of labeled data is limited. To address these problems, we apply the attention mechanism for the utilization of inter-slice information in 3D segmentation tasks based on 2D convolutional networks and propose a spatial attention-based densely connected U-Net (SAU-Net), which consists of stacked 2D Dense U-Net for extraction of intra-slice features and an inter-slice attention module (ISA) to utilize inter-slice information from adjacent slices and refine the segmentation results. We evaluate our method on SpinesegT2W dataset. Experimental results demonstrate the effectiveness of ISA as well as higher accuracy and efficiency of segmentation results of our method compared to other deep learning methods.
Simpler TAG semantics through synchronization

In recent years Laura Kallmeyer, Maribel Romero, and their collaborators have led research on TAG semantics through a series of papers reﬁning a sys-tem of TAG semantics computation. Kallmeyer and Romero bring together the lessons of these attempts with a set of desirable properties that such a system should have. First, computation of the semantics of a sentence should rely only on the relationships expressed in the TAG derivation tree. Second, the generated semantics should compactly represent all valid interpretations of the input sentence, in particular with respect to quantiﬁer scope. Third, the formalism should not, if possible, increase the expressivity of the TAG formalism. We revive the proposal of using synchronous TAG (STAG) to simultaneously generate syntactic and semantic representations for an input sentence. Although STAG meets the three requirements above, no serious attempt had previously been made to determine whether it can model the semantic constructions that have proved diﬃcult for other approaches. In this paper we begin exploration of this question by proposing STAG analyses of many of the hard cases that have spurred the research in this area. We reframe the TAG semantics problem in the context of the STAG formalism and in the process present a simple, intuitive base for further exploration of TAG semantics. We provide analyses that demonstrate how STAG can handle quantiﬁer scope, long-distance WH-movement, interaction of raising verbs and adverbs, attitude verbs and quantiﬁers, relative clauses, and quantiﬁers within prepositional phrases.
Text-Adaptive Generative Adversarial Networks

In this study, we replicate the text-adaptive generative adversarial network proposed in [4]. We do this for two reasons: ﬁrst, image modiﬁcation with natural language is an interesting and challenging problem, and second, replicating studies can provide additional information about the vailidity and generalizability of the original authors’ claims. Our replication followed the speciﬁcations of the network given in the published study and supplementary materials (and not the publicly available code provided by the authors) and we trained our network using the CUB [9] dataset. We evaluated our network using L2 error and informal qualitative measures and found that our results were signiﬁcantly worse than the results from the original study, due to issues in accurately and comprehensively recreating the network
Partially-Typed NER Datasets Integration: Connecting Practice to Theory

While typical named entity recognition (NER) models require the training set to be annotated with all target types, each available datasets may only cover a part of them. Instead of relying on fully-typed NER datasets, many efforts have been made to leverage multiple partially-typed ones for training and allow the resulting model to cover a full type set. However, there is neither guarantee on the quality of integrated datasets, nor guidance on the design of training algorithms. Here, we conduct a systematic analysis and comparison between partially-typed NER datasets and fully-typed ones, in both theoretical and empirical manner. Firstly, we derive a bound to establish that models trained with partially-typed annotations can reach a similar performance with the ones trained with fully-typed annotations, which also provides guidance on the algorithm design. Moreover, we conduct controlled experiments, which shows partially-typed datasets leads to similar performance with the model trained with the same amount of fully-typed annotations
Measurement of cross-sections of yttrium (n,xn) threshold reactions by means of gamma spectroscopy

Neutron activation and gamma spectrometry are usable also for the determination of cross-sections of different neutron reactions. We have studied the cross-section of yttrium (n,xn) threshold reactions using quasi mono-energetic neutron source based on the reaction on 7Li target at Nuclear Physics Institute of ASCR in Rez. Yttrium (n,xn) threshold reactions are suitable candidates for fast neutron field measurement by activation detectors. Fast neutron field monitoring is necessary already today at a wide range of accelerator facilities and will gain on importance in future fast reactors of generation IV, accelerator transmutation systems or fusion reactors. The knowledge of the cross-sections is crucial for such purpose. Unfortunately, the cross-section is sufficiently known only for 89Y(n,2n)88Y reaction. For higher orders of reactions there are almost no experimental data. Special attention was paid to the 89Y(n,3n)87Y reaction. The nuclei are produced, both in the ground state with half-life 79.8 hours and in the isomeric state with half-life 13.38 hours. The isomer decays mainly through the gamma transition to the ground state, the beta decay of the excited state is negligible within our accuracy. The cross-sections of both 87Y productions were analyzed. c © 2013 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of Joint Research Centre Institute for Reference Materials and Measurements.
End-to-End Video Compressive Sensing Using Anderson-Accelerated Unrolled Networks

Compressive imaging systems with spatial-temporal encoding can be used to capture and reconstruct fast-moving objects. The imaging quality highly depends on the choice of encoding masks and reconstruction methods. In this paper, we present a new network architecture to jointly design the encoding masks and the reconstruction method for compressive high-frame-rate imaging. Unlike previous works, the proposed method takes full advantage of denoising prior to provide a promising frame reconstruction. The network is also flexible enough to optimize full-resolution masks and efficient at reconstructing frames. To this end, we develop a new dense network architecture that embeds Anderson acceleration, known from numerical optimization, directly into the neural network architecture. Our experiments show the optimized masks and the dense accelerated network respectively achieve 1.5 dB and 1 dB improvements in PSNR without adding training parameters. The proposed method outperforms other state-of-the-art methods both in simulations and on real hardware. In addition, we set up a coded two-bucket camera for compressive high-frame-rate imaging, which is robust to imaging noise and provides promising results when recovering nearly 1,000 frames per second.
Gaussian Processes as Multiagent Reward Models

In multiagent problems that require complex joint actions, reward shaping methods yield good behavior by incentivizing the agents’ potentially valuable actions. However, reward shaping often requires access to the functional form of the reward function and the global state of the system. In this work, we introduce the Exploratory Gaussian Reward (EGR), a new reward model that creates optimistic stepping stone rewards linking the agents potentially good actions to the desired joint action. EGR models the system reward as a Gaussian Process to leverage the inherent uncertainty in reward estimates that push agents to explore unobserved state space. In the tightly coupled rover coordination problem, we show that EGR significantly outperforms a neural network approximation baseline and is comparable to the system with access to the functional form of the global reward. Finally, we demonstrate how EGR improves performance over other reward shaping methods by forcing agents to explore and escape local optima.
Machine learning theory and practice as a source of insight into universal grammar

In this paper, we explore the possibility that machine learning approaches to naturallanguage processing (NLP) being developed in engineering-oriented computational linguistics (CL) may be able to provide specific scientific insights into the nature of human language. We argue that, in principle, machine learning (ML) results could inform basic debates about language, in one area at least, and that in practice, existing results may offer initial tentative support for this prospect. Further, results from computational learning theory can inform arguments carried on within linguistic theory as well.
Automatic Segmentation of Head and Neck Tumors and Nodal Metastases in PET-CT scans

Radiomics, the prediction of disease characteristics using quantitative image biomarkers from medical images, relies on expensive manual annotations of Regions of Interest (ROI) to focus the analysis. In this paper, we propose an automatic segmentation of Head and Neck (H&N) tumors and nodal metastases from FDG-PET and CT images. A fully-convolutional network (2D and 3D V-Net) is trained on PET-CT images using ground truth ROIs that were manually delineated by radiation oncologists for 202 patients. The results show the complementarity of the two modalities with a statistically signiﬁcant improvement from 48.7% and 58.2% Dice Score Coeﬃcients (DSC) with CT-and PET-only segmentation respectively, to 60.6% with a bimodal late fusion approach. We also note that, on this task, a 2D implementation slightly outperforms a similar 3D design (60.6% vs 59.7% for the best results respectively).
Question Generation with Adaptive Copying Neural Networks

Given the rapid development of communication technology, online business websites are becoming increasingly popular. People could buy all the goods online rather than going to physical stores. However, it is often time-consuming for customers to read long product reviews when they make purchase decisions. Generally speaking, customers have specific questions in their minds when they seek information in product specifications. Therefore, if we can identify the most representative questions that can be answered by extensive product specifications, it will be much easier for customers to obtain their desired information quickly grasp, which is the motivation behind our thesis. In this thesis, we aim to automatically generate questions from reviews. To tackle the automatic question generation (QG) task, we proposed a new adaptive copying recurrent neural network model, titled Adaptive Copying Neural Network (ACNN). The proposed model combines a component copying mechanism component onto a bidirectional LSTM structure to adaptively generate more suitable questions based on the input data. Subsequently, we calculated the generated questions’ summarization score to see if they could be answered by the reviews and to determine whether they were the most representative questions to summarize those reviews. In our evaluation experiments, we first evaluated our ACNN model. We confirmed that our method can exceed the most advanced QG method in BLEU and ROUGE evaluation scores; it also showed better performance in human evaluation. In addition, we combined the summarization scores with our model which resulted in a preliminary performance boost. Overall, our model is fully data-driven and can simultaneously handle two significant tasks in natural language processing, which represents a new direction in this field.