Immigrant Entrepreneurship in the UK: Opportunities and Resources to Start-up and Grow of businesses in the food sector

Master of Science in Business / Siviløkonom - Nord universitet 201

Generating Effective Sentence Representations: Deep Learning and Reinforcement Learning Approaches

Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Many Natural Language applications are powered by machine learning models performing a large variety of underlying tasks. Recently, deep learning approaches have obtained very high performance across many NLP tasks. In order to achieve this high level of performance, it is crucial for computers to have an appropriate representation of sentences. The tasks addressed in the thesis are best approached having shallow semantic representations. These representations are vectors that are then embedded in a semantic space. We present a variety of novel approaches in deep learning applied to NLP for generating effective sentence representations in this space. These semantic representations can either be general or task-specific. We focus on learning task-specific sentence representations, where often these tasks have a good amount of overlap. We design a set of general purpose and task specific sentence encoders combining both word-level semantic knowledge and word- and sentence-level syntactic information. As a method for the former, we perform an intelligent amalgamation of word vectors using modern deep learning modules. For the latter, we use word-level knowledge, such as parts of speech, spelling, and suffix features, and sentence-level information drawn from natural language parse trees which provide the hierarchical structure of a sentence together with grammatical relations between the words. Further expertise is added with reinforcement learning which guides a machine learning model through a reward-penalty game. Rather than just striving for good performance, we always try to design models that are more transparent and explainable. We provide an intuitive explanation about the design of each model and how the model is making a decision. Our extensive experiments show that these models achieve competitive performance compared with the currently available state-of-the-art generalized and task-specific sentence encoders. All but one of the tasks dealt with English language texts. The multilingual semantic similarity task required creating a multilingual corpus for which we provide a novel semi-supervised approach to make artificial negative samples in the presence of just positive samples

Convolutional neural network training with artificial pattern for Bangla handwritten numeral recognition

Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. The progress of handwritten Bangla numeral is well behind Roman, Chinese and Arabic scripts although it is a major language in Indian subcontinent and is the first language of Bangladesh. Handwritten numeral classification is a high dimensional complex task and existing methods use distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. In this study, a CNN based method has been investigated for Bangla handwritten numeral recognition. A moderated pre-processing has been adopted to produce patterns from handwritten scan images. On the other hand, CNN has been trained with the patterns plus a number of artificial patterns. A simple rotation based approach is employed to generate artificial patterns. The proposed CNN with artificial pattern is shown to outperform other existing methods while tested on a popular Bangla benchmark handwritten dataset

Identification of N epsilon-Carboxymethyllysine as a Degradation Product of Fructoselysine in Glycated Protein

The chemistry of Maillard or browning reactionosf glycated proteins was studied using the model compound, Nu-formyl-W-fructoselysine(f FL), an analog of glycated lysine residues in protein. Incubation of fFL (15 mM) at physiological pH and temperature in 0.2 M phosphate buffer resulted in formation of lVcarboxymethyllysine (CML) in about 40% yield after 15 days. CML was formed by oxidative cleavage of fFL between C-2 and C-3 of the carbohydrate chain and erythronic acid (EA) was identified a s , the split product formed in the reaction. Neither CML nor EA was formed from fFL under a nitrogen atmosphere. The rate of formation of CML was dependent on phosphate concentration in the incubation mixture and the reaction was shown to occur by a free radical mechanism. CML was also identified by amino acid analysis in hydrolysates of both poly-L-lysine and bovine pancreatic ribonuclease glycated in phosphate buffer under air. CML was also detected in human lens proteins and tissue collagens by HPLC and the identification was confirmed by gas chromatography/mass spectroscopy. The presence of both CML and EA in human urine suggests that they are formed by degradation of glycated proteins in vivo. The browning of fFL incubation mixtures proceeded to a greater extent under a nitrogen versus an air atmosphere, suggesting that oxidative degradation of Amadori adducts to form CML may limit the browning reactions of glycated proteins. Since the reaction products, CML and EA, are relatively inert, both chemically and metabolically, oxidative cleavage of Amadori adducts may have a role in limiting the consequences of protein glycation in the body

Multiple convolutional neural network training for Bangla handwritten numeral recognition

Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. The progress of handwritten Bangla numeral is well behind Roman, Chinese and Arabic scripts although it is a major language in Indian subcontinent and is the first language of Bangladesh. Handwritten numeral classification is a high-dimensional complex task and existing methods use distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. In this study, three different CNNs with same architecture are trained with different training sets and combined their decisions for Bangla handwritten numeral recognition. One CNN is trained with ordinary training set prepared from handwritten scan images; and training sets for other two CNNs are prepared with fixed (positive and negative, respectively) rotational angles of original images. The proposed multiple CNN based approach is shown to outperform other existing methods while tested on a popular Bangla benchmark handwritten dataset

On Coverage of Critical Nodes in UAV-Assisted Emergency Networks

Unmanned aerial vehicle (UAV)-assisted networks ensure agile and flexible solutions based on the inherent attributes of mobility and altitude adaptation. These features render them suitable for emergency search and rescue operations. Emergency networks (ENs) differ from conventional networks. They often encounter nodes with vital information, i.e., critical nodes (CNs). The efficacy of search and rescue operations highly depends on the eminent coverage of critical nodes to retrieve crucial data. In a UAV-assisted EN, the information delivery from these critical nodes can be ensured through quality-of-service (QoS) guarantees, such as capacity and age of information (AoI). In this work, optimized UAV placement for critical nodes in emergency networks is studied. Two different optimization problems, namely capacity maximization and age of information minimization, are formulated based on the nature of node criticality. Capacity maximization provides general QoS enhancement for critical nodes, whereas AoI is focused on nodes carrying critical information. Simulations carried out in this paper aim to find the optimal placement for each problem based on a two-step approach. At first, the disaster region is partitioned based on CNs’ aggregation. Reinforcement learning (RL) is then applied to observe optimal placement. Finally, network coverage over optimal UAV(s) placement is studied for two scenarios, i.e., network-centric and user-centric. In addition to providing coverage to critical nodes, the proposed scheme also ensures maximum coverage for all on-scene available devices (OSAs)

In Silico and In Vivo : Evaluating the Therapeutic Potential of Kaempferol, Quercetin, and Catechin to Treat Chronic Epilepsy in a Rat Model

Recently, alternative therapies are gaining popularity in the treatment of epilepsy. The present study aimed to find out the antiepileptic potential of quercetin, catechin, and kaempferol. In vivo and in silico experiments were conducted to investigate their therapeutic potential. 25 mg/kg/day of pentylenetetrazole was administered for 4 weeks after epilepsy was induced in the rats; this was followed by the behavioral studies and histological analysis of rat brain slices. Binding affinities of kaempferol, quercetin, and catechin were assessed by performing in silico studies. Kaempferol, quercetin, and catechin were found to have the highest binding affinity with the synaptic vesicle 2A (SV2A) protein, comparable to standard levetiracetam (LEV). The mRNA levels of SV2A, as well as the expression of TNF, IL 6, IL 1 beta, NFkB, IL 1Ra, IL 4, and IL 10, were investigated using qPCR. Our results indicate for the first time that SV2A is also a transporter of understudied phytoflavonoids, due to which a significant improvement was observed in epileptic parameters. The mRNA levels of SV2A were found to be significantly elevated in the PF-treated rats when compared with those of the control rats with epilepsy. Additionally, downregulation of the pro-inflammatory cytokines and upregulation of the anti-inflammatory cytokines were also noted in the PF-treated groups. It is concluded that kaempferol, quercetin, and catechin can effectively decrease the epileptic seizures in our chronic epilepsy rat model to a level that is comparable to the antiepileptic effects induced by levetiracetam drug.Peer reviewe