An Investigation of Broadband Current Preamplification for Obtaining Simultaneous High-Resolution Energy and Time Information from Nuclear Radiation Detectors

In beginning the investigation of low noise current preamplification, noise-performance limitations of existing broadband current-amplifying stages are considered. Dominant noise sources of the general, shuntfeedback amplifier stage having both bipolar and field-effect transistor input devices are discussed. This discussion includes the reasons why optimum noise performance from this amplifier stage requires unavoidable signal integration. The integrating shunt-feedback configuration is commonly known as the charge-sensitive preamplifier. Criteria are developed for differentiating the output voltage pulse of the charge-sensitive preamplifier without degrading the signal-to-noise ratio. Subsequently, a new broadband, shunt-feedback amplifier is described having a current gain equal to the ratio of an RC feedback impedance to an RC load impedance. The value of the feedback resistance and capacitance can be made equal to that of conventional charge-sensitive preamplifiers. Basically, the configuration is similar to that of a charge-sensitive preamplifier, since a charge-proportional signal is present within the feedback network. However, differentiation is performed by the feedback network to allow a broadband current transfer function. The stage has a large bandwidth capability with linearity and noise performance comparable to that of the conventional charge-sensitive configuration. Equations predicting the bandwidth, input and output impedances, and noise-performance are derived. Also criteria are established for achieving the desired linearity and for cascading stages to achieve large current gains To facilitate experiments involving linear gating of the amplified detector current pulse, a review of related technology is presented and a simple Rte filter for use in a gated system is discussed. A three-stage preamplifier having a current gain of 8000 was constructed to experimentally verify the predicted preamplifier performance characteristics. Energy resolution experiments performed with a Ge(Li) semiconductor detector yielded noise line widths as low as 2.35 key FWHM for the integrated current output shaped by a 1.6 microsecond time constant RC-RC filter. The noise line width measured for the gated current pulse shaped by the RLC filter was 2.70 kev FWHM compared to 2.76 key FWHM obtained, without gating, from a 0.4 microsecond RC-RC filter having the same center frequency as the RLC filter. The preamplifier was not optimized for minimum noise line width. Leading edge timing experiments were performed with a 60Co gammaray source using Naton 136 as the detector for the standard timing channel. With the test channel consisting of the three-stage preamplifier coupled to a 1.7 cc. planar diode, a timing uncertainty of 1.6 x 10-9 seconds FWHM for a 13.3 to 1 dynamic energy range was measured. With a 34.1 cc true coacial Ge(Li) detector in the test channel, the timing uncertainty was 3.3 x 10-9 seconds FWHM for a 7.8 to 1 dynamic energy range. Rise times as low as 7 x 10-9 seconds were measured with the 9 x 10-12 farad planar detector connected to the preamplifier

A scoping investigation of eye-tracking in Electronic Gambling Machine (EGM) play

The nature of the association between Electronic Gambling Machines (EGMs) and gambling problems remains uncertain. Eye-tracking offers a potentially powerful method to understand how individuals attend to the visual displays and features of machine games as a function of machine experience, use of other commercial gambling products, the degree to which some game features capture players attention and, critically, vulnerability to problematic patterns of machine play. Characterizing machine players' attention to machine games may aid the design of harm-minimization measures such as, but not limited to, pop-up messages and visible clocks; and provide an important ancillary measure for testing their efficacy. Here, we conducted the first study to use eye-tracking to improve our understanding of how machine players attend to EGM displays in local bookmaker offices (LBOs) situated across North West England, as well as North East and North Wales. Through liaison with 4 bookmaker operators, we recruited a sample of 118 LBO customers who, first, completed a small number of questionnaires about their gambling history and other gambling activities and, then, completed a typical machine gambling session with their own money while wearing eye-tracking glasses to capture eye-movement pattern. The protocol captured regions of gaze fixation while playing (B2) roulette or (B3) slots on B category machines (Gambling Commission, 2012). The final dataset consisted of 91 eye-tracking recordings: 59 games of roulette and 57 slots games. Our principle dependent measure was the percentage of fixations of visual features and machine display locations (as areas of interest; AOIs) as an objective indicator of overt visual attention and their importance to machine players. Our data analysis included statistical correction for differences in the relative size (display area) and display duration across AOIs. To summarise, our main findings are as follows: In roulette, 56.3% of LBO machine players fixations were distributed over the chip-placement area while placing bets (in the stationary states of the game), rising to 75.1% while the roulette wheel Eye-tracking & machines Rogers & Leek; main text v2; 24th March 2017 3 spun in the moving states of the games). Machine players looked at their credit balance 7.2% of the time while placing bets, only slightly more frequently than the previous winning number at 6.8%. In slots games, the slot-reels dominated machine players' visual attention: accounting for 53.6% of fixations while placing bets, rising to 91.7% while spinning. Players' fixations of their credit-balances amounted to 14% of the total while placing bets but only 5.1% while the slot-reels spun. Fixations away from the machine were more frequent while placing bets in both roulette and slots games: 13.5% and 13.4% respectively, dropping to 2.4% and 1.1% while the wheel/slot-reels spun. Players' age and years of education were only weakly related to fixation patterns while playing roulette or slots games. Unemployed players allocated fewer fixations over the chip-placement area (both while the roulette wheel was stationary and while it spun) and over the slot-reels while placing bets; they also tended to look away from the machine more while placing bets in roulette. Frequent machine players tended to look at the roulette wheel less frequently than infrequent users while playing roulette games; involvement in other forms of gambling tended to increase attention towards credit balance but was not otherwise linked to particular patterns of fixations. Finally, problems gamblers allocated fewer fixations to the roulette wheel while placing bets and while it spun compared to non-problem gamblers; and tended to look away from the machine more frequently; in slots games, problem gamblers looked more frequently at amount-won messages. These data describe, for the first time, the distributions of machine players overt attention while navigating roulette and slots games in a commercial settings. In general, fixation counts showed the least variability for moving visual features and events that are likely to capture attention automatically, such as spinning Eye-tracking & machines Rogers & Leek; main text v2; 24th March 2017 4 roulette wheels or spinning slot-reels. However, fixations were also concentrated upon visual features and elements with relatively less attentional capture such as the chip-placement-area while placing bets in roulette and looking at credit balances in both games. Associations between patterns of fixation and both frequency of past-month expenditure on B category machines and broader gambling involvement were modest, suggesting that most of the variability in eye-movements and fixations reflects players navigation through the sequenced behaviours of placing bets and monitoring spinning roulette wheels or slot-reels in anticipation of game outcomes. Players with extensive machine experience tended to discount slightly the roulette wheel as a visual feature; while players with broader patterns of gambling activity looked at credit balances frequently, suggesting that such individuals are mindful of available credits. Players with gambling problems allocated fewer fixations over the chip-placement-area while placing bets and while watching the wheel spun compared to non-problem gamblers, suggesting that placing of bets can be accomplished with less attentional focus. These individuals were also more likely to look away from the machine altogether, suggesting that, in roulette play, gambling problems might be associated with a loosened attentional focus to events elsewhere in the shop. However, in slots games, problems gamblers' attended to the reward signals of previous games (amount-won) when placing the next bets. So far as we are aware, these data are the first to show that eye-tracking methodology has some potential to offer insights into machine-player interactions, and to provide a bias-free measure of individual differences in attention to games visual features and events as a function of their experience with gambling machines, gambling background and vulnerability to gambling-related harms. This study offers a methodology for studying and optimizing the timing, placement and content of harm-minimization messaging

A scoping investigation of eye-tracking in Electronic Gambling Machine (EGM) play

The nature of the association between Electronic Gambling Machines (EGMs) and gambling problems remains uncertain. Eye-tracking offers a potentially powerful method to understand how individuals attend to the visual displays and features of machine games as a function of machine experience, use of other commercial gambling products, the degree to which some game features capture players attention and, critically, vulnerability to problematic patterns of machine play. Characterizing machine players' attention to machine games may aid the design of harm-minimization measures such as, but not limited to, pop-up messages and visible clocks; and provide an important ancillary measure for testing their efficacy. Here, we conducted the first study to use eye-tracking to improve our understanding of how machine players attend to EGM displays in local bookmaker offices (LBOs) situated across North West England, as well as North East and North Wales. Through liaison with 4 bookmaker operators, we recruited a sample of 118 LBO customers who, first, completed a small number of questionnaires about their gambling history and other gambling activities and, then, completed a typical machine gambling session with their own money while wearing eye-tracking glasses to capture eye-movement pattern. The protocol captured regions of gaze fixation while playing (B2) roulette or (B3) slots on B category machines (Gambling Commission, 2012). The final dataset consisted of 91 eye-tracking recordings: 59 games of roulette and 57 slots games. Our principle dependent measure was the percentage of fixations of visual features and machine display locations (as areas of interest; AOIs) as an objective indicator of overt visual attention and their importance to machine players. Our data analysis included statistical correction for differences in the relative size (display area) and display duration across AOIs. To summarise, our main findings are as follows: In roulette, 56.3% of LBO machine players fixations were distributed over the chip-placement area while placing bets (in the stationary states of the game), rising to 75.1% while the roulette wheel Eye-tracking & machines Rogers & Leek; main text v2; 24th March 2017 3 spun in the moving states of the games). Machine players looked at their credit balance 7.2% of the time while placing bets, only slightly more frequently than the previous winning number at 6.8%. In slots games, the slot-reels dominated machine players' visual attention: accounting for 53.6% of fixations while placing bets, rising to 91.7% while spinning. Players' fixations of their credit-balances amounted to 14% of the total while placing bets but only 5.1% while the slot-reels spun. Fixations away from the machine were more frequent while placing bets in both roulette and slots games: 13.5% and 13.4% respectively, dropping to 2.4% and 1.1% while the wheel/slot-reels spun. Players' age and years of education were only weakly related to fixation patterns while playing roulette or slots games. Unemployed players allocated fewer fixations over the chip-placement area (both while the roulette wheel was stationary and while it spun) and over the slot-reels while placing bets; they also tended to look away from the machine more while placing bets in roulette. Frequent machine players tended to look at the roulette wheel less frequently than infrequent users while playing roulette games; involvement in other forms of gambling tended to increase attention towards credit balance but was not otherwise linked to particular patterns of fixations. Finally, problems gamblers allocated fewer fixations to the roulette wheel while placing bets and while it spun compared to non-problem gamblers; and tended to look away from the machine more frequently; in slots games, problem gamblers looked more frequently at amount-won messages. These data describe, for the first time, the distributions of machine players overt attention while navigating roulette and slots games in a commercial settings. In general, fixation counts showed the least variability for moving visual features and events that are likely to capture attention automatically, such as spinning Eye-tracking & machines Rogers & Leek; main text v2; 24th March 2017 4 roulette wheels or spinning slot-reels. However, fixations were also concentrated upon visual features and elements with relatively less attentional capture such as the chip-placement-area while placing bets in roulette and looking at credit balances in both games. Associations between patterns of fixation and both frequency of past-month expenditure on B category machines and broader gambling involvement were modest, suggesting that most of the variability in eye-movements and fixations reflects players navigation through the sequenced behaviours of placing bets and monitoring spinning roulette wheels or slot-reels in anticipation of game outcomes. Players with extensive machine experience tended to discount slightly the roulette wheel as a visual feature; while players with broader patterns of gambling activity looked at credit balances frequently, suggesting that such individuals are mindful of available credits. Players with gambling problems allocated fewer fixations over the chip-placement-area while placing bets and while watching the wheel spun compared to non-problem gamblers, suggesting that placing of bets can be accomplished with less attentional focus. These individuals were also more likely to look away from the machine altogether, suggesting that, in roulette play, gambling problems might be associated with a loosened attentional focus to events elsewhere in the shop. However, in slots games, problems gamblers' attended to the reward signals of previous games (amount-won) when placing the next bets. So far as we are aware, these data are the first to show that eye-tracking methodology has some potential to offer insights into machine-player interactions, and to provide a bias-free measure of individual differences in attention to games visual features and events as a function of their experience with gambling machines, gambling background and vulnerability to gambling-related harms. This study offers a methodology for studying and optimizing the timing, placement and content of harm-minimization messaging

Where Ridehail Drivers Go Between Trips: Trading off Congestion and Curb Availability?

We analyze what ridehail drivers do when out of service between paid trips. We use a dataset of 5.3 million trips in San Francisco and partition each out of service trip into cruising, repositioning, and parking segments. We find that repositioning accounts for nearly two-thirds (63%) of the time between trips, with cruising and parking accounting for 23% and 14% respectively (these figures exclude short trips). Our regression models suggest that drivers tend to make reasonable choices between repositioning and parking, heading to high-demand locations based on the time of day. However, we also find suggestive evidence of racial bias, supporting previous studies of both taxis and ridehailing that indicate that drivers tend to avoid neighborhoods with high proportions of residents of color

Where Do Ridehail Drivers Go Between Paid Trips? A San Francisco Case Study

App-based ridehailing services such as Uber and Lyft have revolutionized urban travel. These services improve mobility and reduce demand for parking, but also increase vehicle travel and shift some trips away from walking and public transit.1 As a result, ridehailing has been the largest contributor to increased congestion in recent years in cities such as San Francisco.2 Ridehil services could also be contributing to traffic congestion and pollution when vehicles are out of service between paid rides. Drivers might cruise (circle around while waiting for the next paid ride) or reposition (move to another location in anticipation of the next ride request), both of which can exacerbate congestion and pollution. They might also park (either on- or off-street), which would reduce congestion and pollution but may affect parking and curbspace availability or interfere with other street activities such as drop-offs and deliveries. To gain a better understanding of ridehail driver behavior between paid rides, UC researchers evaluated over 5.3 million ridehail trips in San Francisco in November and December 2016. Each trip was divided into cruising, repositioning, and parking segments

Where Ridehail Drivers Go Between Trips: Trading off Congestion and Curb Availability?

We analyze what ridehail drivers do when out of service between paid trips. We use a dataset of 5.3 million trips in San Francisco and partition each out of service trip into cruising, repositioning, and parking segments. We find that repositioning accounts for nearly two-thirds (63%) of the time between trips, with cruising and parking accounting for 23% and 14% respectively (these figures exclude short trips). Our regression models suggest that drivers tend to make reasonable choices between repositioning and parking, heading to high-demand locations based on the time of day. However, we also find suggestive evidence of racial bias, supporting previous studies of both taxis and ridehailing that indicate that drivers tend to avoid neighborhoods with high proportions of residents of color

Where ridehail drivers go between trips

AbstractWe analyze what ridehail drivers do when searching for paid fares. We use a dataset of 5.3 million trips in San Francisco and partition each search trip into cruising, repositioning, and parking segments. We find that repositioning accounts for nearly two-thirds (63%) of the time between trips, with cruising and parking accounting for 23% and 14% respectively (these figures exclude short trips). Our regression models suggest that drivers tend to make reasonable choices between repositioning and parking, heading to high-demand locations based on the time of day. However, we also find evidence of racial disparities, supporting previous studies of both taxis and ridehailing that indicate that drivers tend to avoid neighborhoods with high proportions of residents of color

A map of global peatland extent created using machine learning (Peat-ML)

Peatlands store large amounts of soil carbon and freshwater, constituting an important component of the global carbon and hydrologic cycles. Accurate information on the global extent and distribution of peatlands is presently lacking but is needed by Earth system models (ESMs) to simulate the effects of climate change on the global carbon and hydrologic balance. Here, we present Peat-ML, a spatially continuous global map of peatland fractional coverage generated using machine learning (ML) techniques suitable for use as a prescribed geophysical field in an ESM. Inputs to our statistical model follow drivers of peatland formation and include spatially distributed climate, geomorphological and soil data, and remotely sensed vegetation indices. Available maps of peatland fractional coverage for 14 relatively extensive regions were used along with mapped ecoregions of non-peatland areas to train the statistical model. In addition to qualitative comparisons to other maps in the literature, we estimated model error in two ways. The first estimate used the training data in a blocked leave-one-out cross-validation strategy designed to minimize the influence of spatial autocorrelation. That approach yielded an average r2 of 0.73 with a root-mean-square error and mean bias error of 9.11 % and −0.36 %, respectively. Our second error estimate was generated by comparing Peat-ML against a high-quality, extensively ground-truthed map generated by Ducks Unlimited Canada for the Canadian Boreal Plains region. This comparison suggests our map to be of comparable quality to mapping products generated through more traditional approaches, at least for boreal peatlands