Miracle-dz/UniFAR-FTUs huggingface dataset:
3D printing PhycoTrixTM for wound healing

iii With the advent of additive manufacturing and its recent use in regenerative medicine, bioprinting has become a promising technology for tissue engineering applications. PhycoTrixTM, a sulphated marine-derived polysaccharide, taken from the cell wall of a DNA barcoded green algal spp. , (Chlorophyta), has a chemical structure similar to mammalian glycosaminoglycans found within the dermal skin layer extracellular matrix. This sustainable, under-utilised source of biomaterial was developed into a bioink for use in bioprinting. Specifically, a dual-network hydrogel was engineered through ionic and chemical means. This hydrogel was characterised following methacrylation through 1H NMR, FT-IR, and circular dichroism. The physical properties, printability, and crosslinking kinetics were all assessed through rheology and mechanical properties through micro-indentation. Preliminary cytocompatibility studies were evaluated using fibroblasts and adipose-derived stem cells. The results indicated relatively high cell binding affinity and proliferation compared to other alginate studies, suggesting this novel biomaterial could be useful for wound healing applications, such as wound dressings and matrices for tissue repair and regeneration. Figure 1 | Graphical abstract showing the bioink development from algal origin.
WEIBULL-STRENGTH SIZE EFFECT AND COMMON PROBLEMS WITH SIZE EFFECT MODELS

Size effect on quasi-brittle fracture of concrete-like materials is commonly investigated by testing geometrically-similar specimens with artificial notches. It can also be studied by testing plain specimens without any artificial notches to show the statistical influence of pre-existing defects/cracks using Weibull strength distribution. Those un-notched specimens can be further studied non-statistically, using the Fictitious Crack Model (FCM), which emphasizes the influence of crack-bridging within the fracture process zone (FPZ) on quasi-brittle fracture of concrete by adopting a single bridging-stress and crack-opening relation. This study discusses the merits and limitations of common size effect models, including Weibull strength and FCM, in dealing with quasi-brittle fracture of concrete specimens with and without notches, and shows it is necessary to adopt a tri-linear local fracture energy distribution, which considers the boundary influence from both the front and back specimen boundaries. The front boundary influence is particularly important to quasi-brittle fracture of un-notched specimens, as the limited stable crack growth before unstable fracture is well within the front boundary zone. Therefore, size effect models, based on constant fracture energy or constant local fracture energy distribution (suitable only to a long crack away from boundaries), cannot be used to reliably predict quasi-brittle fracture of un-notched concrete specimens.
First Insight into the Processing of the Historical Documents from the Period of Totalitarian Regimes

In this paper, we describe the goals and the initi al s ages of the project ”System for permanent preservation of document ation and presentation of historical sources from the period of totalitarian regimes”. The main goal of this project is to create an integrated archive of the r eco dings, documents, and photographs that would be accessible online and would provide multifaceted search capabilities (spoken content, biographical informat ion, relevant time period, etc.). The recordings contain retrospect interviews with witnesses of the totalitarian regimes in Czechoslovakia; the other document s are copies of relevant text material and photographs mainly from home arch ives. This paper focuses on the processing of the historical documents with the optical character recognition and describes the initial experiments.
Shaping Behaviour with Adaptive Morphology

Robots which exploit their embodiment promise to be more robust, energy-efficient and adaptable. However, the majority of systems designed in this way are only capable of exploiting their embodiment when performing a single task in a single environment. For such robots to be capable of adapting to a range of tasks or environments, they must be capable of adjusting their morphology on-line and have an understanding of how adjustments in both control and morphology affect their behaviour. We introduce the concept of the control-morphology (CM) space and the Variable Stiffness Swimmer (VSS), a multisegment pendular robot with adaptive joint stiffness. This system allows us to perform an initial investigation into how navigating the CM space affects the behaviour of a robot. We show that the behaviour of the system is not only the result of selecting a particular location within the control-morphology space, but also the route taken to arrive at that point. We also demonstrate locations within the space where a shift in behaviour can be caused entirely by smooth on-line changes in morphology.
QUANTIFYING EXPOSURE BIAS

The exposure bias problem refers to the training-testing discrepancy caused by teacher forcing in maximum likelihood estimation (MLE) training, for autoregressive neural network language models (LM). It has been regarded as a central problem for natural language generation (NLG) model training. Although a lot of algorithms have been proposed to avoid teacher forcing and therefore to alleviate exposure bias, there is little work showing how serious the exposure bias problem actually is. In this work, we first identify and analyze the self-recovery ability of MLE-trained LM, which casts doubt on the seriousness of exposure bias. We then develop a precise, quantifiable definition for exposure bias. Based on that definition, we design experiments to measure the seriousness of exposure bias. Surprisingly, we find that removing the training-testing discrepancy only brings very little performance gain, in both real and synthetic settings. With these results, we conclude that on the contrary to popular belief, the exposure bias problem is only a minor problem for MLE-based LM training.
CHANGES IN ADOLESCENT PSYCHOSOCIAL DIFFICULTIES 1 RUNNING HEAD: CHANGES IN ADOLESCENT PSYCHOSOCIAL DIFFICULTIES Changes in Severity of Psychosocial Difficulties in Adolescents Accessing Specialist Mental Healthcare in England (2009–2014) CHANGES IN ADOLESCENT PSYCHOSOCIAL DIFFICULTIES

In England, clinicians and professional organisations report that higher numbers of adolescents with more severe psychosocial difficulties are accessing specialist services. A lack of national data on patterns of access to specialist services means there is limited information to inform policy. We examined whether severity of psychosocial difficulties in adolescents accessing mental healthcare has changed over time. Adolescents seen in specialist child mental healthcare in 2009 vs. 2014 were matched on demographics and problem types using propensity score matching; final sample N=2,776 adolescents. We found: 1) stability over time in overall severity of difficulties, 2) an increase in severity of young women’s emotional problems, and 3) a decrease in adolescents’ conduct problems. The findings suggest the intriguing possibility that the criteria for accessing mental healthcare are not universally rising, but rather the patterns in access to specialist services may mirror epidemiological changes in severity of psychosocial difficulties in the population.
Entropy Minimization In Emergent Languages

There is growing interest in studying the languages that emerge when neural agents are jointly trained to solve tasks requiring communication through a discrete channel. We investigate here the information-theoretic complexity of such languages, focusing on the basic two-agent, one-exchange setup. We find that, under common training procedures, the emergent languages are subject to an entropy minimization pressure that has also been detected in human language, whereby the mutual information between the communicating agent's inputs and the messages is minimized, within the range afforded by the need for successful communication. That is, emergent languages are (nearly) as simple as the task they are developed for allow them to be. This pressure is amplified as we increase communication channel discreteness. Further, we observe that stronger discrete-channel-driven entropy minimization leads to representations with increased robustness to overfitting and adversarial attacks. We conclude by discussing the implications of our findings for the study of natural and artificial communication systems.
Individual based model of plant communities

Simulation modelling is an important aspect of research in ecological and environmental subjects. Such model can guide the research process, integrate the knowledge coming from a variety of studies, permit the testing of hypothesis, and enable predictions to be made. A model of Mediterranean plants growing as stand alone on communities is proposed. In this paper a vegetation model (Individual Based Plant Communities IBCM), developed in the Simile modelling environment, is proposed which is able to simulate the growth of several plant species in mixed communities under different environmental conditions is presented
Discriminative, Competitive, and Collaborative Representation-Based Classification with l2-Norm Regularizations

Recently, collaborative representation-based classification (CRC) and its many variations have been widely applied for various classification tasks in pattern recognition. To further enhance the pattern discrimination of CRC, in this article we propose a novel extension of CRC, entitled discriminative, competitive, and collaborative representation-based classification (DCCRC). In the proposed DCCRC, the class discrimination information is fully utilized for promoting the true class of each testing sample to dominantly represent the testing sample during collaborative representation. The class discrimination information is well considered in the newly designed discriminative l2-norm regularization that can decrease the ability of representation from the interclasses of each testing sample. Simultaneously, a competitive l2-norm regularization is introduced to the DCCRC model with the class discrimination information with the aim of enhancing the competitive ability of representation from the true class of each testing sample. The effectiveness of the proposed DCCRC is explored by extensive experiments on the several public face databases and some real numerical UCI data sets. The experimental results demonstrate that the proposed DCCRC achieves the superior performance over the state-of-the-art representation-based classification methods.
Comparison of clinical outcome between pyeloperfused versus non-pyeloperfused microwave ablation of renal cell carcinoma

Purpose We present the outcomes of microwave ablation (MWA) of renal cell carcinoma (RCC) with and without pyeloperfusion. Material and methods A retrospective review of patients’ records was undertaken to identify patients with RCC, who were treated with MWA with and without adjunctive pyeloperfusion. The distance between the tumour and ureter as well as the tumour size were measured on axial imaging. Pyeloperfusion was performed in nine patients in this series after placement of a ureteral stent and instilment of diluted contrast into the ureter. MWAs of the tumours were performed under computed tomography (CT) guidance. Hydrodissection was performed to displace at-risk organs. Creatinine was measured as renal function index after and before the procedure. A CT scan was performed at the end of the procedure and also after one, three, and six months, to identify the presence of residual disease and complications. Results Eighteen biopsies of proven RCC were treated with 20 sessions of MWA. The average follow-up time for this study was 180 days. The average distance between the ureter and the tumour in axial CT view was 20.8 (± 2.9) mm. Primary efficacy was achieved in 88% of pyeloperfused patients and in 100% of the non-pyeloperfused patients. Two pyeloperfused patients required secondary procedure, and full secondary efficacy was achieved for both. There was only one grade 2 urological complication, which occurred in a patient who underwent pyeloperfusion. Creatinine was not significantly different after the procedure in this study (p-value 0.4). Conclusion In this study MWAs of RCCs were successfully performed using pyeloperfusion as a protective measure against thermal injury to the ureter.
Model Based Inversion of Acoustic Impedance from Seismic Trace for Lithofacies Differentiation: an Application in Xy Field Offshore Niger Delta

This study presents the result of a Model-based seismic inversion technique which was used to invert an acoustic impedance structure within a reservoir interval by intergrating well logs and 3D post stack seismic data obtained from XY field offshore Niger Delta. The purpose was to delineate lateral and vertical alternations in subsurface rock properties which is caused by difference in lithofacies within the reservoir interval. This would help to define hydrocarbon fairways better and constrain the range of hydrocarbon zones for field development. The inversion workflow used in this study includes forward modelling of reflection coefficients from a low frequency impedance model driven from well logs and convolution of the reflection coeffiecients with a source wavelet derived from the seismic data. Acoustic impedance cross section obtained from the inversioin algorithim showed impedance values increasing from 4112 to 7539 (m/sec*g/cm 3 ) from top to bottom of the reservoir with gas filled sand facies observed at the top of the reservoir within time window 1900-2100msec. Below time window 2100msec, there is variation in impedance values observed within the anticlinal structures seen at this interval which suggests porous sand facies containing little shale intercalations. This is characteristic of sandstone reservoirs within the Agbada formation in the Niger Delta. These sands were most likely deposited through distributaries channel deposits, distributaries mouth bars, barrier bars, alluvial fans and crevasse which characterize the reservoir rocks (sandstones) in the Niger Delta. At time window 2100-2200msec, anticlinal structures containing porous sand facies with little shale intercalations was observed again. At time window 2200msec, water bearing sand facies (clean sandstone) was observed and at the bottom of the reservoir within time window 2300- 2500msec, the impedance was dominantly high which suggests the presence of shale facies at the bottom of the reservoir. Gas-oil contact (GOC) was observed between time window 2100-2200msec of the acoustic impedance section. These variations in acoustic impedance amplitude is due to lateral changes in lithofacies within the reservoir. The results obtained gave enhanced structural disposition of the reservoir and are important for accurate stratigraphic imaging interpretation to lower the risk in drilling of exploratory and development wells. Keywords : Acoustic impedance, Seismic inversion, Model-based, Lithofacies
The Gastrointestinal Tumor Microenvironment: An Updated Biological and Clinical Perspective

Gastrointestinal cancers are still responsible for high numbers of cancer-related deaths despite advances in therapy. Tumor-associated cells play a key role in tumor biology, by supporting or halting tumor development through the production of extracellular matrix, growth factors, cytokines, and extracellular vesicles. Here, we review the roles of these tumor-associated cells in the initiation, angiogenesis, immune modulation, and resistance to therapy of gastrointestinal cancers. We also discuss novel diagnostic and therapeutic strategies directed at tumor-associated cells and their potential benefits for the survival of these patients.
Parsing Natural Scenes Using Recursive Neural Network

This work explains and analyse the implementation of RNN (Recursive Neural Networks) proposed in [1] to identify different classes in an image. (For e.g. sky, building, water, etc). In our project the image is first over segmented into super-pixels using Mean Shift Algorithm, then the features are extracted for each of these segments and mapped into the semantic space. And finally using Recursive Neural Network architecture and features as input, we merge the different neighbouring segments based on the similarity in their features so as to output the parsed image.
The impact of Return on Assets (ROA) in relation with internal factors and external factors towards Casio Computer Co.,Ltd.'s performance

Corporate governance is considered as a significant implication for the growth of company. Good corporate governance plays an important role in enhancing the performance of company. Hence, this study targets to determine the impact of Return on Assets (ROA) in relation with determinants towards the selected company’s performance and the name of the selected company in this study is Casio Computer Co., Ltd. (Casio) which is under the electronic industry in Japan. This analysis shows that firm-specific factors (current ratio, quick ratio, average-collection period, debt to income ratio, operational ratio, operating margin and corporate governance index CGI ) and the macroeconomic factors (Gross Domestic Product (GDP), inflation rate, interest rate, exchange rate and STDV of price change in the stock market) each has its significant, small significant or insignificant on return on assets (ROA) of Casio. This study suggests that Casio can perform well management and good corporate governance practices to maximize its revenue or profits for enhancing its corporate performance.
VIETNAM’S RESPONSES TO PROVINCIAL ECONOMIC DISPARITIES THROUGH CENTRAL-PROVINCIAL GOVERNMENT FINANCIAL RELATIONS

The paper examines key changes in central-provincial government financial arrangements and their effects on provincial economic disparities in Vietnam over the period 2000-2008. We find that after 2004 transfers from the central to provincial governments conformed much more closely to objective and pre-determined criteria than before. Econometric estimations indicate that in the post-2004 sub-period, poorer provinces obtained more-than-proportionate assistance from the central government, and the favourable treatment was statistically significant. Responses from interviews and statistical data suggest that transfers from the central government played an important role in reducing poverty and provincial output disparities after 2004. The difficulties experienced by the central government in securing adequate
REGIONAL BASED QUERY IN GRAPH ACTIVE LEARN-

Graph convolution networks (GCN) have emerged as a leading method to classify nodes and graphs. These GCN have been combined with active learning (AL) methods, when a small chosen set of tagged examples can be used. Most ALGCN use the sample class uncertainty as selection criteria, and not the graph. In contrast, representative sampling uses the graph, but not the prediction. We propose to combine the two and query nodes based on the uncertainty of the graph around them. We here propose two novel methods to select optimal nodes in ALGCN that explicitly use the graph information to query for optimal nodes. The first method named regional uncertainty is an extension of the classical entropy measure, but instead of sampling nodes with high entropy, we propose to sample nodes surrounded by nodes of different classes, or nodes with high ambiguity. The second method called Adaptive Page-Rank is an extension of the page-rank algorithm, where nodes that have a low probability of being reached by random walks from tagged nodes are selected. We show that the latter is optimal when the fraction of tagged nodes is low, and when this fraction grows to one over the average degree, the regional uncertainty performs better than all existing methods. While we have tested these methods on graphs, such methods can be extended to any classification problem, where a distance can be defined between the input samples.
Optimal Control Synthesis for ω-regular Objectives with Abstraction-Refinement

This paper presents an abstraction-refinement based framework for optimal controller synthesis of discrete-time systems with respect to ω-regular objectives. It consists of first abstracting the discrete-time “concrete” system into a finite weighted transition system using a finite partition of the state-space. Then, a two-player mean payoff parity game is solved on the product of the abstract system and the Büchi automaton corresponding to the ω-regular objective, to obtain an optimal “abstract” controller that satisfies the ω-regular objective. The abstract controller is guaranteed to be implementable in the concrete discrete-time system, with a sub-optimal cost. The abstraction is refined with finer partitions to reduce the sub-optimality. In contrast to existing formal controller synthesis algorithms based on abstractions, apart from synthesizing a controller, this technique also provides an upper bound on the trajectory cost when implementing the suboptimal controller. In addition, under the assumption on the existence of certain robust controllers, the refinement procedure guarantees to find controllers whose costs are arbitrarily close to the optimal cost. An example is presented to illustrate the feasibility of the approach.
Convolutional LSTM for Next Frame Predic

Convolutional LSTM [27] is an increasingly popular and very promising algorithm to solve machine learning tasks in the context of video data. Next frame prediction, i.e. predicting how a video will continue, is the machine learning task subject to this thesis. The used data stems from car camera data sets, i.e. the scenes that are to be predicted show ordinary traffic situations recorded from a roof-mounted camera of a car. Convolutional LSTM has already proven feasibility for such tasks [18, 19]. This thesis explores new ways of presenting the data to a known model architecture, the Predictive Coding Network [18], which is based on convolutional LSTM. One approach is to present delta information, i.e. the change between two consecutive frames, to the model in order to ease the learning task. The second approach examined tries to bundle the model’s capacity as to concentrate on the prediction of a single frame instead of a sequence. In both cases, the model architecture is not or only marginally affected by the necessary changes. This ensures, that the models remain comparable. Previous to these experiments, a feature of convolutional LSTMs called peepholes [8] is studied with respect to its effectivity in the context of next frame prediction. This is done on a toy data set named Moving MNIST [28], while the prediction techniques are examined on the real world car camera sets KITTI [7] and Caltech Pedestrian [5].