In this paper, we introduce ProtoPShare, a self-explained method that incorporates the paradigm of prototypical parts to explain its predictions. The main novelty of the ProtoPShare is its ability to efficiently share prototypical parts between the classes thanks to our data-dependent merge-pruning. Moreover, the prototypes are more consistent and the model is more robust to image perturbations than the state of the art method ProtoPNet. We verify our findings on two datasets, the CUB-200-2011 and the Stanford Cars.

Most of the existing medicine recommendation systems that are mainly based on electronic medical records (EMRs) are significantly assisting doctors to make better clinical decisions benefiting both patients and caregivers. Even though the growth of EMRs is at a lighting fast speed in the era of big data, content limitations in EMRs restrain the existed recommendation systems to reflect relevant medical facts, such as drug-drug interactions. Many medical knowledge graphs that contain drug-related information, such as DrugBank, may give hope for the recommendation systems. However, the direct use of these knowledge graphs in the systems suffers from robustness caused by the incompleteness of the graphs. To address these challenges, we stand on recent advances in graph embedding learning techniques and propose a novel framework, called Safe Medicine Recommendation (SMR), in this paper. Specifically, SMR first constructs a high-quality heterogeneous graph by bridging EMRs (MIMIC-III) and medical knowledge graphs (ICD-9 ontology and DrugBank). Then, SMR jointly embeds diseases, medicines, patients, and their corresponding relations into a shared lower dimensional space. Finally, SMR uses the embeddings to decompose the medicine recommendation into a link prediction process while considering the patient's diagnoses and adverse drug reactions. To our best knowledge, SMR is the first to learn embeddings of a patient-disease-medicine graph for medicine recommendation in the world. Extensive experiments on real datasets are conducted to evaluate the effectiveness of proposed framework.

Electricity load forecasting enables the grid operators to optimally implement the smart grid's most essential features such as demand response and energy efficiency. Electricity demand profiles can vary drastically from one region to another on diurnal, seasonal and yearly scale. Hence to devise a load forecasting technique that can yield the best estimates on diverse datasets, specially when the training data is limited, is a big challenge. This paper presents a deep learning architecture for short-term load forecasting based on bidirectional sequential models in conjunction with feature engineering that extracts the hand-crafted derived features in order to aid the model for better learning and predictions. In the proposed architecture, named as Deep Derived Feature Fusion (DeepDeFF), the raw input and hand-crafted features are trained at separate levels and then their respective outputs are combined to make the final prediction. The efficacy of the proposed methodology is evaluated on datasets from five countries with completely different patterns. The results demonstrate that the proposed technique is superior to the existing state of the art.

Can computers read and apply legal rules? It's an idea that's gaining momentum, as it promises to make laws more accessible to the public and easier to follow. But it raises a host of legal, technical and ethical questions. The OECD recently published a white paper on "Rules as Code" efforts around the world. The Australian Senate Select Committee on Financial Technology and Regulatory Technology will be accepting submissions on the subject until 11 December 2020.

The market research report compiles information from over 120 hardware and software companies to identify the key trends in the drone industry. The major players of the drone industry are compared within the areas of drone industry such as software, hardware, and analytics. This provides you with the knowledge to make informed decisions and understanding of this disruptive and fast-growing market area. These use cases include Search and Rescue, Agriculture, Delivery, Security, Mapping and Localisation.

As even the most inattentive observer of contemporary international politics will attest, technological competition – mostly, but not always, between the U.S. and its allies on one hand, and China and Russia on the other – has once again risen to the fore. Analysts, so far, have approached this issue from various angles: what it means in terms of military balances, the possibility of international cooperation, what a technological edge implies for domestic policies, and so on. The outgoing Trump administration has made technological contestation with China a cornerstone of its strategic policy, emphasizing the need for the United States to maintain its edge when it comes to artificial intelligence (AI), quantum information science, and aerospace and other critical technologies, among others. Other Indo-Pacific powers, such as Australia, India, and Japan, have also joined the fray in pushing both new and emerging tech at home as well as promoting collaboration around it between "like-minded countries." In June this year, a Global Partnership on Artificial Intelligence of 14 states along with the European Union was launched, to facilitate collective AI research as well as implementation.

Rideshare platform Ola, is using the power of artificial intelligence to help increase safety in the rideshare industry. The new safety-tech feature Guardian will be the first of its kind in New Zealand and is set to roll out in all its locations across the country early next year. The safety technology uses real-time trip information to detect irregular activity such as, a possible crash, an unusually long stop, or an unexpected deviation from the planned route. Ola's 24/7 Safety Response Team is alerted and they contact both riders and drivers to confirm they are safe and offer any assistance they might need. Because Guardian is an intelligent product built using machine learning, it is able to continuously improve its ability to predict risk signals as it keeps collecting data over time, says Ola.

A graphic illustration showing how the technology combines the core software needed to drive AI with image-capturing hardware, in a single electronic device. Prototype tech shrinks AI to deliver brain-like functionality in one powerful device. Researchers have developed artificial intelligence technology that brings together imaging, processing, machine learning, and memory in one electronic chip, powered by light. The prototype shrinks artificial intelligence technology by imitating the way that the human brain processes visual information. The nanoscale advance combines the core software needed to drive artificial intelligence with image-capturing hardware in a single electronic device.

See also our related columns The Turning Point, Techie Tuesdays, and Storybites. Though artificial intelligence (AI) may not surpass human intelligence for at least a few more decades, it opens up opportunities and challenges that we must address today in order to shape a better world for us all. A call to action for business leaders, entrepreneurs, academics, and policymakers is effectively made in Toby Walsh's new book, 2062: The World that AI Made. The rise of AI poses serious philosophical, economic and social questions for all of us, and more vision and collaboration are urgently called for. How many jobs will AI take away or create?

Linking clinical narratives to standardized vocabularies and coding systems is a key component of unlocking the information in medical text for analysis. However, many domains of medical concepts lack well-developed terminologies that can support effective coding of medical text. We present a framework for developing natural language processing (NLP) technologies for automated coding of under-studied types of medical information, and demonstrate its applicability via a case study on physical mobility function. Mobility is a component of many health measures, from post-acute care and surgical outcomes to chronic frailty and disability, and is coded in the International Classification of Functioning, Disability, and Health (ICF). However, mobility and other types of functional activity remain under-studied in medical informatics, and neither the ICF nor commonly-used medical terminologies capture functional status terminology in practice. We investigated two data-driven paradigms, classification and candidate selection, to link narrative observations of mobility to standardized ICF codes, using a dataset of clinical narratives from physical therapy encounters. Recent advances in language modeling and word embedding were used as features for established machine learning models and a novel deep learning approach, achieving a macro F-1 score of 84% on linking mobility activity reports to ICF codes. Both classification and candidate selection approaches present distinct strengths for automated coding in under-studied domains, and we highlight that the combination of (i) a small annotated data set; (ii) expert definitions of codes of interest; and (iii) a representative text corpus is sufficient to produce high-performing automated coding systems. This study has implications for the ongoing growth of NLP tools for a variety of specialized applications in clinical care and research.