Miracle-dz/UniFAR-FTUs huggingface dataset:
Drug modulation of soluble biomarkers and oxidative stress in LLC-WRC 256 cell line: role in cell proliferation and angiogenesis

During the last years, studies were focused to understanding cancer mechanisms at the molecular level, identifying new biomarkers that interfere at different stages of tumour initiation and progression, but also in metastasis Angiogenesis is a complex, multistage process that includes the interaction between endothelial cells, neoplastic cells and extracellular matrix components. The molecular basis of this mechanism may be the increase of the production of angiogenic factors or may be the loss of angiogenesis inhibitors. Angiogenesis is regulated by growth factors, pro-angiogenic cytokines, angiogenesis modulators and neovascularization inhibitors. The regulation of tumour angiogenesis is achieved by the balance between activating factors and angiogenesis inhibitors. Aberrant glycosylation is often considered a "cancer fingerprint", playing a fundamental role in tumour development and progression. Thus, glycans play an important role in signaling cancer cells, dissociating and invasion of tumour cells, cell-matrix interactions, and angiogenesis, metastasis and immune modulation. Resistance to treatment, escape from host immune system control, tumour invasion and angiogenesis, metastasis are closely related to aberrant sialilation of glycoproteins and glycolipids. Oxidative stress can cause angiogenesis in breast carcinoma because oxygen radicals increase the production of angiogenic factors (IL-8 and VEGF), promote secretion of MMP-1, that aids the growth of vessels in the tumour microenvironment. Therefore, our study focused on modulation of oxidative stress and biomarkers release in cell culture supernatants of LLC-WRC 256 rat carcinosarcoma cell line, molecules with important functions in tumour proliferation and angiogenesis. In order to find alternative therapeutic approaches based on using lower concentrations of anti-cancer drugs (EPI, EDX), we investigated the regulatory roles of natural compounds (Rsv) added to cancer drugs, in order to diminish the undesirable side-effects, obtain a stronger anti-tumour respones and decrease the drug-resistance.
Mutual Transfer Learning for Massive Data

In the transfer learning problem, the target and the source data domains are typically known. In this article, we study a new paradigm called mutual transfer learning where among many heterogeneous data domains, every data domain could potentially be the target of interest, and it could also be a useful source to help the learning in other data domains. However, it is important to note that given a target not every data domain can be a successful source; only data sets that are similar enough to be thought as from the same population can be useful sources for each other. Under this mutual learnability assumption, a confidence distribution fusion approach is proposed to recover the mutual learnability relation in the transfer learning regime. Our proposed method achieves the same oracle statistical inferential accuracy as if the true learnability structure were known. It can be implemented in an efficient parallel fashion to deal with large-scale data. Simulated and real examples are analyzed to illustrate the usefulness of the proposed method.
Towards Semantically Enhanced Information Retrieval

- The paper discusses information retrieval on the World Wide Web. The focus is to retrieve and find the most useful and relevant. The challenge is to overcome the limitations of traditional keyword based search. A semantic search overcomes the drawbacks of overload and mismatch associated with keyword based search. A semantic search uses ontology and clustering algorithm to perform relevant and valuable retrieval of web documents. The approach is an attempt to improve the information retrieval process. It aims at finding the most useful information from huge amount of data available on the World Wide Web
General Transportability of Soft Interventions: Completeness Results

The challenge of generalizing causal knowledge across different environments is pervasive in scientiﬁc explorations, including in AI, ML, and Data Science. Experiments are usually performed in one environment (e.g., in a lab, on Earth) with the intent, almost invariably, of being used elsewhere (e.g., outside the lab, on Mars), where the conditions are likely to be different. In the causal inference literature, this generalization task has been formalized under the rubric of transportability (Pearl and Bareinboim, 2011), where a number of criteria and algorithms have been developed for various settings. Despite the generality of such results, trans-portability theory has been conﬁned to atomic, do() -interventions. In practice, many real-world applications require more complex, stochastic interventions; for instance, in reinforcement learning, agents need to continuously adapt to the changing conditions of an uncertain and unknown environment. In this paper, we extend transportability theory to encompass these more complex types of interventions, which are known as “soft,” both relative to the input as well as the target distribution of the analysis. Speciﬁcally, we develop a graphical condition that is both necessary and sufﬁcient for deciding soft-transportability. Second, we develop an algorithm to determine whether a non-atomic intervention is computable from a combination of the distributions available across domains. As a corollary, we show that the � -calculus is complete for the task of soft-transportability.
O N M EMORY IN H UMAN AND A RTIFICIAL L ANGUAGE P ROCESSING S YSTEMS

Memory in humans and artificial intelligence (AI) systems has similar functions— both are responsible for encoding, retrieving, and storing of information. While memory in humans has specialized systems for different functions (e.g., working memory, semantic memory, episodic memory), memory in AI systems is often implicitly represented in the weights of parametric neural networks. Focusing on language processing systems, we argue that this property makes it hard for AI systems to generalize across complex linguistic tasks. We consider the separation of computation and storage as necessary, suggest desired properties of the storage system, and discuss the benefit of integrating different types of human memory (as separate modules) into next-generation language processing systems.
VRTyper: Keyboard for Virtual Reality Add On

Virtual Reality is often touted as the future of schools, businesses, and social gatherings. People will soon be able to use the platform to learn in a virtual classroom or conduct meetings in a virtual conference room. However, the ability to interact with the virtual environment is paramount to Virtual Reality being widely adopted across these areas. Users need to be able to do things like write and read with the same ease with which they carry out these basic tasks in the physical world. One large barrier to converting these physical spaces into virtual ones is simply one’s ability to type. In a virtual conference room, you would most likely have your computer in front of you in order to take notes. How can you take notes while you’re wearing a headset? Do you type on a real keyboard that is projected into the Virtual Space so that you can visualize it? Do you type freely on a keyboard that’s projected anywhere onto the scene? How do we simulate a real keyboard without additional bulky hardware? Answering these questions will help to facilitate the adoption of Virtual Reality within more disciplines than just gaming, and increase the variety of situations in which it’s beneficial for people to interact with each other virtually. We sought to create a simple, low-cost keyboard without the lag usually associated with VR tracking.
Entity Skeletons for Visual Storytelling

We are enveloped by stories of visual interpretations in our everyday lives. Story narration often comprises of two stages, which are, forming a central mind map of entities and then weaving a story around them. In this paper, we address these two stages of introducing the right entities at seemingly reasonable junctures and also referring them co-herently in the context of visual storytelling. The building blocks of the central mind map, also known as entity skeleton are entity chains including nominal and coreference expressions. We establish a strong baseline for skeleton informed generation and propose a glocal hierarchical attention model that attends to the skeleton both at the sentence (local) and the story (global) levels. We observe that our proposed models outperform the baseline in terms of automatic evaluation metric, METEOR. We also conduct human evaluation from which it is concluded that the visual stories generated by our model are preferred 82% of the times.
Weakly Supervised Object Localization via Sensitivity Analysis

Deep Convolutional Neural Networks (CNNs) have been repeatedly shown to perform well on image classiﬁcation tasks, successfully recognizing a broad array of objects when given sufﬁcient training data. Methods for object localization, however, are still in need of substantial improvement. In this paper, we discuss a fast and simple approach to the localization of recognized objects in images. Our method is predicated on the idea that a deep CNN capable of recognizing an object must implicitly contain knowledge about object location in its connection weights. We provide an easy method to interpret classiﬁer weights in the context of individual classiﬁed images. This method involves the calculation of the derivative of output class label activation with regard to each input pixel, performing a sensitivity analysis that identiﬁes the pixels that, in a local sense, have the greatest inﬂuence on object recognition. Our experimental results, using real-world data sets for which ground-truth localization information is known, reveal competitive accuracy from our extremely fast technique.
for: Towards non-parametric drift detection via Dynamic Adapting Window Independence Drift Detection (DAWIDD)

Show ”1. ⇒ 2. ”: Since PT is finite we may write (PT × PT )(A) = ∫ PT (A )PT (dx), where A = {y|(x, y) ∈ A}. This implies that PT ({s ∈ T |ps = pt}) = 1 for PT -a.s. all t ∈ [0, 1]. Therefore there exists a t0 ∈ [0, 1] such that pt = pt0 for PT -a.s. all t ∈ [0, 1]; so we may choose PX = pt0 . Show ”2. ⇐⇒ 3. ”: Since B(R) has a intersection stable countable generator the statement follows by the fact that in this case it holds pt ⊗ P = qt ⊗Q⇔ P = Q and pt = qt P -a.s. for probability measures P,Q and Markov kernels pt, qt.
A TTENTION D RIVEN D YNAMIC M EMORY M APS

In order to act in complex, natural environments, biological intelligence has developed attention to collect limited informative observations, a short term memory to store them, and the ability to build live mental models of its surroundings. We mirror this concept in artificial agents by learning to 1) guide an attention mechanism to the most informative parts of the state, 2) efficiently represent state from a sequence of partial observations, and 3) update unobserved parts of the state through learned world models. Key to this approach is a novel short-term memory architecture, the Dynamic Memory Map (DMM), and an adversarially trained attention controller. We demonstrate that our approach is effective in predicting the full state from a sequence of partial observations. We also show that DMMs can be used for control, outperforming baselines in two partially observable reinforcement learning tasks.
S IMULATED SLEEP HELPS TO GENERALIZE KNOWL - EDGE IN A SPIKING NETWORK TRAINED WITH SPIKE - TIMING DEPENDENT PLASTICITY

Artiﬁcial neural networks are known to generalize poorly to new examples; although they excel at representing data observed in the training set, they are unable to represent data drawn from different distributions. In the mammalian brain, evidence suggests that sleep promotes generalization of learned examples. To address the validity of this hypothesis, we utilized a previously developed spiking neural network trained with spike-timing dependent plasticity (STDP) to perform digit classiﬁcation on the MNIST dataset. We demonstrate that incorporating an ofﬂine, sleep-like period after training leads to generalization and robustness to novel inputs
Machine Learning - A Neoteric Medicine to Healthcare

: Health is an important resource for a human being to live in our society from any kind of disease. The fast development of the population, it appears to be trying to record and dissect the large measure of data about patients. Healthcare is a need, and clinical specialists are constantly attempting to discover approaches to actualize innovations and give effective outcomes. The main challenges of healthcare industries are rising healthcare costs that include prediction and diagnosis of diseases, drug discovery, medical imaging diagnosis, personalized medicine, behavior modification, and smart health records. Machine learning gives us such an approach to discover and process this information naturally, which makes the human services framework progressively powerful and strong. Getting the correct determination is a key part of Healthcare - it clarifies a patient's medical issue and suggests health care treatment. The disease diagnostic procedure is a complex, community-oriented action that includes clinical intelligent and data social events to decide a patient's medical issue. Google has built up an ML model to help recognize dangerous tumors on mammograms. Stanford’s profound learning calculation to distinguish skin malignancy. JAMA article discussed a new ML model that had the option to analyze diabetic retinopathy in retinal pictures. Unmistakably ML places another bolt in the bunch of clinical dynamic. The above mentioned are the few of the hottest research trends of ML in Healthcare. In this work, we focused more on the importance of Machine Learning in Healthcare like the latest research works in healthcare, different application areas
Patterns of abundance across geographical ranges as a predictor for responses to climate change: Evidence from UK rocky shores

Understanding patterns in the abundance of species across thermal ranges can give useful insights into the potential impacts of climate change. The abundant‐centre hypothesis suggests that species will reach peak abundance at the centre of their thermal range where conditions are optimal, but evidence in support of this hypothesis is mixed and limited in geographical and taxonomic scope. We tested the applicability of the abundant‐centre hypothesis across a range of intertidal organisms using a large, citizen science‐generated data set.
Bayesian Estimation of RGP90 ELISA Parameters for Diagnosis of Equine Infectious Anemia

Copyright: © Amaral Haddad JP (2020). This Article is distributed under the terms of Creative Commons Attribution 4.0 International License *Corresponding Author(s): Joao Paulo Amaral Haddad Department of Preventive Veterinary Medicine, School of Veterinary Medicine, Federal University of Minas Gerais, Belo Horizonte / MG, Av. Antônio Carlos 6627 CEP: 31 270-901, Brazil Email: jphaddad01@globo.com Journal of Veterinary Medicine and Animal Sciences
Batch Reinforcement Learning with Hyperparameter Gradients

We consider the batch reinforcement learning problem where the agent needs to learn only from a ﬁxed batch of data, without further interaction with the environment. In such a scenario, we want to prevent the optimized policy from deviating too much from the data collection policy since the estimation becomes highly unstable otherwise due to the off-policy nature of the problem. However, imposing this requirement too strongly will result in a policy that merely follows the data collection policy. Unlike prior work where this trade-off is controlled by hand-tuned hyperparameters, we propose a novel batch reinforcement learning approach, batch optimization of policy and hyper-parameter (BOPAH), that uses a gradient-based optimization of the hyperparameter using held-out data. We show that BOPAH outperforms other batch reinforcement learning algorithms in tabular and continuous control tasks, by ﬁnding a good balance to the trade-off between adhering to the data collection policy and pursuing the possible policy improvement.
Individualized Running Wheel System with a Dynamically Adjustable Exercise Area and Speed for Rats Following Ischemic Stroke

Background An innovative animal running wheel with an individualized design was implemented for the rehabilitation of rats following ischemic stroke. Material/Methods The design of the running wheel platform included the running wheel and a side plate for exercise area adjustments. A U-curve with a width of 2 cm was drawn on the lower half of the side plate for the dynamic adjustments of five infrared (IR) sensors based on the physical fitness of the rats. The individualized training process for this running wheel consisted of 2 days of free training to record their average and maximum speeds, 3 days of progressive training to determine their exercise areas, and 2 weeks of normal training based on their average speeds, maximum speeds, and exercise areas. Blood samples were obtained from the tail veins of all rats before the operations and on Days 14, 21, and 28 postsurgery to measure cortisol levels. The motor function tests were performed on Days 7 and 28 postsurgery. On Day 28 postsurgery, the rats were sacrificed under anesthesia, and their brains were removed for Nissl and H&E staining. Results On Day 28 after surgery, the motor function, lesion volume, and cell damage of the DEARW and control groups differed significantly, indicating that this device is effective for stroke rehabilitation. Conclusions The outcomes of the rats that were rehabilitated using the newly designed training system were better than those of their control-group counterparts, indicating the advantages of this designed system.
Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks

We study the problem of constructing black-box adversarial attacks, where no model information is revealed except for the feedback knowledge of the given inputs. To obtain sufﬁcient knowledge for crafting adversarial examples, previous methods query the target model with inputs that are perturbed with different searching directions. However, these methods suffer from poor query efﬁciency since the employed searching directions are sampled randomly. To mitigate this issue, we formulate the goal of mounting efﬁcient attacks as an optimization problem in which the adversary tries to fool the target model with a limited number of queries. Under such settings, the adversary has to select appropriate searching directions to reduce the number of model queries. By solving the efﬁcient-attack problem, we ﬁnd that we need to distill the knowledge in both the path of the adversarial examples and the path of the searching directions. Therefore, we propose a novel framework, dual-path distillation, that utilizes the feedback knowledge not only to craft adversarial examples but also to alter the searching directions to achieve efﬁcient attacks. Experimental results suggest that our framework can signiﬁcantly increase the query efﬁciency.
The Role of Micro-Credit in Economic Empowerment of the Rural Poor: A Case Study of Iwo Town, Osun State

Poverty as one of the major development problems that Nigeria is faced with, is seen mostly as an unpleasant situation where poor people earn so small income, with consumption not adequate to be classified as a satisfactory living standard; addressing this situation is hence imperative for the country to progress. Microfinance is a vital development approach used in alleviating poverty as well as empowering the poor. Using Iwo Town as a case study, the research investigated the contribution of microfinance to the socio-economics of the rural people where financial exclusion is high. The study was based on the Grameen Bank concept of collateral-free micro-credit advanced to poor entrepreneurs in a bid to enhance their productivity for income generation towards reducing their level of poverty. The research employed the twin methods of qualitative (interview) and quantitative (questionnaire) means of data collection. The questionnaire was administered over 60 clients of the microfinance institutions (MFIs) purposively sampled. The ordinary least squares (OLS) regression was employed to carry out the analysis of the data collected, and the research result showed that micro-credit has the potential to effectively alleviate poverty in the society through empowering the people financially. The research investigated the effect of micro-credit on the socio-economics of the people of Iwo Town using poverty as its indicator. The role of micro-credit in poverty alleviation was assessed through change in income to beneficiaries of micro-credit from microfinance institutions (MFIs). The result showed that there is a positive and significant relationship between productive utilization of microcredit and the poverty level of the people. This is evident in its ability to increase income.
Specifying preference heterogeneity regarding natural attributes of urban green spaces to inform renaturation policies

It is now acknowledged that greening cities influences positively the quality of life of dwellers through several ways, such as increasing outdoor recreational opportunities, improving in turn social connections, physical or mental health. However, in urban planning, it is usually implicitly assumed that any type of urban green space will have the same effect on citizen s well-being. The location and design of urban greening investments are barely compared to the needs of the city dwellers. This paper addresses the demand side of urban greening by questioning which green spaces characteristics people value. To do so, we applied a distance-based choice experiment to capture the tradeoff between the green spaces attributes (e.g. tree cover, size, shape) and the travel time citizens are willing to spend to reach a hypothetical site. We found a strong heterogeneity among the interviewees expectations regarding a green space. We show that dwellers tastes are influenced by many parameters such as geographical and socioeconomic characteristics, or the activities they are used to practice. The results suggest that accounting for potential users preferences regarding urban nature characteristics is critical to inform urban planning in order to build efficient designs of urban greening programs.
Flow Experience and MOOC Acceptance: Mediating Role of MOOC Satisfaction

The primary purpose of this study is to examine the impact of flow experience on acceptance of Massive Open Online Courses (MOOC). It has also investigated whether MOOC satisfaction would mediate the relationship between flow experience and MOOC acceptance. The proposed research model was tested using a survey research method. Data was collected from 310 MOOC users using an online questionnaire and was analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results confirmed that flow experience impacts MOOC acceptance and MOOC satisfaction plays a mediating role in the relationship between flow experience and MOOC acceptance.