Using sensor data from multiple modalities presents an opportunity to encode redundant and complementary features that can be useful when one modality is corrupted or noisy. Humans do this everyday, relying on touch and proprioceptive feedback in visually-challenging environments. However, robots might not always know when their sensors are corrupted, as even broken sensors can return valid values. In this work, we introduce the Crossmodal Compensation Model (CCM), which can detect corrupted sensor modalities and compensate for them. CMM is a representation model learned with self-supervision that leverages unimodal reconstruction loss for corruption detection. CCM then discards the corrupted modality and compensates for it with information from the remaining sensors. We show that CCM learns rich state representations that can be used for contact-rich manipulation policies, even when input modalities are corrupted in ways not seen during training time.

So far, the members of Joe Biden's foreign policy team are all veterans of Barack Obama's administration. They've pledged to revive Obama-era initiatives like the Iran nuclear deal and the Paris climate agreement that Donald Trump tried to undo, as well as recommit to long-term U.S. alliances. Some U.S. foreign policy critics from the left and the libertarian right are less than fully enthusiastic about this team. They don't particularly relish a return to the approach that led to the intervention in Libya, a ramped-up drone war, and a troop surge in Afghanistan, and are concerned that all the talk of "America is back" broadly suggests an embrace of the interventionist worldview that predated Trump. Progressive concerns about the more hawkish views of Michèle Flournoy (Democratic Rep. Ro Khanna is one representative example), who was thought to be a shoo-in for secretary of defense, are reportedly one reason why that position has not yet been announced.

The latest Drug Developing Platforms by Artificial Intelligence (AI) market report offers a detailed analysis of growth driving factors, challenges, and opportunities that will govern the industry expansion in the ensuing years. Besides, it delivers a complete assessment of several industry segments to provide a clear picture of the top revenue prospects of this industry vertical. According to industry analysts, the market is projected to accrue notable gains while recording a CAGR of XX% over the forecast period 2020-2025. Considering the impact of Covid-19, except from healthcare industries, the global health crisis has turned out to be a nightmare for majority of businesses. While some have successfully made changes to their business model or pivoted the entire organization's mission, others continue to face an onslaught of challenges.

The latest Artificial Intelligence (AI) in Insurance market report offers a detailed analysis of growth driving factors, challenges, and opportunities that will govern the industry expansion in the ensuing years. Besides, it delivers a complete assessment of several industry segments to provide a clear picture of the top revenue prospects of this industry vertical. According to industry analysts, the market is projected to accrue notable gains while recording a CAGR of XX% over the forecast period 2020-2025. Considering the impact of Covid-19, except from healthcare industries, the global health crisis has turned out to be a nightmare for majority of businesses. While some have successfully made changes to their business model or pivoted the entire organization's mission, others continue to face an onslaught of challenges.

TEHRAN, Iran (AP) -- Iran's supreme leader on Saturday demanded the "definitive punishment" of those behind the killing of a scientist who led Tehran's disbanded military nuclear program, as the Islamic Republic blamed Israel for a slaying that has raised fears of reignited tensions across the Middle East. After years of being in the shadows, the image of Mohsen Fakhrizadeh suddenly was to be seen everywhere in Iranian media, as his widow spoke on state television and officials publicly demanded revenge on Israel for the scientist's slaying. Israel, long suspected of killing Iranian scientists a decade ago amid earlier tensions over Tehran's nuclear program, has yet to comment on Fakhrizadeh's killing Friday. However, the attack bore the hallmarks of a carefully planned, military-style ambush, the likes of which Israel has been accused of conducting before. The attack has renewed fears of Iran striking back against the U.S., Israel's closest ally in the region, as it did earlier this year when a U.S. drone strike killed a top Iranian general.

Middle east is one of the top tech destinations where artificial intelligence is playing a significant role. The region is known for its oil wells, which are major contributors to the region's economy. Slowly the economy is shifting its base from petrochemicals to technology. The region is slowly shifting its economic dependence on oil wells. According to an IDC report, spending on AI in the region is expected to grow at an annual growth of 19%.

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.

How can we assess the value of data objectively, systematically and quantitatively? Pricing data, or information goods in general, has been studied and practiced in dispersed areas and principles, such as economics, marketing, electronic commerce, data management, data mining and machine learning. In this article, we present a unified, interdisciplinary and comprehensive overview of this important direction. We examine various motivations behind data pricing, understand the economics of data pricing and review the development and evolution of pricing models according to a series of fundamental principles. We discuss both digital products and data products. We also consider a series of challenges and directions for future work.

How should computer science and social science collaborate to build a common model? How should they proceed to gather data that is really useful to the modelling? How can they design a survey that is tailored to the target model? This paper aims to answer those crucial questions in the framework of a multidisciplinary research project. This research addresses the issue of co-constructing a model when several disciplines are involved, and is applied to modelling human behaviour immediately after an earthquake. The main contribution of the work is to propose a tool dedicated to multidisciplinary dialogue. It also proposes a reflexive analysis of the enriching intellectual process carried out by the different disciplines involved. Finally, from working with an anthropologist, a complementary view of the multidisciplinary process is given.

Recently, several studies have investigated active learning (AL) for natural language processing tasks to alleviate data dependency. However, for query selection, most of these studies mainly rely on uncertainty-based sampling, which generally does not exploit the structural information of the unlabeled data. This leads to a sampling bias in the batch active learning setting, which selects several samples at once. In this work, we demonstrate that the amount of labeled training data can be reduced using active learning when it incorporates both uncertainty and diversity in the sequence labeling task. We examined the effects of our sequence-based approach by selecting weighted diverse in the gradient embedding approach across multiple tasks, datasets, models, and consistently outperform classic uncertainty-based sampling and diversity-based sampling.