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Despite dedicated research has been carried out to adequately map the distribution of the sperm whale in the Mediterranean Sea, unlike other regions of the world, the species population status is still presently uncertain. The analysis of two years of continuous acoustic data provided by the ANTARES neutrino telescope revealed the year-round presence of sperm whales in the Ligurian Sea, probably associated with the availability of cephalopods in the region. The presence of the Ligurian Sea sperm whales was demonstrated through the real-time analysis of audio data streamed from a cabled-to-shore deep-sea observatory that allowed the hourly tracking of their long-range echolocation behaviour on the Internet. Interestingly, the same acoustic analysis indicated that the occurrence of surface shipping noise would apparently not condition the foraging behaviour of the sperm whale in the area, since shipping noise was almost always present when sperm whales were acoustically detected. The continuous presence of the sperm whale in the region confirms the ecological value of the Ligurian sea and the importance of ANTARES to help monitoring its ecosystems.

Recent findings on cephalopods in laboratory conditions showed that exposure to artificial noise had a direct consequence on the statocyst, sensory organs, which are responsible for their equilibrium and movements in the water column. The question remained about the contribution of the consequent near-field particle motion influence from the tank walls, to the triggering of the trauma. Offshore noise controlled exposure experiments (CEE) on common cuttlefish (Sepia officinalis), were conducted at three different depths and distances from the source and particle motion and sound pressure measurements were performed at each location. Scanning electron microscopy (SEM) revealed injuries in statocysts, which severity was quantified and found to be proportional to the distance to the transducer. These findings are the first evidence of cephalopods sensitivity to anthropogenic noise sources in their natural habitat. From the measured received power spectrum of the sweep, it was possible to determine that the animals were exposed at levels ranging from 139 to 142 dB re 1 μPa 2 and from 139 to 141 dB re 1 μPa 2 , at 1/3 octave bands centred at 315 Hz and 400 Hz, respectively. These results could therefore be considered a coherent threshold estimation of noise levels that can trigger acoustic trauma in cephalopods.

Jellyfishes represent a group of species that play an important role in oceans, particularly as a food source for different taxa and as a predator of fish larvae and planktonic prey. The massive introduction of artificial sound sources in the oceans has become a concern to science and society. While we are only beginning to understand that non-hearing specialists like cephalopods can be affected by anthropogenic noises and regulation is underway to measure European water noise levels, we still don't know yet if the impact of sound may be extended to other lower level taxa of the food web. Here we exposed two species of Mediterranean Scyphozoan medusa, Cotylorhiza tuberculata and Rhizostoma pulmo to a sweep of low frequency sounds. Scanning electron microscopy (SEM) revealed injuries in the statocyst sensory epithelium of both species after exposure to sound, that are consistent with the manifestation of a massive acoustic trauma observed in other species. The presence of acoustic trauma in marine species that are not hearing specialists, like medusa, shows the magnitude of the problem of noise pollution and the complexity of the task to determine threshold values that would help building up regulation to prevent permanent damage of the ecosystems.

Clinicians need to routinely make management decisions about patients who are at risk for a disease such as breast cancer. This paper presents a novel clinical decision support tool that is capable of helping physicians make diagnostic decisions. We apply this support system to improve the specificity of breast cancer screening and diagnosis. The system utilizes clinical context (e.g., demographics, medical history) to minimize the false positive rates while avoiding false negatives. An online contextual learning algorithm is used to update the diagnostic strategy presented to the physicians over time. We analytically evaluate the diagnostic performance loss of the proposed algorithm, in which the true patient distribution is not known and needs to be learned, as compared with the optimal strategy where all information is assumed known, and prove that the false positive rate of the proposed learning algorithm asymptotically converges to the optimum. In addition, our algorithm also has the important merit that it can provide individualized confidence estimates about the accuracy of the diagnosis recommendation. Moreover, the relevancy of contextual features is assessed, enabling the approach to identify specific contextual features that provide the most value of information in reducing diagnostic errors. Experiments were conducted using patient data collected at a large academic medical center. Our proposed approach outperforms the current clinical practice by 36% in terms of false positive rate given a 2% false negative rate.

Marine invertebrates potentially represent a group of species whose ecology may be influenced by artificial noise. Exposure to anthropogenic sound sources could have a direct consequence on the functionality and sensitivity of their sensory organs, the statocysts, which are responsible for their equilibrium and movements in the water column. The availability of novel laser Doppler vibrometer techniques has recently opened the possibility of measuring whole body (distance, velocity, and acceleration) vibration as a direct stimulus eliciting statocyst response, offering the scientific community a new level of understanding of the marine invertebrate hearing mechanism.

The Shannon Estuary on the west coast of Ireland is one of Europe's premier deepwater berths catering for ships up to 200,000 deadweight tonnage. It is also Ireland's only designated candidate special area of conservation for bottlenose dolphins under the EU Habitats Directive. Long-term static acoustic monitoring was carried out at a number of intensive shipping sites. In 2012, noise monitoring took place over a 6-month period (at 1 site) as part of Ireland's requirements under the Marine Strategy Framework Directive (MSFD). This is the first assessment of the potential effect of vessel traffic on the behavior of this discrete dolphin population.

We propose an image stream mining method in which images arrive with contexts (metadata) and need to be processed in real time by the image mining system (IMS), which needs to make predictions and derive actionable intelligence from these streams. After extracting the features of the image by preprocessing, IMS determines online the classifier to use on the extracted features to make a prediction using the context of the image. A key challenge associated with stream mining is that the prediction accuracy of the classifiers is unknown, since the image source is unknown; thus, these accuracies need to be learned online. Another key challenge of stream mining is that learning can only be done by observing the true label, but this is costly to obtain. To address these challenges, we model the image stream mining problem as an active, online contextual experts problem, where the context of the image is used to guide the classifier selection decision. We develop an active learning algorithm and show that it achieves regret sublinear in the number of images that have been observed so far. To further illustrate and assess the performance of our proposed methods, we apply them to diagnose breast cancer from the images of cellular samples obtained from the fine needle aspirate of breast mass. Our findings show that very high diagnosis accuracy can be achieved by actively obtaining only a small fraction of true labels through surgical biopsies. Other applications include video surveillance and video traffic monitoring.

The analysis of acoustic data from the ocean is a valuable tool to study free ranging cetaceans and anthropogenic noise. Due to the typically large volume of acquired data, there is a demand for automated analysis techniques. Many cetaceans produce acoustic pulses (echolocation clicks) with a pulse repetition interval (PRI) remaining nearly constant over several pulses. Analyzing these pulse trains is challenging because they are often interleaved. This article presents an algorithm that estimates a pulse's PRI with respect to neighboring pulses. It includes a deinterleaving step that operates via a spectral dissimilarity metric. The sperm whale (SW) produces trains with PRIs between 0.5 and 2 s. As a validation, the algorithm was used for the PRI-based identification of SW click trains with data from the NEMO-ONDE observatory that contained other pulsed sounds, mainly from ship propellers. Separation of files containing SW clicks with a medium and high signal to noise ratio from files containing other pulsed sounds gave an area under the receiver operating characteristic curve value of 0.96. This study demonstrates that PRI can be used for the automated identification of SW clicks and that deinterleaving via spectral dissimilarity contributes to algorithm performance.

The development and broad use of passive acoustic monitoring techniques have the potential to help assessing the large-scale influence of artificial noise on marine organisms and ecosystems. Deep-sea observatories have the potential to play a key role in understanding these recent acoustic changes. LIDO (Listening to the Deep Ocean Environment) is an international project that is allowing the real-time long-term monitoring of marine ambient noise as well as marine mammal sounds at cabled and standalone observatories. Here, we present the overall development of the project and the use of passive acoustic monitoring (PAM) techniques to provide the scientific community with real-time data at large spatial and temporal scales. Special attention is given to the extraction and identification of high frequency cetacean echolocation signals given the relevance of detecting target species, e.g. beaked whales, in mitigation processes, e.g. during military exercises., (Copyright © 2011. Published by Elsevier Ltd.)

In this paper, we discuss distributed optimization techniques for configuring classifiers in a real-time, informationally-distributed stream mining system. Due to the large volume of streaming data, stream mining systems must often cope with overload, which can lead to poor performance and intolerable processing delay for real-time applications. Furthermore, optimizing over an entire system of classifiers is a difficult task since changing the filtering process at one classifier can impact both the feature values of data arriving at classifiers further downstream and thus, the classification performance achieved by an ensemble of classifiers, as well as the end-to-end processing delay. To address this problem, this paper makes three main contributions: 1) Based on classification and queuing theoretic models, we propose a utility metric that captures both the performance and the delay of a binary filtering classifier system. 2) We introduce a low-complexity framework for estimating the system utility by observing, estimating, and/or exchanging parameters between the inter-related classifiers deployed across the system. 3) We provide distributed algorithms to reconfigure the system, and analyze the algorithms based on their convergence properties, optimality, information exchange overhead, and rate of adaptation to non-stationary data sources. We provide results using different video classifier systems.

In our previous work, we proposed a systematic cross-layer framework for dynamic multimedia systems, which allows each layer to make autonomous and foresighted decisions that maximize the system's long-term performance, while meeting the application's real-time delay constraints. The proposed solution solved the cross-layer optimization offline, under the assumption that the multimedia system's probabilistic dynamics were known a priori, by modeling the system as a layered Markov decision process. In practice, however, these dynamics are unknown a priori and, therefore, must be learned online. In this paper, we address this problem by allowing the multimedia system layers to learn, through repeated interactions with each other, to autonomously optimize the system's long-term performance at run-time. The two key challenges in this layered learning setting are: (i) each layer's learning performance is directly impacted by not only its own dynamics, but also by the learning processes of the other layers with which it interacts; and (ii) selecting a learning model that appropriately balances time-complexity (i.e., learning speed) with the multimedia system's limited memory and the multimedia application's real-time delay constraints. We propose two reinforcement learning algorithms for optimizing the system under different design constraints: the first algorithm solves the cross-layer optimization in a centralized manner and the second solves it in a decentralized manner. We analyze both algorithms in terms of their required computation, memory, and interlayer communication overheads. After noting that the proposed reinforcement learning algorithms learn too slowly, we introduce a complementary accelerated learning algorithm that exploits partial knowledge about the system's dynamics in order to dramatically improve the system's performance. In our experiments, we demonstrate that decentralized learning can perform equally as well as centralized learning, while enabling the layers to act autonomously. Additionally, we show that existing application-independent reinforcement learning algorithms, and existing myopic learning algorithms deployed in multimedia systems, perform significantly worse than our proposed application-aware and foresighted learning methods.