† People involved in the organization of the challenge. ‡ People contributing data from their institutions.§ Equal senior authors.

Background: Processing stimuli in one sensory modality is known to result in suppression of other sensory-specific cortices. Additionally, behavioral experiments suggest that the primary consequence of paying attention to a specific sensory modality is poorer task performance in the unattended sensory modality. This study was designed to determine how focusing attention on the auditory or visual modality impacts neural activity in cortical regions responsible for processing stimuli in the unattended modality.

I ntracranial hemorrhage is a potentially life-threatening problem that has many direct and indirect causes. Accuracy in diagnosing the presence and type of intracranial hemorrhage is a critical part of effective treatment. Diagnosis is often an urgent procedure requiring review of medical images by highly trained specialists and sometimes necessitating confirmation through clinical history, vital signs, and laboratory examinations. The process is complicated and requires immediate identification for optimal treatment.Intracranial hemorrhage is a relatively common condition that has many causes, including trauma, stroke, aneurysm, vascular malformation, high blood pressure, illicit drugs, and blood clotting disorders (1). Neurologic consequences can vary extensively from headache to death depending upon the size, type, and location of the hemorrhage. The role of the radiologist is to detect the hemorrhage, characterize the type and cause of the hemorrhage, and to determine if the hemorrhage could be jeopardizing critical areas of the brain that might require immediate surgery.While all acute hemorrhages appear attenuated on CT images, the primary imaging features that help radiologists determine the cause of hemorrhage are the location, shape, and proximity to other structures. Intraparenchymal hemorrhage is blood that is located completely within the brain itself. Intraventricular or subarachnoid hemorrhage is blood that has leaked into the spaces of the brain that normally contain cerebrospinal fluid (the ventricles or subarachnoid cisterns, respectively). Extra-axial hemorrhage is blood that collects in the tissue coverings that surround the brain (eg, subdural or epidural subtypes). It is important to note that patients may exhibit more than one type of cerebral hemorrhage, which may appear on the same image or imaging study. Although small hemorrhages are typically less morbid than large hemorrhages, even a small hemorrhage can lead to death if it is in a critical location. Small hemorrhages also may herald future hemorrhages that could be fatal (eg, ruptured cerebral aneurysm). The presence or absence of hemorrhage may guide specific treatments (eg, stroke).Detection of cerebral hemorrhage with brain CT is a popular clinical use case for machine learning (2-5). Many of these early successful investigations were based upon relatively small datasets (hundreds of examinations) from single institutions. Chilamkurthy et al created a diverse brain CT dataset that was selected from 20 geographically distinct centers in India (more than 21 000 unique examinations). This was used to create smaller randomly selected subsets for validation and testing on common acute brain abnormalities (6). The ability for machine learning algorithms to generalize to "real-world" clinical imaging data from disparate institutions is paramount to successful use in the clinical environment.The intent for this challenge was to provide a large multiinstitutional and multinational dataset to help develop machine learning algorithms that ca...

Despite the ubiquitous use of instructional videos in both formal and informal learning settings, questions remain largely unanswered on how to design and develop video lessons that are often used as the primary method for delivering instruction in online courses. In this study, we experimented with a model of seven principles drawn from instructional design theories for designing and developing video lessons for an online graduate course. Feedback was collected from students through surveys on their perceptions of the effectiveness of the video lessons and the overall course quality for eight semesters. This paper shares the instructors’ experience on the design and development of the video lessons as well as the survey findings. Implications of the findings for instructional design and future research are also discussed.

This survey will look at SymPy, a free and open source computer algebra system started in 2005 by the second author (O.Č.). It is written entirely in Python, available from http://sympy.org. SymPy is licensed under the "modified BSD" license, as is its beautiful logo designed by Fredrik Johansson.

In this note, a class of error-correcting codes is associated to a toric variety associated to a fan defined over a finite field F q , analogous to the class of Goppa codes associated to a curve. For such a "toric code" satisfying certain additional conditions, we present an efficient list decoding algorithm for the dual code. Many examples are given. For small q, many of these codes have parameters beating the GilbertVarshamov bound. In fact, using toric codes, we construct a (n, k, d) = (49, 11, 28) code over F 8 , which is better than any other known code listed in Brouwer's tables [B] for that n and k. We give upper and conjectural lower bounds on the minimum distance. The upper bounds are known to be sharp in some cases. We conclude with a discussion of some decoding methods.

In fall 2014, we launched a foundational course in artificial intelligence (CS7637: Knowledge-Based AI) as part of the Georgia Institute of Technology's Online Master of Science in Computer Science program. We incorporated principles and practices from the cognitive and learning sciences into the development of the online AI course. We also integrated AI techniques into the instruction of the course, including embedding 100 highly focused intelligent tutoring agents in the video lessons. By now, more than 2000 students have taken the course. Evaluations have indicated that OMSCS students enjoy the course compared to traditional courses, and more importantly, that online students have matched residential students' performance on the same assessments. In this article, we present the design, delivery, and evaluation of the course, focusing on the use of AI for teaching AI. We also discuss lessons we learned for scaling the teaching and learning of AI.

No abstract