We report on the effects of cosmic ray interactions with the kinetic inductance detector (KID)-based focal plane array for the terahertz intensity mapper (TIM). TIM is a NASA-funded balloon-borne experiment designed to probe the peak of the star formation in the Universe. It employs two spectroscopic bands, each equipped with a focal plane of four ∼ 900-pixel, KID-based array chips. Measurements of an 864-pixel TIM array show 791 resonators in a 0.5 GHz bandwidth. We discuss challenges with resonator calibration caused by this high multiplexing density. We robustly identify the physical positions of 788 (99.6 events/min/cm^2 in our array. 66 < 5 KIDs per event spread over a 0.66 cm^2 region (2 pixel pitches in radius). We observe a total cosmic ray dead fraction of 0.0011 ∼ 0.124

Apparent and latent knowledge claims made by social media authors are uncovered via Natural Language Processing tools and techniques. This phenomenon presents several fundamental issues as researchers distinguish correlation factors between an author's endogenous self-described and self-validated data disclosure and exogenous relationships. While social media processing protocols produce near-instantaneous analysis of a social dataset, studies examining such tools often overlook the strength of the correlation relationship. In addition, they frequently neglect to report an assessment of the normality of data distribution, disregard the rationale for choosing Pearson's or Spearman's test of correlations, and sometimes even use contrasting interpretations of correlation coefficients. This study proffers a correlation study research approach via direct inquiry that strengthens the theoretical foundation for identifying the relationship among social media emotion, sentiment, and cognition across diverse locations concerning real-world events. It attempts to answer the main research question: Does any relationship exist between social media's sentiment and cognition variables? It constructs a robust research-based analysis to validate the main question and fill the gap that other projects fail to address. Furthermore, it presents and discusses methods for investigating and interpreting correlation matrices and scatterplots. Moreover, it answers the research question and attempts to prove spatial relationships among variables. Last, it guides and describes future work of a predictive artifact that will input, process, and visualize a spatiotemporal, NLP processed, social media dataset and its integration with Pearson's and Spearman's correlations, and visual data constructs.

The Terahertz Intensity Mapper (TIM) is a balloon-borne far-infrared imaging spectrometer designed to characterize the star formation history of the universe. In its Antarctic science flight, TIM will map the redshifted 158um line of ionized carbon over the redshift range 0.5-1.7 (lookback times of 5-10 Gyr). TIM will spectroscopically detect ~100 galaxies, determine the star formation rate history over this time interval through line intensity mapping, and measure the stacked CII emission from galaxies in its well-studied target fields (GOODS-S, SPT Deep Field). TIM consists of a 2-meter telescope feeding two grating spectrometers that that cover 240-420um at R~250 across a 1.3deg field of view, detected with 7200 kinetic inductance detectors and sampled through a novel RF system-on-chip readout. TIM will serve as an important scientific instrument, accessing wavelengths that cannot easily be studied from the ground, and as a testbed for future FIR space technology.

Due to the massive competition, the business environment is getting dynamic and as well as complicated. To win this competition, a successful business needs to develop strategic decisions by exploring all the available information. To this end, competitive intelligence is one of the appropriate tools to reach this goal. In this paper, we proposed an artifact for strategic decision-making by telecommunication firms. A detailed comparison is conducted among the firms and geographical areas in the United States. The study includes Spectrum (Charter Communications), Verizon Communications Inc, Xfinity (Comcast Corporation), and Cox Communications. We conduct qualitative and quantitative approaches to evaluate the proposed artifact. The findings showed positive results towards using the artifact and it exhibits the potential to be effective with respect to business decision-making in the telecommunication industry. Compared to the benchmark data, the achieved results shows that the participants experienced in the proposed artifact had an excellent experience with respect to the stimulation’s novelty.

With social media a de facto global communication channel used to disseminate news, entertainment, and one's self-revelations, the latter contains double-talk, peculiar insight, and contextual observation about real-world events. The primary objective is to propose a novel pipeline to classify a tweet as either "useful" or "not useful" by using widely-accepted Natural Language Processing (NLP) techniques, and measure the effect of such method based on the change in performance of a Geographical Information System (GIS) artifact. A 1,000 tweet sample is manually tagged and compared to an innovative social media grammar applied by a rule-based social media NLP pipeline. Evaluation underpins answering, prior to content analysis of a tweet, does a method exist to support identifying a tweet as "useful" for subsequent processing? Indeed, "useful" tweet identification via NLP returned precision of 0.9256, recall of 0.6590, and F-measure of 0.7699; consequently GIS social media processing increased 0.2194 over baseline.

Social media is a desirable Big Data source used to examine the relationship between crime and social behavior. Observation of this connection is enriched within a geographic information system (GIS) rooted in environmental criminology theory, and produces several different results to substantiate such a claim. This paper presents the construction and implementation of a GIS artifact producing visualization and statistical outcomes to develop evidence that supports predictive crime analysis. An information system research prototype guides inquiry and uses crime as the dependent variable and a social media tweet corpus, operationalized via natural language processing, as the independent variable. This inescapable realization of social media as a predictive crime variable is prudent; researchers and practitioners will better appreciate its capability. Inclusive visual and statistical results are novel, represent state-of-the-art predictive analysis, increase the baseline R value by 7.26%, and support future predictive crime-based research when frontrun with real-time social media.

Whereas ad hoc single domain Big Data inquiry is successful, observation of a multi-domain GIS artifact needs consideration. A GIS solution for multi-domain data analysis must provide visualization and overt statistical analysis tools, e.g., regression capabilities of constituent data streams, in order to enable largescale dataset processing and evaluation. Such guidelines direct inquiry and creation of a robust GIS artifact considering a social media tweet corpus and a domain specific crime dataset. The tweet corpus is operationalized via natural language processing treatments and used in GIS artifact construction and evaluation. Although results are not statistically significant and visualizing crime data is not novel, learning how to combine the two in predictive ways via GIS is. As such, extensions and possible future work support social media natural language processing techniques and Big Data processing for predictive crime-based incident interactions as front-run by real-time social media analysis.

Identifying the type of the place a user is tweeting from is important for many business and social applications, e.g., user profiles can help local businesses identify current and potential clients and their interests. We used Random Forest to identify six location categories. They are active life, eating out, hotels, nightlife, shopping, and shows. We evaluated 16 features for use in classification. The features are generated from the textual contents in the tweet, the metadata associated with the tweet, and the geographical area the user is tweeting from. We trained our classifier by analyzing 43,149 reviews from Yelp and by examining two twitter datasets. The first is an original dataset consisting of 6,359 tweets and the second is a stratified one containing 2,400 tweets uniformly distributed between the six categories. We evaluated our approach by creating a gold standard. Using 60% of our tweets for training and 40% for testing, our approach classified 74% of tweets in the original dataset, and 77% of tweets in the stratified dataset, correctly with the right location category. The results could be beneficial for research and business.