Biomedical machine reading comprehension (biomedical-MRC) aims to comprehend complex biomedical narratives and assist healthcare professionals in retrieving information from them. The high performance of modern neural network-based MRC systems depends on high-quality, large-scale, human-annotated training datasets. In the biomedical domain, a crucial challenge in creating such datasets is the requirement for domain knowledge, inducing the scarcity of labeled data and the need for transfer learning from the labeled general-purpose (source) domain to the biomedical (target) domain. However, there is a discrepancy in marginal distributions between the general-purpose and biomedical domains due to the variances in topics. Therefore, direct-transferring of learned representations from a model trained on a general-purpose domain to the biomedical domain can hurt the model's performance. We present an adversarial learning-based domain adaptation framework for the biomedical machine reading comprehension task (BioADAPT-MRC), a neural network-based method to address the discrepancies in the marginal distributions between the general and biomedical domain datasets. BioADAPT-MRC relaxes the need for generating pseudo labels for training a well-performing biomedical-MRC model. We extensively evaluate the performance of BioADAPT-MRC by comparing it with the best existing methods on three widely used benchmark biomedical-MRC datasets -- BioASQ-7b, BioASQ-8b, and BioASQ-9b. Our results suggest that without using any synthetic or human-annotated data from the biomedical domain, BioADAPT-MRC can achieve state-of-the-art performance on these datasets. Availability: BioADAPT-MRC is freely available as an open-source project at \url{https://github.com/mmahbub/BioADAPT-MRC}.

Learning to derive subgoals reduces the gap between experts and students and makes students prepared for future problem solving. Researchers have explored subgoal labeled instructional materials with explanations in traditional problem solving and within tutoring systems to help novices learn to subgoal. However, only a little research is found on problem-solving strategies in relationship with subgoal learning. Also, these strategies are under-explored within computer-based tutors and learning environments. Backward problem-solving strategy is closely related to the process of subgoaling, where problem solving iteratively refines the goal into a new subgoal to reduce difficulty. In this paper, we explore a training strategy for backward strategy learning within an intelligent logic tutor that teaches logic proof construction. The training session involved backward worked examples (BWE) and problem-solving (BPS) to help students learn backward strategy towards improving their subgoaling and problem-solving skills. To evaluate the training strategy, we analyzed students' 1) experience with and engagement in learning backward strategy, 2) performance, and 3) proof construction approaches in new problems that they solved independently without tutor help after each level of training and in post-test. Our results showed that, when new problems were given to solve without any tutor help, students who were trained with both BWE and BPS outperformed students who received none of the treatment or only BWE during training. Additionally, students trained with both BWE and BPS derived subgoals during proof construction with significantly higher efficiency than the other two groups.

In this paper, we show that the process of continually learning new tasks and memorizing previous tasks introduces unknown privacy risks and challenges to bound the privacy loss. Based upon this, we introduce a formal definition of Lifelong DP, in which the participation of any data tuples in the training set of any tasks is protected, under a consistently bounded DP protection, given a growing stream of tasks. A consistently bounded DP means having only one fixed value of the DP privacy budget, regardless of the number of tasks. To preserve Lifelong DP, we propose a scalable and heterogeneous algorithm, called L2DP-ML with a streaming batch training, to efficiently train and continue releasing new versions of an L2M model, given the heterogeneity in terms of data sizes and the training order of tasks, without affecting DP protection of the private training set. An end-to-end theoretical analysis and thorough evaluations show that our mechanism is significantly better than baseline approaches in preserving Lifelong DP. Lifelong learning (L2M) is crucial for machine learning (ML) to acquire new skills through continual learning, pushing ML toward a more human learning in reality. Given a stream of different tasks and data, a deep neural network (DNN) can quickly learn a new task, by leveraging the acquired knowledge after learning previous tasks, under constraints in terms of the amount of computing and memory required (Chaudhry et al., 2019). As a result, it is quite challenging to train an L2M model with a high utility. In practice, the privacy risk will be more significant since an adversary can observe multiple versions of an L2M model released after training on each task.

Financial Technology (FinTech) has experienced exponential growth in recent years with this trend set to continue. In FinTech at DBS is tailored to suit the demands of this thriving industry and provide students with relevant, employable skills. The Masters has been listed by Fintechnews Switzerland in their Top 10 Master's Degrees in Fintech in Europe, so why not study an MSc in FinTech at DBS? The DBS MSc in FinTech programme focuses on practical skills in core areas such as financial analytics, advanced databases, disruptive technologies, web technologies and security while also offering applied skills in contemporary topics such as data analytics, and financial applications. Its aim is to create a critical understanding of core financial technologies and financial systems while also enhancing the practical technical skills of the learners.

If the question has other problems (such as being a homework dump), guide them to improve their question before reposting it. Among several ideas that come to mind throughout the research process, how do I filter them and evaluate my assumptions before experiments and coding? Is there any general hands-on justification?

Udemy is the biggest website in the world that offer courses in many categories, all the skills that you would be looking for are offered in Udemy, including languages, design, marketing and a lot of other categories, so when you ever want to buy a courses and pay for a new skills, Udemy would be the best forum for you. You can find payment courses, 100 free courses and coupons also, more than 12 categories are offered, and that what makes sure you will find the domain and the skill you are looking for. Our duty is to search for 100 off courses and free coupons. AI is an enabler in transforming diverse realms by exploiting deep learning architectures. The course aims to expose students to cutting-edge algorithms, techniques, and codes related to AI and particularly the Generative Adversarial Networks used for data creation in deep learning routines.

At a time when the US and China are divided on everything from economics to human rights, artificial intelligence is still a point of particular friction. With the potential to revolutionise everything from food production and health care to financial markets and surveillance, it's a technology that sparks both optimism and paranoia. One of the field's most influential figures is Andrew Chi-Chih Yao, whose education and professional life have straddled the world's two biggest economies. China-born and Harvard-trained, Yao is his country's only recipient of the Turing Award, computer science's equivalent of a Nobel Prize. After almost 40 years in the US, he returned to China in 2004.

What was your first reaction when you heard about Blake Lemoine, the Google engineer who announced last month the AI program he was working on had developed consciousness? If, like me, you're instinctively suspicious, it might have been something like: Is this guy serious? Does he honestly believe what he is saying? Or is this an elaborate hoax? Put the answers to those questions to one side.

This is one of the insights from a survey conducted by IEEE and it speaks to the growing importance of AI. The conversation about this technology spans possibilities, sectors, and use cases, both present and predicted. Depending on where you stand, this might seem very scary or exciting, or both. In business today, AI is being used beyond IT departments, through integration in key parts of an organization's operations. A good rule for the extent of AI adoption is that anything that is a process will eventually be done by AI.

We're just now sending invites and beginning to confirm speakers for the upcoming edition in January. If you'd like to join us as a speaker, take a look at our Proposals page. As Vice President of Developer Experience at Treeverse, Adi Polak shapes the future of data & ML technologies for hands-on builders. She also contributes to the lakeFS open-source, a git-like interface for object stores. In her work, she brings her vast industry research and engineering experience to bear in educating and helping teams design, architect, and build cost-effective data systems and machine learning pipelines that emphasize scalability, expertise, and business goals.