Named Entity Recognition (NER) is a critical component of Natural Language Processing (NLP) for extracting structured information from unstructured text. However, for low-resource languages like Catalan, the performance of NER systems often suffers due to the lack of high-quality annotated datasets. This paper introduces NERCat, a fine-tuned version of the GLiNER[1] model, designed to improve NER performance specifically for Catalan text. We used a dataset of manually annotated Catalan television transcriptions to train and fine-tune the model, focusing on domains such as politics, sports, and culture. The evaluation results show significant improvements in precision, recall, and F1-score, particularly for underrepresented named entity categories such as Law, Product, and Facility. This study demonstrates the effectiveness of domain-specific fine-tuning in low-resource languages and highlights the potential for enhancing Catalan NLP applications through manual annotation and high-quality datasets.

The necessity of data driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a Healthcare Provider Facility or a Hospital (from here on termed as Facility) Market Share is of key importance. This pilot study aims at developing a data driven Machine Learning - Regression framework which aids strategists in formulating key decisions to improve the Facilitys Market Share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study; and the data spanning across 60 key Facilities in Washington State and about 3 years of historical data is considered. In the current analysis Market Share is termed as the ratio of facility encounters to the total encounters among the group of potential competitor facilities. The current study proposes a novel two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. The proposed method to identify pool of competitors in current analysis, develops Directed Acyclic Graphs (DAGs), feature level word vectors and evaluates the key connected components at facility level. This technique is robust since its data driven which minimizes the bias from empirical techniques. Post identifying the set of competitors among facilities, developed Regression model to predict the Market share. For relative quantification of features at a facility level, incorporated SHAP a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share.

Scientists have begun turning to new tools offered by machine learning to help save time and money. In the past several years, nuclear physics has seen a flurry of machine learning projects come online, with many papers published on the subject. Now, 18 authors from 11 institutions summarize this explosion of artificial intelligence-aided work in "Machine Learning in Nuclear Physics," a paper recently published in Reviews of Modern Physics. "It was important to document the work that has been done. We really do want to raise the profile of the use of machine learning in nuclear physics to help people see the breadth of the activities," said Amber Boehnlein, lead author of the paper and the associate director for computational science and technology at the U.S. Department of Energy's Thomas Jefferson National Accelerator Facility.

Commentators on AI converge on two goals they believe define the field: (1) to better understand the mind by specifying computational models and (2) to construct computer systems that perform actions traditionally regarded as mental. We should recognize that AI has a third, hidden, more basic aim; that the first two goals are special cases of the third; and that the actual technical substance of AI concerns only this more basic aim. This third aim is to establish new computation-based representational media, media in which human intellect can come to express itself with different clarity and force. This article articulates this proposal by showing how the intellectual activity we label AI can be likened in revealing ways to each of five familiar technologies. AI is not about building artificial intelligences, nor is it about understanding the human mind or any other kind of mind.

The representation language used in one domain is seldom borrowed and adapted to another, because the facilities that were assets for one task become limitations elsewhere. For this reason, most such languages are built from scratch. The goal of the RLL effort is to reduce the amount of time expended in building a representation language, by providing a Representation Language Language, that is, a language that provided the user with the components of many representation languages, and with the ability to integrate them. RLL contains a large library of "representational pieces," for example, the mode of inheritance used by the Examples link of the Units package, or the A-Kind-Of type of slot used in the MIT Frames Representation Language, FRL. A novice user can easily design a language simply by picking an amalgamation of pieces; RLL is responsible for meshing them together into a coherent and working whole.

The Center for Automation and Intelligent Systems Research at Case Western Reserve University, founded in 1984, provides the setting and the administrative and funding mechanisms for coordinating and focusing the capabilities of faculty members and students from many disciplines and departments to deal with significant realworld problems encountered in the automation of production. The center serves as an interface between separate basic research efforts in the various disciplines and academic departments and the multidisciplinary group efforts needed to deal effectively with nontrivial real problems. The main focus of research at the center is on the effective integration of computer-based technologies that appear to be essential for the factory of the future. Thus, the scope of activities at the center is somewhat broader based than AI, but AI plays a central role in this integration because effective integration of complex systems requires intelligence. The major emphasis at the center is on industrial applications.

MIT Artificial Intelligence Laboratory The MIT AI Laboratory has a long tradition of research in most aspects of Artificial Intelligence. Currently, the major foci include computer vision, manipulation, learning, Englishlanguage understanding, VLSI design, expert engineering problem solving, commonsense reasoning, computer architecture, distributed problem solving, models of human memory, programmer apprentices, and human education. Understanding Visual Images Professor Berthold K. P. Horn and his students have studied intensively the image irradiance equation and its applications. The reflectance and albedo map representations have been introduced to make surface orientation, illumination geometry, and surface reflectivity explicit. Recent work has centered on modelling the effects of the atmosphere which distort intensity values and make classification of terrain and related computations using the albedo map inaccurate.

Over the past two years we have started a program of research into the development of VLSI systems. Professor Gerald Sussman, John Holloway, Tom Knight, Howie Shrobe, and others have developed a number of systems, including a single ship SCHEME processor. We have TTL implementations of a number of our vision algorithms, and we are in the process of switching some of them to VLSI. Expert Problem Solving and VLSI Design Traditional automated synthesis techniques for circuit design are restricted to small classes of circuit functions for which mathematical methods exist. Professor Gerald J. Sussman and his group have developed computer-aided design tools that can be of much broader assistance.

Introduction Factory design is the specification of functional requirements for a new factory or the specification of functional changes to an existing factory. Factory design is essentially initiated upon formalization of a product or set of products that must be manufactured. Once designed, the factory is subjected to a continuous cycle of redesign that is only complete when the factory has served its useful life, which can include the manufacture of products not conceived during the original design. The design of a factory and the implications of this design on the manufacture of goods typically involves millions of dollars in expenditures. Recent estimates are that 8 percent of the U.S. gross national product (GNP) can be attributed to new factory design and construction (Tompkins and White 1984).

In the past a healthcare worker would made a twice or four-times a day visit for those in care; coming in is a room equipped with sensors and cameras; elderly people wearing monitoring wristbands; the deployment of biomimetic companion robots, each sending vital signs to a data hub which is interpreted by artificial intelligence. One example of an artificial intelligence driven assisted living platform is CarePredict's Tempo, a collaborative product between the companies CarePredict and LifeWell Senior Living. Tempo is a care coordination platform designed for senior living communities, combining Artificial Intelligence and smart location technology to measure activity and behavioral patterns of seniors. Remote reporting The Tempo system allows for remote reporting to care home staff and family members, measuring important in the daily activities of people in care like sleeping patterns, toileting, movement, hydration, eating, and socialization. Through monitoring care home staff can be proactive in providing early interventions and also avoiding unnecessary'false alarms'.