Algorithmic evaluation of procedurally generated content struggles to find metrics that align with human experience, particularly for composite artefacts. Automatic decomposition as a possible solution requires concepts that meet a range of properties. To this end, drawing on Games Studies and Game AI research, we introduce the nested concepts of \textit{Landmarks}, \textit{Monuments}, and \textit{Beacons}. These concepts are based on the artefact's perceivability, evocativeness, and Call to Action, all from a player-centric perspective. These terms are generic to games and usable across genres. We argue that these entities can be found and evaluated with techniques currently used in both research and industry, opening a path towards a fully automated decomposition of PCG, and evaluation of the salient sub-components. Although the work presented here emphasises mixed-initiative PCG and compositional PCG, we believe it applies beyond those domains. With this approach, we intend to create a connection between humanities and technical game research and allow for better computational PCG evaluation

Rep. Chrissy Houlahan, D-Pa., said after the House Intelligence Committee's classified briefing on New Jersey drones, she's not concerned about any threat and it's irresponsible to scare the public. Drones spotted flying over Marine Corps Base Camp Pendleton in Southern California over the past week posed no threat to operations at the installation, according to reports. James C. Sartain told The Warzone that between Dec. 9 and 15, "there were six instances of unmanned aerial systems (UAS)" seen entering the airspace over Camp Pendleton. Sartain also said the UAS did not pose any threat to installation operations. The publication also learned from base personnel that countermeasures to take out the drones were not necessary as air and ground operations were not impacted.

According to [2], 11.6% of men will develop prostate cancer in their lifetime, with approximately a 20% death rate in the United States. Radical prostatectomy is a popular surgical approach to treat PCa by removing the entire prostate gland since 1905 [3,4]. In clinical practice, the traditional open radical prostatectomy (ORP) has almost been replaced by laparoscopic radical prostatectomy (RLP) [5]. As a minimally invasive surgical procedure for PCa, RLP significantly reduces blood loss, hospitalization duration, and postoperative complications [6]. However, the long learning curve associated with laparoscopic procedures limits the application of RLP [7]. Robot-assisted laparoscopic prostatectomy (RALP) has been demonstrated [5] to shorten this learning curve by leveraging the wristed instruments and the 3-D endoscopic camera of the telerobotic surgical system, usually the da Vinci surgical system, to achieve intuitive operation [8]. However, the endoscopic camera cannot localize the prostate lesions nor visualize the sub-surface anatomy of the prostate gland. Therefore, a complementary medical imaging modality is necessary to facilitate RALP.

Gen. Mark Milley tells graduates of the US Military Academy to prepare West Point military academy graduates to prepare for increasingly dangerous world. Gen. Mark Milley told cadets graduating from U.S. Military Academy West Point Saturday to be prepared for increasing risk of global conflict and a host of new weapons technologies in their careers. "The world you are being commissioned into has the potential for a significant international conflict between great powers. And that potential is increasing, not decreasing," Milley, the chairman of the Joint Chiefs of Staff, told the cadets at the 2022 commencement ceremony in West Point, New York. "And right now, at this very moment, a fundamental change is happening in the very character of war. We are facing right now two global powers, China and Russia, each with significant military capabilities, and both who fully intend to change the current rules based order," Milley said.

Dysarthria is a condition which hampers the ability of an individual to control the muscles that play a major role in speech delivery. The loss of fine control over muscles that assist the movement of lips, vocal chords, tongue and diaphragm results in abnormal speech delivery. One can assess the severity level of dysarthria by analyzing the intelligibility of speech spoken by an individual. Continuous intelligibility assessment helps speech language pathologists not only study the impact of medication but also allows them to plan personalized therapy. It helps the clinicians immensely if the intelligibility assessment system is reliable, automatic, simple for (a) patients to undergo and (b) clinicians to interpret. Lack of availability of dysarthric data has resulted in development of speaker dependent automatic intelligibility assessment systems which requires patients to speak a large number of utterances. In this paper, we propose (a) a cost minimization procedure to select an optimal (small) number of utterances that need to be spoken by the dysarthric patient, (b) four different speaker independent intelligibility assessment systems which require the patient to speak a small number of words, and (c) the assessment score is close to the perceptual score that the Speech Language Pathologist (SLP) can relate to. The need for small number of utterances to be spoken by the patient and the score being relatable to the SLP benefits both the dysarthric patient and the clinician from usability perspective.

Episode 63 of Landscape Digital Show reveals how data science can help discover and interpret customer insights for running a smarter business. I'll introduce terminology associated with data science that that may be new to you. This will help you to intelligently research and study this important topic further. And I'll give you an example that will show how to identify and analyze data points to run a smarter business. It turns out that I've just returned from a conference where I had the opportunity to get a first-hand take on this subject from Ginni Rometty, the CEO of IBM This event was a celebration of IBM's top performers and significant others from around the world.

We have been conducting research on bottom-up classification techniques ba;ed on trainable artificial neural networks (ANNs), in combination with comprehensive but weakly-applied language models. To focus our work on a subproblem that is tractable enough to le.:'ld to usable products in a reasonable time, we have restricted the domain to hand-printing, so that strokes are clearly delineated by pen lifts. In the process of optimizing overall performance of the recognizer, we have discovered some useful techniques for architecting and training ANNs that must participate in a larger recognition process. Some of these techniques-especially the normalization of output error, frequency balanCing, and error emphal;is-suggest a common theme of significant value derived by reducing the effect of a priori biases in training data to better represent low frequency, low probability smnples, including second and third choice probabilities. There is mnple prior work in combining low-level classifiers with various search strategies to provide integrated segmentation and recognition for writing (Tappert et al 1990) and speech (Renals et aI1992). And there is a rich background in the use of ANNs a-; classifiers, including their use as a low-level, character classifier in a higher-level word recognition system (Bengio et aI1995).

We have been conducting research on bottom-up classification techniques ba;ed on trainable artificial neural networks (ANNs), in combination with comprehensive but weakly-applied language models. To focus our work on a subproblem that is tractable enough to le.:'ld to usable products in a reasonable time, we have restricted the domain to hand-printing, so that strokes are clearly delineated by pen lifts. In the process of optimizing overall performance of the recognizer, we have discovered some useful techniques for architecting and training ANNs that must participate in a larger recognition process. Some of these techniques-especially the normalization of output error, frequency balanCing, and error emphal;is-suggest a common theme of significant value derived by reducing the effect of a priori biases in training data to better represent low frequency, low probability smnples, including second and third choice probabilities. There is mnple prior work in combining low-level classifiers with various search strategies to provide integrated segmentation and recognition for writing (Tappert et al 1990) and speech (Renals et aI1992). And there is a rich background in the use of ANNs a-; classifiers, including their use as a low-level, character classifier in a higher-level word recognition system (Bengio et aI1995).

We have combined an artificial neural network (ANN) character classifier with context-driven search over character segmentation, word segmentation, and word recognition hypotheses to provide robust recognition of hand-printed English text in new models of Apple Computer's Newton MessagePad. We present some innovations in the training and use of ANNs al; character classifiers for word recognition, including normalized output error, frequency balancing, error emphasis, negative training, and stroke warping. A recurring theme of reducing a priori biases emerges and is discussed.