Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-gene risk analysis of susceptibility genes on primary gastric cancer

Objective To screen and assess genetic risk factors(gene polymorphism) of primary gastric cancer susceptibility and to select the genotype relating to gastric cancer in Han population of Nanjing city.Methods A 1:1 matched case-control study was carried out in 374 primary stomach cancer cases(273 males and 101 females) and gender,age matched controls in Nanjing city.PCR-RFLP and AS-PCR technologies were used to determine the genotypes of CYP2E1,GSTM1,GSTT1,NAT2,ALDH2,MTHFR,XRCC1,IL-1β,VDR,and TNF.Conditional-logistic regression model was adopted to screen the environmental and genetic risk factors of primary gastric cancer and to select the genotype relating to gastric cancer for multi-gene risk analysis.Results The genetic risk factors associated to primary gastric cancer susceptibility were CYP2E1 wild genotype(odds ratio[OR]=1.348,95% confidence interval[CI]=1.075-1.690),NAT2M1 mutant genotype(OR=2.310,95%CI=1.613-3.309),NAT2 slow-acetylating genotype(OR=1.768,95%CI =1.018-3.072),XRCC1 194 mutant genotype(OR=1.449,95%CI=1.140-1.842),MTHFR A1298C mutant genotype(OR=1.521,95%CI=1.11-2.083),IL-1β mutant genotype(OR=1.271,95%CI=1.031-1.568). Multi-gene risk analysis showed that there was a close relation between the OR of polygenic combination and genotypes frequency.Conclusion With the increase of the susceptibility gene,the risk of primary gastric cancer increases.Multi-gene risk assessment can further promote the recognition of susceptible population.
Effect of bio-stimulants and growth regulators on plant growth and herbage yield of fenugreek (Trigonella foenum- graecum L)

WA study was conducted to find out the effect of different bio-stimulants and growth regulators on plant growth, herbage yield and quality of fenugreek var CO 2. Foliar spray of bio-stimulants and growth regulators like TNAU Panchagavya (3%), Moringa Leaf Extract (3%), PPFM (1%), Vermiwash (3%), Humic Acid (1000 ppm) and Cytozyme (1000 ppm) were used as a treatments. Among the different treatments, the mean value of number of leaves (62.24), number of primary branches (6.87), leaf area (255.68cm2) and herbage yield per plot (5.49 kg plot-1) was recorded highest in the treatment T2 (vermiwash @ 3%). While in plant height (30.01cm) PPFM recorded best compared to all other treatments. B:C ratio was also high in treatment T2 (3.85) compared to control (1.88).
#okboomer : A multi-modal approach towards generating hashtags on social media posts

Filtering of data into appropriate categories is among the oldest problems in computer science. Since the advent of the internet and the subsequent surfeit of data collected by websites, this problem has become a ubiquitous and exponentially challenging one. Among the popular manifestations of this today is on social media platforms that host several thousands of gigabytes of data and turn to hashtags as a means of achieving this categorization. We propose a novel (to the best of our knowledge) multi-modal deep learning method for the generation of hashtags – conditioned on an input image and a corresponding caption to the image. Viewed another way, this is an extension of the auto-correct feature on most text applications today, with the addition being that the predicted values are dependent not just on textual inputs but also on images. As part of this project, we have also collected a new dataset comprising Instagram posts with their images, captions and hashtags. In our evaluations, our best results have a BLEU score of 0.69 on the validation split while trained over a subsample of 7160 images across 20 epochs which we believe could get better when trained without computational and time constraints.
MODEL OF ASSIMILATION/INTEGRATION OF IMMIGRANTS

The article aims to familiarize the scientific community with one of the results of a qualitative case study investigation that employed ethnographic methods of gathering data on Russian-speaking immigrants in the Brisbane region, Queensland, Australia, conducted in 2016-2017. The study, titled "Perceptions of Russian-speaking immigrants: A case study in Australia," was held in response to one of the most pressing and challenging questions on the agenda of many public administration offices around the world –"What can be done for better integration/assimilation of immigrants into host communities? " It set itself to identify factors affecting the processes of assimilation and integration of Russian-speaking immigrants into Australian society. The research not only pointed out alleged obstacles such as (1) employment, (2) education, (3) healthcare, (4) government assistance, and (5) discrimination but also suggested the theoretical Model of Assimilation/Integration of Immigrants (MAI). The proposed model could explain not only why immigrants cannot integrate easily into the host society but also some other futures of their behavior. The model indicates the possibility for quantification of assimilation/integration itself and thus, provides an opportunity for further comparison of degrees of assimilation in between population groups.
Machine Learning for Network Intrusion Detection Based on SVM Binary Classification Model

A R T I C L E I N F O A B S T R A C T Article history: Received: . 23 June, 2020 Accepted: 20 July, 2020 Online: 25 August, 2020 Recently, the number of connected machines around the worldwide has become very large, generating a huge amount of data either to be stored or to be communicated. Data protection is a concern for all institutions, it is difficult to manage the masses of data that are susceptible to multiple threats. In this work, we present a novel method of Intrusion Detection System (IDS) based on the detection of anomalies in computer networks. The aim is to use artificial intelligence techniques in the form of Machine Learning (ML) for intrusion detection. For this purpose, we have proposed a Support Vector Machine (SVM) classification model with two kernels, one Polynomial and the other Gaussian. This model is trained and tested with the recent UNSWNB-15 dataset. Regarding the results obtained, we have evaluated our model with six metrics capable of offering all potential threats. As a result, we have achieved a percentage of 94% for the detection rate (DR).
The impact of interest rate on savings behavior in Bangladesh

The purpose of this study is to determine the impact of interest rate on savings and deposits behavior in Bangladesh for the period of 2009 to 2016. The data is mainly collected from secondary sources such as World Bank and annual reports of Bangladesh bank. The explanatory variable is the interest rate; savings and deposits are considered as dependent variable in two different models. We use simple ordinary least square regression method to examine the relation between interest rate and savings behavior. The study finds that interest Rate positively affects customer’s savings behavior in the banking sector of Bangladesh.
FLOOD PREDICTION AND ASSESSMENT PLATFORM A MULTI-MODEL APPROACH

— Floods are the most destructive natural disasters, causing huge damage to life and socioeconomic system. Early flood warning systems are good countermeasures against flood hazards. So here we propose a flood prediction system that uses machine learning models to deliver chances of flood based on location and time queries. The system can be integrated with multi-model deep learning methods such as Support Vector machines, Neural Networks and Extreme Learning Model to predict the chances of flood based on various environmental, climatic factors and geographical conditions. Based on the results, actions that need to be taken can also be predicted. The model is trained on a comprehensive data set expanding over years. The system has the ability to compare the results of various sub models such that the overall accuracy can be modified. It is helpful for the people living in the flood affected areas as well as to the ones who are related to the organizations in this field.
INTEGRABLE GEODESIC FLOWS WITH WILD FIRST INTEGRALS: THE CASE OF 2-STEP NILMANIFOLDS TO APPEAR IN ERGODIC THEORY AND DYNAMICAL SYSTEMS COPYRIGHT: CAMBRIDGE UNIVERSITY PRESS

This paper has four main results: (i) it shows that left-invariant geodesic flows on a broad class of 2-step nilmanifolds – which are dubbed almost non-singular [22, 39] – are integrable in the non-commutative sense of Nehoros̆ev; (ii) the left-invariant geodesic flows on all Heisenberg-Reiter nilmanifolds are Liouville integrable [40]; (iii) the topological entropy of a left-invariant geodesic flow on a 2-step nilmanifold vanishes; (iv) there exist 2-step nilmanifolds with non-integrable left-invariant geodesic flows. It is also shown that for each of the integrable hamiltonians investigated here, there is a C-open neighbourhood in C(T ∗M) such that every integrable hamiltonian vector field in this neighbourhood must have wild first integrals.
Structural and dynamical fingerprints of the anomalous dielectric properties of water under confinement

There is a long-standing question about the molecular configuration of interfacial water molecules in the proximity of solid surfaces, particularly carbon atoms which play a crucial role in electrochemistry and biology. In this study, the dielectric, structural and dynamical properties of confined water placed between two parallel graphene walls at different inter distances from the Angstrom scale to few tens of nanometer have been investigated using molecular dynamics. For dielectric properties of water, we show that the perpendicular component of water dielectric constant drastically decreases under sub $2\; nm$ spatial confinement. The achieved dielectric constant data through linear response and fluctuation-dissipation theory, are consistent with recent reported experimental results. By determining the charge density as well as fluctuations in the number of atoms, we provide a molecular rationale for the behavior of perpendicular dielectric response function. We also interpret the behavior of the dielectric response in terms of the presence of dangling O-H bonds of waters. By examining the residence time and lateral diffusion constant of water under confinement, we reveal that the water molecules tend to keep their hydrogen bond networks at the interface of water-graphene. We also found consistency between lateral diffusion and z-component of variance in the center of mass of the system as a function of confinement.
Group-wise Contrastive Learning for Neural Dialogue Generation

Neural dialogue response generation has gained much popularity in recent years. Maximum Likelihood Estimation (MLE) objective is widely adopted in existing dialogue model learning. However, models trained with MLE objective function are plagued by the low-diversity issue when it comes to the open-domain conversational setting. Inspired by the observation that humans not only learn from the positive signals but also benefit from correcting behaviors of undesirable actions, in this work, we introduce contrastive learning into dialogue generation, where the model explicitly perceives the difference between the well-chosen positive and negative utterances. Specifically, we employ a pretrained baseline model as a reference. During contrastive learning, the target dialogue model is trained to give higher conditional probabilities for the positive samples, and lower conditional probabilities for those negative samples, compared to the reference model. To manage the multi-mapping relations prevalent in human conversation, we augment contrastive dialogue learning with group-wise dual sampling. Extensive experimental results show that the proposed group-wise contrastive learning framework is suited for training a wide range of neural dialogue generation models with very favorable performance over the baseline training approaches.
Multi-span Style Extraction for Generative Reading Comprehension

Generative machine reading comprehension (MRC) requires a model to generate well-formed answers. For this type of MRC, answer generation method is crucial to the model performance. However, generative models, which are supposed to be the right model for the task, in generally perform poorly. At the same time, single-span extraction models have been proven effective for extractive MRC, where the answer is constrained to a single span in the passage. Nevertheless, they generally suffer from generating incomplete answers or introducing redundant words when applied to the generative MRC. Thus, we extend the single-span extraction method to multi-span, proposing a new framework which enables generative MRC to be smoothly solved as multi-span extraction. Thorough experiments demonstrate that this novel approach can alleviate the dilemma between generative models and single-span models and produce answers with better-formed syntax and semantics. We will open-source our code for the research community.
Path Planning using Neural A* Search

We present Neural A*, a novel data-driven search algorithm for path planning problems. Although data-driven planning has received much attention in recent years, little work has focused on how search-based methods can learn from demonstrations to plan better. In this work, we reformulate a canonical A* search algorithm to be differentiable and couple it with a convolutional encoder to form an end-to-end trainable neural network planner. Neural A* solves a path planning problem by (1) encoding a visual representation of the problem to estimate a movement cost map and (2) performing the A* search on the cost map to output a solution path. By minimizing the difference between the search results and ground-truth paths in demonstrations, the encoder learns to capture a variety of visual planning cues in input images, such as shapes of dead-end obstacles, bypasses, and shortcuts, which makes estimated cost maps informative. Our extensive experiments confirmed that Neural A* (a) outperformed state-of-the-art data-driven planners in terms of the search optimality and efficiency trade-off and (b) predicted realistic pedestrian paths by directly performing a search on raw image inputs.
Pooling Methods in Deep Neural Networks, a Review

Nowadays, Deep Neural Networks are among the main tools used in various sciences. Convolutional Neural Network is a special type of DNN consisting of several convolution layers, each followed by an activation function and a pooling layer. The pooling layer is an important layer that executes the down-sampling on the feature maps coming from the previous layer and produces new feature maps with a condensed resolution. This layer drastically reduces the spatial dimension of input. It serves two main purposes. The first is to reduce the number of parameters or weights, thus lessening the computational cost. The second is to control the overfitting of the network. An ideal pooling method is expected to extract only useful information and discard irrelevant details. There are a lot of methods for the implementation of pooling operation in Deep Neural Networks. In this paper, we reviewed some of the famous and useful pooling methods.
Parallel Interactive Networks for Multi-Domain Dialogue State Generation

The dependencies between system and user utterances in the same turn and across different turns are not fully considered in existing multidomain dialogue state tracking (MDST) models. In this study, we argue that the incorporation of these dependencies is crucial for the design of MDST and propose Parallel Interactive Networks (PIN) to model these dependencies. Specifically, we integrate an interactive encoder to jointly model the in-turn dependencies and cross-turn dependencies. The slot-level context is introduced to extract more expressive features for different slots. And a distributed copy mechanism is utilized to selectively copy words from historical system utterances or historical user utterances. Empirical studies demonstrated the superiority of the proposed PIN model.
Bayesian inference for stationary points in Gaussian process regression models for event‐related potentials analysis

Stationary points embedded in the derivatives are often critical for a model to be interpretable and may be considered as key features of interest in many applications. We propose a semiparametric Bayesian model to efficiently infer the locations of stationary points of a nonparametric function, which also produces an estimate of the function. We use Gaussian processes as a flexible prior for the underlying function and impose derivative constraints to control the function's shape via conditioning. We develop an inferential strategy that intentionally restricts estimation to the case of at least one stationary point, bypassing possible mis‐specifications in the number of stationary points and avoiding the varying dimension problem that often brings in computational complexity. We illustrate the proposed methods using simulations and then apply the method to the estimation of event‐related potentials derived from electroencephalography (EEG) signals. We show how the proposed method automatically identifies characteristic components and their latencies at the individual level, which avoids the excessive averaging across subjects that is routinely done in the field to obtain smooth curves. By applying this approach to EEG data collected from younger and older adults during a speech perception task, we are able to demonstrate how the time course of speech perception processes changes with age.
SLGAN: Style- and Latent-Guided Generative Adversarial Network for Desirable Makeup Transfer and Removal

There are five features to consider when using generative adversarial networks to apply makeup to photos of the human face. These features include (1) facial components, (2) interactive color adjustments, (3) makeup variations, (4) robustness to poses and expressions, and the (5) use of multiple reference images. To tackle the key features, we propose a novel style- and latent-guided makeup generative adversarial network for makeup transfer and removal. We provide a novel, perceptual makeup loss and a style-invariant decoder that can transfer makeup styles based on histogram matching to avoid the identity-shift problem. In our experiments, we show that our SLGAN is better than or comparable to state-of-the-art methods. Furthermore, we show that our proposal can interpolate facial makeup images to determine the unique features, compare existing methods, and help users find desirable makeup configurations.
GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis

Genome sequence analysis has enabled significant advancements in medical and scientific areas such as personalized medicine, outbreak tracing, and the understanding of evolution. To perform genome sequencing, devices extract small random fragments of an organism’s DNA sequence (known as reads). The first step of genome sequence analysis is a computational process known as read mapping. In read mapping, each fragment is matched to its potential location in the reference genome with the goal of identifying the original location of each read in the genome. Unfortunately, rapid genome sequencing is currently bottlenecked by the computational power and memory bandwidth limitations of existing systems, as many of the steps in genome sequence analysis must process a large amount of data. A major contributor to this bottleneck is approximate string matching (ASM), which is used at multiple points during the mapping process. ASM enables read mapping to account for sequencing errors and genetic variations in the reads.We propose GenASM, the first ASM acceleration framework for genome sequence analysis. GenASM performs bitvectorbased ASM, which can efficiently accelerate multiple steps of genome sequence analysis. We modify the underlying ASM algorithm (Bitap) to significantly increase its parallelism and reduce its memory footprint. Using this modified algorithm, we design the first hardware accelerator for Bitap. Our hardware accelerator consists of specialized systolic-array-based compute units and on-chip SRAMs that are designed to match the rate of computation with memory capacity and bandwidth, resulting in an efficient design whose performance scales linearly as we increase the number of compute units working in parallel.We demonstrate that GenASM provides significant performance and power benefits for three different use cases in genome sequence analysis. First, GenASM accelerates read alignment for both long reads and short reads. For long reads, GenASM outperforms state-of-the-art software and hardware accelerators by 116× and 3.9×, respectively, while reducing power consumption by 37× and 2.7×. For short reads, GenASM outperforms state-of-the-art software and hardware accelerators by 111× and 1.9×. Second, GenASM accelerates pre-alignment filtering for short reads, with 3.7× the performance of a state-of-the-art pre-alignment filter, while reducing power consumption by 1.7× and significantly improving the filtering accuracy. Third, GenASM accelerates edit distance calculation, with 22–12501× and 9.3–400× speedups over the state-of-the-art software library and FPGA-based accelerator, respectively, while reducing power consumption by 548–582× and 67×. We conclude that GenASM is a flexible, high-performance, and low-power framework, and we briefly discuss four other use cases that can benefit from GenASM.
FLAVAN-3-OL IN VITRO CULTURE CAMELLIA SINENSIS CONTRIBUTIONS IN ORDER SUPPORT FUNCTIONAL FOOD

Abstract Flavan-3-ol act as bioactive compounds from in vitro culture of Camellia sinensis plant can support the functional food. These compounds are bioactive because of it has a chemical structure that is rich in hydroxide groups so that it can be as functional food. However, flavan-3-ol which is extracted from the fields stunted harvest wait time of up to 3-5 years. To overcome obstacles waiting time of harvesting of the land should be developed mode of production flavan-3-ol via in vitro culture. The purpose of this paper is to obtain the production of f lavan-3-ol becoming more efficient. The process of production to achieve these objectives are: (1) initiation of callus, (2) sub callus, (3) the initiation of suspension culture, (4) extraction suspense culture, (3) Analysis of qualitative / quantitative compound of flavan-3-ol. Results of this study in the form of biomass culture can play a role in supporting both as a functional food additives, flavor and mix of food raw materials . Keywords: f lavan-3-ol , Came l lia sinensis , in vitro culture , functional food Abstrak Flavan-3-ol bertindak sebagai senyawa bioaktif dari kultur in vitro dari tanaman Camellia sinensis dapat berperan sebagai makanan fungsional. Senyawa bioaktif ini memiliki struktur kimia yang kaya kelompok hidroksida sehingga dapat menjadi makanan fungsional. Namun, flavan-3-ol yang dapat diekstrak terhambat panen menunggu hingga 3-5 tahun. Untuk mengatasi kendala waktu panen maka dilakukan produksi flavan-3-ol yang dikembangkan melalui kultur in vitro. Tujuan dari makalah ini adalah untuk mendapatkan produksi flavan-3-ol menjadi lebih efisien. Proses produksi untuk mencapai tujuan tersebut adalah: (1) inisiasi kalus, (2) sub kalus, (3) inisiasi kultur suspensi, (4) budaya ketegangan ekstraksi, (3) Analisis kualitatif / kuantitatif senyawa flavan -3-ol. Hasil penelitian ini dalam bentuk biomassa dapat berperan sebagai aditif makanan fungsional, flavor dan campuran bahan baku makanan. Kata kunci: flavan-3-ol, Camellia sinensis, kultur in vitro, makanan fungsional