Archaeological and Metal Detection Investigations of a 4-acre Proposed Development at the Levi Jordan Plantation State Historic Site (41BO165), Brazoria, Texas

Report Title: Archaeological and Metal Detection Investigations of a 4-acre Proposed Development at the Levi Jordan Plantation State Historic Site (41BO165), Brazoria, Texas. Report Date: February 2015 Report Number: WSA Technical Report No. 2015-03 Agency: Texas Historical Commission Permit Number: Texas Antiquities Code (TAC) Permit 7083 Project Description:On behalf of the Texas Historical Commission (THC), William Self Associates, Inc. (WSA), conducted metal detecting and shovel test survey investigations of the east side of the Levi Jordan Plantation State Historic Site (41BO165), Brazoria County, Texas. The THC sponsored the current survey investigations in advance of proposed infrastructure improvements to a currently unoccupied, forested 4-acre tract on the northeast side of the property adjacent to FM 524. The surveys were conducted consistent with the requirements of the Texas Natural Resources Code Title 9, Chapter 191 (Texas Antiquities Code [TAC]) and accompanying Rules of Practice and Procedure (Texas Administrative Code, Title 13, Chapter 26), under Texas Antiquities Permit 7083. The metal detection survey included the participation experienced, volunteer metal detectorists under the guidance of the WSA archaeology team. The investigations were focused on the proposed location of six boreholes, on an approximately 0.3 mile-long (1,600 ft) by 10 foot wide utilities corridor, as well as the locations of a proposed visitor center, parking lot, maintenance complex, and walking trail. These areas were subject to metal detecting at close spacing (approximately 2-m) by three metal detectorists. This was followed immediately by a shovel test survey, with tests placed at proposed borehole locations and then judgmentally based on metal detector survey results. Thirteen negative shovel tests were excavated in support of the survey investigations. Acres Surveyed: 4 Project Number: WSA 2014-105 Project Location: Brazoria County, Texas Unevaluated Properties: 1 NRHP Eligible Properties: 0 NRHP Ineligible Properties: 0 NRHP Listed Properties: 0 Isolated Occurrences: 0 Total Project Resources: 0 Recommendations: The location of the structural remnant is recommended for avoidance by the current project. Should there be any proposed ground disturbing impacts to the feature location, WSA recommends additional archaeological investigations in the form of hand excavated test units and metal detection prospection and recovery, to determine the nature, context, and extent of the feature, and any possible association with the plantation and its important historic context as a State of Texas Historic Site. The feature was identified by the metal detectorists as a relatively large area containing a high volume of buried metal that will require extensive excavation and treatment beyond Phase I survey level recording techniques to expose and thoroughly sample. Indications are from the WSA surveyed portions of the 4-acre tract, that plantation-era artifacts are present, but in low density and widely scattered, and with the exception of the potential feature location (structure remnant), the area contains no intact, plantation-era features. Recovered artifacts are consistent with the use of majority of this area as active cropland during the primary plantation period. WSA recommends and respectfully requests THC concurrence that, except for the feature location as mapped in this report, there is a low probability that additional archaeological investigations will add to our understanding of plantation-age features or events within the investigated area. WSA recommends and respectfully requests that, except for the feature location as mapped in this report, there is little likelihood that any SAL or NRHP eligible components to Site 41BO165 will be impacted by the proposed project. WSA recommends and respectfully requests that, except for the feature location as mapped in this report, no additional archaeological investigations are warranted within the approximately 4-acre project area prior to construction, and that the remainder of the proposed project may proceed to construction with regard to the TAC, and that all TAC reporting-related consultations for the remainder of the proposed project be considered concluded and complete. All recovered artifacts will be curated at the THC Austin facility. All modern trash documented during the investigations has been discarded

Predicting students' happiness from physiology, phone, mobility, and behavioral data

In order to model students' happiness, we apply machine learning methods to data collected from undergrad students monitored over the course of one month each. The data collected include physiological signals, location, smartphone logs, and survey responses to behavioral questions. Each day, participants reported their wellbeing on measures including stress, health, and happiness. Because of the relationship between happiness and depression, modeling happiness may help us to detect individuals who are at risk of depression and guide interventions to help them. We are also interested in how behavioral factors (such as sleep and social activity) affect happiness positively and negatively. A variety of machine learning and feature selection techniques are compared, including Gaussian Mixture Models and ensemble classification. We achieve 70% classification accuracy of self-reported happiness on held-out test data.MIT Media Lab ConsortiumRobert Wood Johnson Foundation (Wellbeing Initiative)National Institutes of Health (U.S.) (Grant R01GM105018)Samsung (Firm)Natural Sciences and Engineering Research Council of Canad

Geophysical characterization of derelict coalmine workings and mineshaft detection: a case study from Shrewsbury, United Kingdom

A study site of derelict coalmine workings near Shrewsbury, United Kingdom was the focus for multi‐phase, near‐surface geophysical investigations. Investigation objectives were: 1) site characterization for remaining relict infrastructure foundations, 2) locate an abandoned coalmine shaft, 3) determine if the shaft was open, filled or partially filled and 4) determine if the shaft was capped (and if possible characterize the capping material). Phase one included a desktop study and 3D microgravity modelling of the relict coalmine shaft thought to be on site. In phase two, electrical and electromagnetic surveys to determine site resistivity and conductivity were acquired together with fluxgate gradiometry and an initial microgravity survey. Phase three targeted the phase two geophysical anomalies and acquired high‐resolution self potential and ground penetrating radar datasets. The phased‐survey approach minimised site activity and survey costs. Geophysical results were compared and interpreted to characterize the site, the microgravity models were used to validate interpretations. Relict buildings, railway track remains with associated gravel and a partially filled coalmine shaft were located. Microgravity proved optimal to locate the mineshaft with radar profiles showing ‘side‐swipe’ effects from the mineshaft that did not directly underlie survey lines. Geophysical interpretations were then verified with subsequent geotechnical intrusive investigations. Comparisons of historical map records with intrusive geotechnical site investigations show care must be taken using map data alone, as the latter mineshaft locations was found to be inaccurate

Combining Stream Mining and Neural Networks for Short Term Delay Prediction

The systems monitoring the location of public transport vehicles rely on wireless transmission. The location readings from GPS-based devices are received with some latency caused by periodical data transmission and temporal problems preventing data transmission. This negatively affects identification of delayed vehicles. The primary objective of the work is to propose short term hybrid delay prediction method. The method relies on adaptive selection of Hoeffding trees, being stream classification technique and multilayer perceptrons. In this way, the hybrid method proposed in this study provides anytime predictions and eliminates the need to collect extensive training data before any predictions can be made. Moreover, the use of neural networks increases the accuracy of the predictions compared with the use of Hoeffding trees only

Portable Gamma Spectrometry Surveys of Sites in Portugal in Support of the VADOSE Project

The VADOSE project involves the use of multiple techniques to evaluate dose rate variability on different spatial scales. Several sites in central northern Portugal, mostly in the vicinity of Aveiro, have been investigated. As part of this investigation, portable gamma spectrometry techniques were used to map areas of approximately 100x100m around each sampling location. The SUERC portable gamma spectrometry system used consists of a 3x3” NaI(Tl) spectrometer with integral GPS receiver. Measurements were conducted with 10s integration time. Maps of the dose rate variability in each area were generated in the field, and used to confirm data quality and coverage and identify any remaining locations that would benefit from further measurements prior to leaving the site. Maps of natural radionuclide distribution (40K, 214Bi from the 238U decay series, and 208Tl from the 232Th decay series) were produced after the conclusion of measurements each day. Natural radionuclide specific activities (Bq kg-1 ) were estimated using a spectral windows method with stripping1 , using a working calibration assuming planar geometry and uniform activity distribution. As agreed prior to the start of work, a working calibration derived from field measurements and photon fluence calculations conducted for similar detectors in the 1990s2 has been used here, with calibration parameters given in the appendix. This report presents the dose rate maps produced during the field work, with a very brief description of the data. Summary statistics for each data set are presented in Table 1. All data have been mapped using a UTM (zone 29T) grid, with the approximate location of ground features added by hand as a guide. Further work could be conducted to produce more accurate overlays of ground features. At each site in-situ gamma spectrometry measurements were also conducted by ITN, and the data collected by the two detector systems and the soil samples will be compared at a later date

Creation of regions for dialect features using a cellular automaton

An issue in dialect research has been how to make generalizations from survey data about where some dialect feature might be found. Pre-computational methods included drawing isoglosses or using shadings to indicate areas where an analyst expected a feature to be found. The use of computers allowed for faster plotting of locations where any given feature had been e¬licited, and also allowed for the use of statistical techniques from technical geography to estimate regions where particular features might be found. However, using the computer did not make the analysis less subjective than isoglosses, and statistical methods from technical geography have turned out to be limited in use. We have prepared a cellular automaton (CA) for use with data collected for the Linguistic Atlas Project that can address the problems involved in this type of data visualization. The CA plots the locations where survey data was elicited, and then through the application of rules creates an estimate of the spatial distributions of selected features. The application of simple rules allows the CA to create objective and reproducible estimates based on the data it was given, without the use of statistical methods

Multi-dimensional modelling for the national mapping agency: a discussion of initial ideas, considerations, and challenges

The Ordnance Survey, the National Mapping Agency (NMA) for Great Britain, has recently begun to research the possible extension of its 2-dimensional geographic information into a multi-dimensional environment. Such a move creates a number of data creation and storage issues which the NMA must consider. Many of these issues are highly relevant to all NMA’s and their customers alike, and are presented and explored here. This paper offers a discussion of initial considerations which NMA’s face in the creation of multi-dimensional datasets. Such issues include assessing which objects should be mapped in 3 dimensions by a National Mapping Agency, what should be sensibly represented dynamically, and whether resolution of multi-dimensional models should change over space. This paper also offers some preliminary suggestions for the optimal creation method for any future enhanced national height model for the Ordnance Survey. This discussion includes examples of problem areas and issues in both the extraction of 3-D data and in the topological reconstruction of such. 3-D feature extraction is not a new problem. However, the degree of automation which may be achieved and the suitability of current techniques for NMA’s remains a largely unchartered research area, which this research aims to tackle. The issues presented in this paper require immediate research, and if solved adequately would mark a cartographic paradigm shift in the communication of geographic information – and could signify the beginning of the way in which NMA’s both present and interact with their customers in the future

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig