The dataset contains tens of thousands of 64x64 pixel images of cells. Each image shows cells labelled with a fluorescent protein. In total, seven different fluorescent proteins were employed. The authors consider the image analysis task of classifying images w.r.t.

In astronomical surveys, such as the Zwicky Transient Facility, supernovae (SNe) are relatively uncommon objects compared to other classes of variable events. Along with this scarcity, the processing of multi-band light-curves is a challenging task due to the highly irregular cadence, long time gaps, missing-values, few observations, etc. These issues are particularly detrimental to the analysis of transient events: SN-like light-curves. We offer three main contributions: 1) Based on temporal modulation and attention mechanisms, we propose a Deep attention model (TimeModAttn) to classify multi-band light-curves of different SN types, avoiding photometric or hand-crafted feature computations, missing-value assumptions, and explicit imputation/interpolation methods. 2) We propose a model for the synthetic generation of SN multi-band light-curves based on the Supernova Parametric Model, allowing us to increase the number of samples and the diversity of cadence. Thus, the TimeModAttn model is first pre-trained using synthetic light-curves. Then, a fine-tuning process is performed. The TimeModAttn model outperformed other Deep Learning models, based on Recurrent Neural Networks, in two scenarios: late-classification and early-classification. Also, the TimeModAttn model outperformed a Balanced Random Forest (BRF) classifier (trained with real data), increasing the balanced-$F_1$score from $\approx.525$ to $\approx.596$. When training the BRF with synthetic data, this model achieved similar performance to the TimeModAttn model proposed while still maintaining extra advantages. 3) We conducted interpretability experiments. High attention scores were obtained for observations earlier than and close to the SN brightness peaks. This also correlated with an early highly variability of the learned temporal modulation.

From an AI perspective, finding effective treatments for cancer is a high-dimensional search problem characterized by many molecularly distinct cancer subtypes, many potential targets and drug combinations, and a dearth of highquality data to connect molecular subtypes and treatments to responses. The broadening availability of molecular diagnostics and electronic medical records presents both opportunities and challenges to apply AI techniques to personalize and improve cancer treatment. We discuss these in the context of Cancer Commons, a "rapid learning" community where patients, physicians, and researchers collect and analyze the molecular and clinical data from every cancer patient and use these results to individualize therapies. Research opportunities include adaptively planning and executing individual treatment experiments across the whole patient population, inferring the causal mechanisms of tumors, predicting drug response in individuals, and generalizing these findings to new cases. The goal is to treat each patient in accord with the best available knowledge and to continually update that knowledge to benefit subsequent patients.

A numeric representation of uncertain and incomplete sensor knowledge called certainty grids was used successfully in several recent mobile robot control programs developed at the Carnegie-Mellon University Mobile Robot Laboratory (MRL) Certainty grids have proven to be a powerful and efficient unifying solution for sensor fusion, motion planning, landmark identification, and many other central problems MRL had good early success with ad hoc formulas for updating grid cells with new information. A new Bayesian statistical foundation for the operations promises further improvement MRL proposes to build a software framework running on processors onboard the new Uranus mobile robot that will maintain a probabilistic, geometric map of the robot's surroundings as it moves, The certainty grid representation will allow this map to be incrementally updated in a uniform way based on information coming from various sources, including sonar, stereo vision, proximity, and contact sensors The approach can correctly model the fuzziness of each reading and, at the same time, combine multiple measurements to produce sharper map features; it can also deal correctly with uncertainties in the robot's motion The map will be used by planning programs to choose clear paths, The certainty grid representation can be extended in the time dimension and used to detect and track moving objects Even the simplest versions of the idea allow us to fairly straightforwardly program the robot for tasks that have hitherto been out of reach MRL looks forward to a program that can explore a region and return to its starting place, using map "snapshots" from its outbound journey to find its way back, even in the presence of disturbances Objects have been modeled by polygons and polyhedra or bounded by curved surfaces. Free space has been partitioned into Vornoi regions or, heuristically, free corridors. Traditionally, the models have been hard edged; positional uncertainty, if considered at all, was used in just a few special places in the algorithms, expressed as a Gaussian spread. Partly, this oversimplification of uncertainty information is the result of analytic difficulty in manipulating interacting uncertainties, especially if the distributions are not Gaussian.

The Connection Machine introduces a new type of parallel computer which may lead to radically new ways of thinking about computing, particularly in Artificial Intelligence. The author, Daniel Hillis, is the designer of the machine and the founder of Thinking Machines Corporation, a company committed to building "Connection Machines." Hillis' book describes the Connection Machine and the issues surrounding its design. The Connection Machine is like an active memory made up of thousands, potentially millions, of small, simple, processors working simultaneously, each with its own tiny memory. This memory is controlled by a conventional, sequential, host computer.

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.

Gene-expression microarrays, commonly called gene chips, make it possible to simultaneously measure the rate at which a cell or tissue is expressing--translating into a protein--each of its thousands of genes. One can use these comprehensive snapshots of biological activity to infer regulatory pathways in cells; identify novel targets for drug design; and improve the diagnosis, prognosis, and treatment planning for those suffering from disease. However, the amount of data this new technology produces is more than one can manually analyze. Hence, the need for automated analysis of microarray data offers an opportunity for machine learning to have a significant impact on biology and medicine. This article describes microarray technology, the data it produces, and the types of machine learning tasks that naturally arise with these data.

Incremental search reuses information from previous searches to find solutions to a series of similar search problems potentially faster than is possible by solving each search problem from scratch. This is important because many AI systems have to adapt their plans continuously to changes in (their knowledge of) the world. AI has developed several ways of speeding up searches by trading off the search time and the cost of the resulting path, which includes using inadmissible heuristics (Pohl 1973, 1970) and search with limited look ahead (Korf 1990; Ishida and Korf 1991; Koenig 2001), which is also called real-time or agent-centered search. In this article, we discuss a different way of speeding up searches, namely, incremental search. Incremental search is a search technique for continual planning (or, synonymously, replanning, plan reuse, and lifelong planning) that reuses information from previous searches to find solutions to a series of similar search problems potentially faster than is possible by solving each search problem from scratch.

A general game playing system is one that can accept a formal description of a game and play the game effectively without human intervention. Unlike specialized game players, such as Deep Blue, general game players do not rely on algorithms designed in advance for specific games; and, unlike Deep Blue, they are able to play different kinds of games. In order to promote work in this area, the AAAI is sponsoring an open competition at this summer's Twentieth National Conference on Artificial Intelligence. This article is an overview of the technical issues and logistics associated with this summer's competition, as well as the relevance of general game playing to the long range-goals of artificial intelligence. We can exercise and improve our intellectual skills by playing such games.

Vast information from the neurosciences may enable bottom-up understanding of human intelligence; that is, derivation of function from mechanism. This article describes such a research program: simulation and analysis of the circuits of the brain has led to derivation of a detailed set of elemental and composed operations emerging from individual and combined circuits. The specific hypothesis is forwarded that these operations constitute the "instruction set" of the brain, that is, the basic mental operations from which all complex behavioral and cognitive abilities are constructed, establishing a unified formalism for description of human faculties ranging from perception and learning to reasoning and language, and representing a novel and potentially fruitful research path for the construction of human-level intelligence. Attempts to construct intelligent systems are strongly impeded by the lack of formal specifications of natural intelligence, which is defined solely in terms of observed and measured human (or animal) abilities, so candidate computational descriptions of human-level intelligence are necessarily underconstrained. This simple fact underlies Turing's proposed test for intelligence: lacking any specification to test against, the sole measures at that time were empirical observations of behavior, even though such behaviors may be fitted by multiple different hypotheses and simulated by many different proposed architectures.