Incel communities have recently attracted the public's interest mainly due to their high degree of extreme views and involvement in real-world violence. A common theme in Incel communities is self-harm discussions. Despite this, beyond small-scale qualitative analyses of self-harm discussions in Incel communities, we lack a large-scale quantitative understanding of how Incels discuss self-harm and how it differs from mainstream communities. In this work, we aim to demystify self-harm discussions in Incel communities using a data-driven approach and understand how Incels differentiate from mainstream communities. We use a dataset of 6.4M posts from 18 Incel subreddits and 2.4M posts from an Incel forum, as well as 5.8M posts from two mainstream subreddits discussing mental health. Using word embedding approaches, temporal analyses, topic modeling, and qualitative analysis, we shed light on self-harm discussions in Incel and mainstream communities and their evolution over time. We find substantial differences in the language related to self-harm deployed among the communities; we find that Incels use niche terms related to self-harm, which is not the case in mainstream communities. We observe that over time, language related to self-harm evolves considerably more among Incels than in mainstream communities. Also, we observe that negative perception of their physical appearance is the most recurrent theme in self-harm conversations for Incels, which does not feature in mainstream communities. Finally, by analyzing social factors, we find that Substance abuse is the most closely associated social factor to self-harm in Incel and mainstream communities and that Physical Appearance, over time, is becoming increasingly closely related to self-harm discussions in Incel communities.

The Space Shuttle utilizes several rocket engine systems, all of which must function with a high degree of reliability for successful mission completion. The Space Shuttle Main Engine (SSME) is by far the most complex of the rocket engine systems. In earlier spacecraft, rocket systems (and, in fact, the entire spacecraft) were designed for use on only a single mission and were discarded after use. In a major departure from earlier practices, almost all systems on the Space Shuttle, including the rocket systems, are designed to be reusable; only the external tank is discarded during each mission.

No abstract available.

An expert system, called LEADER, has been designed and implemented for automatic learning, detection, identification, verification and correction of anomalous propulsion system operations in real time. LEADER employs a set of sensors to monitor engine component performance, and to detect, identify and validate abnormalities with respect to varying engine dynamics and behavior. Two diagnostic approaches are adopted in the architecture of LEADER. In the first approach fault diagnosis is performed through learning and identifying engine behavior patterns. LEADER, utilizing this approach, generates few hypotheses about the possible abnormalities. These hypotheses are then validated based on the SSME design and functional knowledge. The second approach directs the processing of engine sensory data and performs reasoning based on the SSME design, functional knowledge, and the deep-level knowledge, i.e., the first principles (physics and mechanics) of SSME subsystems and components. This paper describes LEADER's architecture which integrates a design-based reasoning approach with neural network-based fault pattern matching techniques. The fault diagnosis results obtained through the analyses of SSME ground test data are presented and discussed.

No abstract available.

The adaptive pattern recognition theory has been programmed on the IBM 1130 computer and applied for the recognition of speech patterns initially in the form of sonograms of CVC Hindi syllables spoken in isolation. A set of thirty six Hindi syllables spoken by three male speakers was used in the learning and recognition phases of the system. Two experiments were performed. In the first, the computer was trained by using the syllables spoken by a single speaker only; in the second, training was based on syllables spoken by two speakers. In the recognition phases, all the syllables spoken by the speakers of the learning phases were correctly recognized. However, for those speakers whose syllables were not used in the learning phase, the percentage of correct recognition was 19.4% and 61.1% for the first and second experiment, respectively.

In this paper CEP functions are applied to realize nonlinearly separable switching functions. It is proved that the introduction of these functions may realize all the 22nswitching functions of n binary variables. A model of a nonlinear threshold component is proposed to realize the switching functions.