Comparing cities’ cycling patterns using online shared bicycle maps

Bicycle sharing systems are increasingly being deployed in urban areas around the world, alongside online maps that disclose the state (i.e., location, number of bicycles/number of free parking slots) of stations in each city. Recent work has demonstrated how regularly monitoring these online maps allows for a granular analysis of a city’s cycling trends; further, the literature indicates that different cities have unique spatio-temporal patterns, reducing the generalisability of any insights or models derived from a single system. In this work, we analyse 4.5 months of online bike-sharing map data from 10 cities which, combined, have 996 stations. While an aggregate comparison supports the view of cities having unique usage patterns, results of applying unsupervised learning to the temporal data shows that, instead, only the larger systems display heterogeneous behaviour, indicating that many of these systems share intrinsic similarities. We further show how these similarities are reflected in the predictability of stations’ occupancy data via a cross-city comparison of the error that a variety of approaches achieve when forecasting the number of bicycles that a station will have in the near future.We close by discussing the impact of uncovering these similarities on how future bicycle sharing systems can be designed, built, and managed.This is the accepted manuscript. The final published version is available at http://link.springer.com/article/10.1007%2Fs11116-015-9599-9

Putting mood in context: Using smartphones to examine how people feel in different locations

Does personality predict how people feel in different types of situations? The present research addressed this question using data from several thousand individuals who used a mood tracking smartphone application for several weeks. Results from our analyses indicated that people’s momentary affect was linked to their location, and provided preliminary evidence that the relationship between state affect and location might be moderated by personality. The results highlight the importance of looking at person-situation relationships at both the trait- and state-levels and also demonstrate how smartphones can be used to collect person and situation information as people go about their everyday lives.Engineering and Physical Sciences Research Council (Grant ID: EP/I032673/1)This is the final version of the article. It first appeared from Elsevier via https://doi.org/10.1016/j.jrp.2016.06.00

Low-resource Multi-task Audio Sensing for Mobile and Embedded Devices via Shared Deep Neural Network Representations

Continuous audio analysis from embedded and mobile devices is an increasingly important application domain. More and more, appliances like the Amazon Echo, along with smartphones and watches, and even research prototypes seek to perform multiple discriminative tasks simultaneously from ambient audio; for example, monitoring background sound classes (e.g., music or conversation), recognizing certain keywords (‘Hey Siri’ or ‘Alexa’), or identifying the user and her emotion from speech. The use of deep learning algorithms typically provides state-of-the-art model performances for such general audio tasks. However, the large computational demands of deep learning models are at odds with the limited processing, energy and memory resources of mobile, embedded and IoT devices. In this paper, we propose and evaluate a novel deep learning modeling and optimization framework that speci cally targets this category of embedded audio sensing tasks. Although the supported tasks are simpler than the task of speech recognition, this framework aims at maintaining accuracies in predictions while minimizing the overall processor resource footprint. The proposed model is grounded in multi-task learning principles to train shared deep layers and exploits, as input layer, only statistical summaries of audio lter banks to further lower computations. We nd that for embedded audio sensing tasks our framework is able to maintain similar accuracies, which are observed in comparable deep architectures that use single-task learning and typically more complex input layers. Most importantly, on an average, this approach provides almost a 2.1⇥ reduction in runtime, energy, and memory for four separate audio sensing tasks, assuming a variety of task combinations.Microsoft Researc

Quantifying Privacy Loss of Human Mobility Graph Topology

Human mobility is often represented as a mobility network, or graph, with nodes representing places of significance which an individual visits, such as their home, work, places of social amenity, etc., and edge weights corresponding to probability estimates of movements between these places. Previous research has shown that individuals can be identified by a small number of geolocated nodes in their mobility network, rendering mobility trace anonymization a hard task. In this paper we build on prior work and demonstrate that even when all location and timestamp information is removed from nodes, the graph topology of an individual mobility network itself is often uniquely identifying. Further, we observe that a mobility network is often unique, even when only a small number of the most popular nodes and edges are considered. We evaluate our approach using a large dataset of cell-tower location traces from 1 500 smartphone handsets with a mean duration of 430 days. We process the data to derive the top−N places visited by the device in the trace, and find that 93% of traces have a unique top−10 mobility network, and all traces are unique when considering top−15 mobility networks. Since mobility patterns, and therefore mobility networks for an individual, vary over time, we use graph kernel distance functions, to determine whether two mobility networks, taken at different points in time, represent the same individual. We then show that our distance metrics, while imperfect predictors, perform significantly better than a random strategy and therefore our approach represents a significant loss in privacy

Promile: A management architecture for programmable modular routers

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The Transcriptional Complex Sp1/KMT2A by Up-Regulating Restrictive Element 1 Silencing Transcription Factor Accelerates Methylmercury-Induced Cell Death in Motor Neuron-Like NSC34 Cells Overexpressing SOD1-G93A

Methylmercury (MeHg) exposure has been related to amyotrophic lateral sclerosis (ALS) pathogenesis and molecular mechanisms of its neurotoxicity has been associated to an overexpression of the Restrictive Element 1 Silencing Transcription factor (REST). Herein, we evaluated the possibility that MeHg could accelerate neuronal death of the motor neuron-like NSC34 cells transiently overexpressing the human Cu2+/Zn2+superoxide dismutase 1 (SOD1) gene mutated at glycine 93 (SOD1-G93A). Indeed, SOD1-G93A cells exposed to 100 nM MeHg for 24 h showed a reduction in cell viability, as compared to cells transfected with empty vector or with unmutated SOD1 construct. Interestingly, cell survival reduction in SOD1-G93A cells was associated with an increase of REST mRNA and protein levels. Furthermore, MeHg increased the expression of the transcriptional factor Sp1 and promoted its binding to REST gene promoter sequence. Notably, Sp1 knockdown reverted MeHg-induced REST increase. Co-immunoprecipitation experiments demonstrated that Sp1 physically interacted with the epigenetic writer Lysine-Methyltransferase-2A (KMT2A). Moreover, knocking-down of KMT2A reduced MeHg-induced REST mRNA and protein increase in SOD1-G93A cells. Finally, we found that MeHg-induced REST up-regulation triggered necropoptotic cell death, monitored by RIPK1 increased protein expression. Interestingly, REST knockdown or treatment with the necroptosis inhibitor Necrostatin-1 (Nec) decelerated MeH-induced cell death in SOD1-G93A cells. Collectively, this study demonstrated that MeHg hastens necroptotic cell death in SOD1-G93A cells via Sp1/KMT2A complex, that by epigenetic mechanisms increases REST gene expression

The Mergers in Abell 2256: Displaced Gas and its Connection to the Radio-emitting Plasma

We present the results of deep Chandra and XMM-Newton X-ray imaging and spatially-resolved spectroscopy of Abell 2256, a nearby (z=0.058) galaxy cluster experiencing multiple mergers and displaying a rich radio morphology dominated by a large relic. The X-ray data reveals three subclusters: (i) the `main cluster'; (ii) the remnant of an older merger in the east of the cluster with a ~ 600 kpc long tail; (iii) a bright, bullet-like, low-entropy infalling system, with a large line-of-sight velocity component. The low-entropy system displays a 250 kpc long cold front with a break and an intriguing surface brightness decrement. Interestingly, the infalling gas is not co-spatial with bright galaxies and the radio loud brightest cluster galaxy of the infalling group appears dissociated from the low entropy plasma by 50 kpc in projection, to the south of the eastern edge of the cold front. Assuming that the dark matter follows the galaxy distribution, we predict that it is also significantly offset from the low-entropy gas. Part of the low frequency radio emission near the cold front might be revived by magnetic field amplification due to differential gas motions. Using analytical models and numerical simulations, we investigate the possibility that the supersonic infall of the subcluster generates a large scale shock along our line-of-sight, which can be detected in the X-ray temperature map but is not associated with any clear features in the surface brightness distribution.Comment: Accepted for publication in MNRAS. The online supplement is available at https://bit.ly/ShockSupplemen

High-resolution, High-sensitivity, Low-frequency uGMRT View of Coma Cluster of Galaxies

We present high-resolution, high-sensitivity upgraded Giant Metrewave Radio Telescope observations of the Coma cluster (A1656) at 250-500 MHz and 550-850 MHz. At 250-500 MHz, 135 sources have extensions >0.'45 (with peak-to-local-noise ratio >4). Of these, 24 sources are associated with Coma-member galaxies. In addition, we supplement this sample of 24 galaxies with 20 ram pressure stripped (RPS) galaxies from (Chen et al. 2020, eight are included in the original extended radio source sample) and an additional five are detected and extended. We present radio morphologies, radio spectra, spectral index maps, and equipartition properties for these two samples. In general, we find the equipartition properties lie within a narrow range (e.g., Pmin = 1-3 × 10- 13 dynes cm-2). Only NGC 4874, one of the two brightest central Coma cluster galaxies, has a central energy density and pressure about five times higher and a radio source age about 50% lower than that of the other Coma galaxies. We find a diffuse tail of radio emission trailing the dominant galaxy of the merging NGC 4839 group that coincides with the slingshot tail seen in X-rays. The southwestern radio relic, B1253+275, has a large extent ≍32' × 10' (≃1.08 × 0.34 Mpc2). For NGC 4789, whose long radio tails merge into the relic and may be a source of its relativistic seed electrons, we find a transverse radio spectral gradient, a steepening from southwest to northeast across the width of the radio source. Finally, radio morphologies of the extended and RPS samples suggest that these galaxies are on their first infall into Coma on (predominantly) radial orbits