Quick Lists: Enriched Playlist Embeddings for Future Playlist Recommendation

Recommending playlists to users in the context of a digital music service is a difficult task because a playlist is often more than the mere sum of its parts. We present a novel method for generating playlist embeddings that are invariant to playlist length and sensitive to local and global track ordering. The embeddings also capture information about playlist sequencing, and are enriched with side information about the playlist user. We show that these embeddings are useful for generating next-best playlist recommendations, and that side information can be used for the cold start problem

Assessing movements of three buoy line types using DSTmilli Loggers: Implications for entanglements of bottlenose dolphins in the crab pot fishery

A study was conducted in October 2006 in the Charleston, South Carolina area to test the movements of three different buoy line types to determine which produced a preferred profile that could reduce the risk of dolphin entanglement. Tests on diamond-braided nylon commonly used in the crab pot fishery were compared with stiffened line of Esterpro and calf types in both shallow and deep water environments using DSTmilli data loggers. Loggers were placed at intervals along the lines to record depth, and thus movements, over a 24 hour period. Three observers viewed video animations and charts created for each of the six trial days from the collected logger data and provided their opinions on the most desirable line type that fit set criteria. A quantitative analysis (ANCOVA) of the data was conducted taking into consideration daily tidal fluctuations and logger movements. Loggers tracking the tides had an r2 value approaching 1.00 and produced little movement other than with the tides. Conversely, r2 values approaching 0.00 were less affected by tidal movement and influenced by currents that cause more erratic movement. Results from this study showed that stiffened line, in particular the medium lay Esterpro type, produced the more desirable profiles that could reduce risk of dolphin entanglement. Combining the observer’s results with the ANCOVA results, Esterpro was chosen nearly 60% of the time as opposed to the nylon line which was only chosen 10% of the time. ANCOVA results showed that the stiffened lines performed better in both the shallow and deep water environments, while the nylon line only performed better during one trial in a deep water set, most probably due to the increased current velocities experienced that day. (58pp.)(PDF contains 68 pages

Relative Comparison Kernel Learning with Auxiliary Kernels

In this work we consider the problem of learning a positive semidefinite kernel matrix from relative comparisons of the form: "object A is more similar to object B than it is to C", where comparisons are given by humans. Existing solutions to this problem assume many comparisons are provided to learn a high quality kernel. However, this can be considered unrealistic for many real-world tasks since relative assessments require human input, which is often costly or difficult to obtain. Because of this, only a limited number of these comparisons may be provided. In this work, we explore methods for aiding the process of learning a kernel with the help of auxiliary kernels built from more easily extractable information regarding the relationships among objects. We propose a new kernel learning approach in which the target kernel is defined as a conic combination of auxiliary kernels and a kernel whose elements are learned directly. We formulate a convex optimization to solve for this target kernel that adds only minor overhead to methods that use no auxiliary information. Empirical results show that in the presence of few training relative comparisons, our method can learn kernels that generalize to more out-of-sample comparisons than methods that do not utilize auxiliary information, as well as similar methods that learn metrics over objects

Surface acoustic wave attenuation by a two-dimensional electron gas in a strong magnetic field

The propagation of a surface acoustic wave (SAW) on GaAs/AlGaAs heterostructures is studied in the case where the two-dimensional electron gas (2DEG) is subject to a strong magnetic field and a smooth random potential with correlation length Lambda and amplitude Delta. The electron wave functions are described in a quasiclassical picture using results of percolation theory for two-dimensional systems. In accordance with the experimental situation, Lambda is assumed to be much smaller than the sound wavelength 2*pi/q. This restricts the absorption of surface phonons at a filling factor \bar{\nu} approx 1/2 to electrons occupying extended trajectories of fractal structure. Both piezoelectric and deformation potential interactions of surface acoustic phonons with electrons are considered and the corresponding interaction vertices are derived. These vertices are found to differ from those valid for three-dimensional bulk phonon systems with respect to the phonon wave vector dependence. We derive the appropriate dielectric function varepsilon(omega,q) to describe the effect of screening on the electron-phonon coupling. In the low temperature, high frequency regime T << Delta (omega_q*Lambda /v_D)^{alpha/2/nu}, where omega_q is the SAW frequency and v_D is the electron drift velocity, both the attenuation coefficient Gamma and varepsilon(omega,q) are independent of temperature. The classical percolation indices give alpha/2/nu=3/7. The width of the region where a strong absorption of the SAW occurs is found to be given by the scaling law |Delta \bar{\nu}| approx (omega_q*Lambda/v_D)^{alpha/2/nu}. The dependence of the electron-phonon coupling and the screening due to the 2DEG on the filling factor leads to a double-peak structure for Gamma(\bar{\nu}).Comment: 17 pages, 3 Postscript figures, minor changes mad

CityNet—Deep learning tools for urban ecoacoustic assessment

Cities support unique and valuable ecological communities, but understanding urban wildlife is limited due to the difficulties of assessing biodiversity. Ecoacoustic surveying is a useful way of assessing habitats, where biotic sound measured from audio recordings is used as a proxy for population abundance and/or activity. However, existing algorithms systematically over and underestimate measures of biotic activity in the presence of typical urban non-biotic sounds in recordings. We develop CityNet, a deep learning system using convolutional neural networks (CNNs), to measure audible biotic (CityBioNet) and anthropogenic (CityAnthroNet) acoustic activity in cities. The CNNs were trained on a large dataset of annotated audio recordings collected across Greater London, UK. Using a held-out test dataset, we compare the precision and recall of CityBioNet and CityAnthroNet separately to the best available alternative algorithms: four Acoustic Indices: Acoustic Complexity Index, Acoustic Diversity Index, Bioacoustic Index, and Normalised Difference Soundscape Index, and a state-of-the-art bird call detection CNN (bulbul). We also compare the effect of non-biotic sounds on the predictions of CityBioNet and bulbul. Finally we apply CityNet to describe acoustic patterns of the urban soundscape in two sites along an urbanisation gradient. CityBioNet was the best performing algorithm for measuring biotic activity in terms of precision and recall, followed by bulbul, whereas the Acoustic Indices performed worst. CityAnthroNet outperformed the Normalised Difference Soundscape Index, but by a smaller margin than CityBioNet achieved against the competing algorithms. The CityBioNet predictions were impacted by mechanical sounds, whereas air traffic and wind sounds influenced the bulbul predictions. Across an urbanisation gradient, we show that CityNet produced realistic daily patterns of biotic and anthropogenic acoustic activity from real-world urban audio data. Using CityNet, it is possible to automatically measure biotic and anthropogenic acoustic activity in cities from audio recordings. If embedded within an autonomous sensing system, CityNet could produce environmental data for cites at large-scales and facilitate investigation of the impacts of anthropogenic activities on wildlife. The algorithms, code and pretrained models are made freely available in combination with two expert-annotated urban audio datasets to facilitate automated environmental surveillance in cities

2,4-Dinitrophenol, the inferno drug: a netnographic study of user experiences in the quest for leanness.

Background: Despite not being licensed for human consumption, the Internet has triggered renewed, widespread interest and availability of 2,4-Dinitrophenol (DNP). DNP, a cellular metabolic poison, causes thermogenesis resulting in fat burning and weight loss. Whilst extensively available for purchase online, research on user experiences of DNP is limited. Methods: A netnographic approach was used to describe user experiences of DNP via online public websites. Public websites discussing DNP were identified and a purposeful sample selected. Discussion threads were downloaded and a textual qualitative analysis conducted. Four themes containing 71 categories were generated. Results: There exists a plethora of communal folk pharmacological advice and recommendations for DNP manufacture and use, together with associated harms and outcomes. The efficacy and untoward effects of DNP were described and discussed alongside the notion that DNP should only be used by experienced bodybuilders. Dosage and regimes for optimal use were also described. Conclusion: This unique study provides a rich examination of the knowledge, attitudes, and motivations of DNP users, illustrating the significant role of online public websites in sharing information. Further understanding of DNP users and the online communities in which they reside is warranted to facilitate engagement and formulate appropriate and effective policy responses