Identification of Ajnala skeletal remains using multiple forensic anthropological methods and techniques: a bioarchaeological report.

Fragmented and badly damaged commingled human remains present a tough challenge for their identification pursuits in forensic anthropology. Thousands of unknown human remains along with items of contextual identity, reportedly belonging to 282 Indian sepoys killed in 1857, were exhumed non-scientifically from a disused ancient well at Ajnala (Amritsar, India). In this manuscript, the non-scientific excavation of unknown human remains from the abandoned well, their forensic anthropological strategies for identification purposes, challenges being faced and future possibilities of their biological profiling have been discussed. Multiple methods and techniques like anthropological examinations, odontological profiling, radiological analyses, stable isotope and mitochondrial DNA (mtDNA) analyses were applied to few bones and teeth collected from the Ajnala skeletal assemblage. Though majority of studied bones and teeth were found belonging to adult males, very few of them had morphological, osteological and molecular features questioning the authenticity and validity of the written records. Due to certain ambiguous findings or gaps observed between the anthropological analyses of the Ajnala skeletal remains and the reported versions about their affiliations; certain advanced radiological, chemical and molecular techniques were applied to estimate their probable age, sex and populational affinity. The obtained radiological, isotopic and molecular signatures of the remains were compared with the available databases to estimate their affinity with the individuals of geographic area to whom the remains reportedly belonged to. The maternally inherited mtDNA haplogroup assignments, and stable isotope analysis of carbon and oxygen suggested that the studied human remains belonged to the individuals from West Bengal, Bihar, Odisha, Awadh (presently in Uttar Pradesh) and parts of Meghalaya and Manipur as potential regions of their geographic identity and thus, attributing the victims to be non-local to the site. However, merely on the basis of forensic anthropological examinations of very few bones and teeth (collected out of a huge collection of thousands of bones and teeth); it would be just an unqualified and sweeping conclusion to claim their identity as adult or non-adult, male or female, local or non-local, victims of 1857 mass killings or to the victims of ceremonial sacrifices or criminal activities committed in the past. A sufficient number of bones and teeth along with items of personal identity needs to be examined with multiple scientific techniques to arrive at some valid conclusions about their biological identity

Large Subcapsular Liver Hematoma Following Single-Incision Laparoscopic Cholecystectomy

Single incision laparoscopic cholecystectomy may pose significant risks that warrant operative caution. The authors suggest that single incision laparoscopic surgery be compared to the “gold-standard” laparoscopic procedure to further elucidate benefits and complications of this novel technique

Feudal Networks for Hierarchical Reinforcement Learning Revisited

Hierarchical Reinforcement Learning (RL) has gained popularity in recent years in designing RL algorithms that converge in complex environments. Convergence of RL algorithms remains an active area of research, and no single approach has been found to work for all RL applications. Feudal networks (FuNs) are a hierarchical RL technique attempting to address portability and other problems by defining an internal structure for an RL agent using a Manager-Worker hierarchy. A Manager is that portion of the system utilizing a low temporal resolution component for setting goals to maximize rewards from the environment, while the Worker utilizes a high temporal resolution component for selecting among action primitives to maximize rewards from the Manager. This thesis provides an overview of reinforcement learning and the FuN architecture, then compares the relative convergence rates of untrained FuNs to FuNs constructed by Workers with different physical embodiments under a trained Manager

Recent advances in the application of stable isotope ratio analysis in forensic chemistry

This review paper updates the previous literature in relation to the continued and developing use of stable isotope ratio analysis in samples which are relevant to forensic science. Recent advances in the analysis of drug samples, explosive materials, and samples derived from human and animal samples are discussed. The paper also aims to put the use of isotope ratio mass spectrometry into a forensic context and discuss its evidential potential

SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications

We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and information extraction communities

Simulation-Based Training for Patient Safety: 10 Principles That Matter

Simulation-based training can improve patient care when factors influencing its design, delivery, evaluation, and transfer are taken into consideration. In this paper, we provide a number of principles and practical tips that organizations in health care can use to begin implementing effective simulation-based training as a way to enhance patient safety. We commend the health care community for their efforts thus far. We hope that the information provided in this paper will encourage thinking beyond the bells and whistles of the simulation and bring to light full potential of simulation-based training in health care and patient safety

emoji2vec: Learning Emoji Representations from their Description

Many current natural language processing applications for social media rely on representation learning and utilize pre-trained word embeddings. There currently exist several publicly-available, pre-trained sets of word embeddings, but they contain few or no emoji representations even as emoji usage in social media has increased. In this paper we release emoji2vec, pre-trained embeddings for all Unicode emoji which are learned from their description in the Unicode emoji standard. The resulting emoji embeddings can be readily used in downstream social natural language processing applications alongside word2vec. We demonstrate, for the downstream task of sentiment analysis, that emoji embeddings learned from short descriptions outperforms a skip-gram model trained on a large collection of tweets, while avoiding the need for contexts in which emoji need to appear frequently in order to estimate a representation

Dense Antihydrogen: Its Production and Storage to Envision Antimatter Propulsion

We discuss the possibility that dense antihydrogen could provide a path towards a mechanism for a deep space propulsion system. We concentrate at first, as an example, on Bose-Einstein Condensate (BEC) antihydrogen. In a Bose-Einstein Condensate, matter (or antimatter) is in a coherent state analogous to photons in a laser beam, and individual atoms lose their independent identity. This allows many atoms to be stored in a small volume. In the context of recent advances in producing and controlling BECs, as well as in making antihydrogen, this could potentially provide a revolutionary path towards the efficient storage of large quantities of antimatter, perhaps eventually as a cluster or solid.Comment: 12 pages, 3 figure