Miracle-dz/UniFAR-FTUs huggingface dataset:
The economic benefits of fulfilling the World Health Organization’s limits for particulates: A case study in Algeciras Bay (Spain)

ABSTRACT Algeciras Bay is an important industrial and port zone in the south of Spain whose pollution by particulate matter surpasses the threshold levels recommended by the World Health Organization (WHO) in its 2005 Guide on Air Quality. This study analyses the mortality avoided and the economic benefit which would be derived from a reduction of the pollution of PM2.5 and PM10 to the levels recommended by the WHO in Algeciras Bay in the period 2005-2015. The analysis carried out shows that the industrial zones, such as Los Barrios and San Roque, are those which have greater levels of pollution and in which the relative risk is greater. The calculations for Algeciras Bay between 2000 and 2015 show 182 deaths which would be avoided if the particulate matter pollution were reduced to the levels recommended by the WHO. Likewise, the economic valuation which this impact has on health is carried out through two concepts: the cost of illness and the Value of Statistical Life (VSL). The result shows that the economic benefit that would come out with the cost of illness valuation is 5,329,110€ and from the VSL is 414,787,113€. Implications: PM2.5 has a greater concentration in industrial localities and is linked to the industrial activity. When the particulate matter pollution is reduced to the levels recommended by the WHO in an industrialised area such as Algeciras (Spain), 182 deaths which would be avoided. The result shows that the economic benefit that would come out with the cost of illness valuation is 5,329,110€ and from the value of statistical life is 414,787,113€.
Belief Control Strategies for Interactions Over Weakly-Connected Graphs

In diffusion social learning over weakly-connected graphs, it has been shown recently that influential agents shape the beliefs of non-influential agents. This paper analyzes this mechanism more closely and addresses two main questions. First, the article examines how much freedom influential agents have in controlling the beliefs of the receiving agents, namely, whether receiving agents can be driven to arbitrary beliefs and whether the network structure limits the scope of control by the influential agents. Second, even if there is a limit to what influential agents can accomplish, this article develops mechanisms by which they can lead receiving agents to adopt certain beliefs. These questions raise interesting possibilities about belief control over networked agents. Once addressed, one ends up with design procedures that allow influential agents to drive other agents to endorse particular beliefs regardless of their local observations or convictions. The theoretical findings are illustrated by means of examples.
A perceptual study on the manipulation of facial features for trait portrayal in virtual agents

Human perceptual studies have shown that facial characteristics affect judgments about the personality of a person. For example, larger facial width has been associated with judgments of aggressiveness, dominance, and untrustworthiness. Previous studies of virtual faces have not been able to reflect the same perceptual rules, but have used characters with unrealistic feature sizes or highly abstract characters. For this study, we created virtual characters with realistic feature dimensions and investigated the effects of facial width and eye size on personality perception. Our results indicate that virtual characters may indeed follow different perceptual rules for facial width, and care must be taken when manipulating eye size. These findings are useful for effective character design for video games, movies, and embodied virtual agents.
Robust and Fine-grained Prosody Control of End-to-end Speech Synthesis

We propose prosody embeddings for emotional and expressive speech synthesis networks. The proposed methods introduce temporal structures in the embedding networks, thus enabling fine-grained control of the speaking style of the synthesized speech. The temporal structures can be designed either on the speech side or the text side, leading to different control resolutions in time. The prosody embedding networks are plugged into end-to-end speech synthesis networks and trained without any other supervision except for the target speech for synthesizing. It is demonstrated that the prosody embedding networks learned to extract prosodic features. By adjusting the learned prosody features, we could change the pitch and amplitude of the synthesized speech both at the frame level and the phoneme level. We also introduce the temporal normalization of prosody embeddings, which shows better robustness against speaker perturbations during prosody transfer tasks.
MARKOV RECURRENT NEURAL NETWORKS

Deep learning has achieved great success in many real-world applications. For speech and language processing, recurrent neural networks are learned to characterize sequential patterns and extract the temporal information based on dynamic states which are evolved through time and stored as an internal memory. Traditionally, simple transition function using input-to-hidden and hidden-to-hidden weights is insufficient. To strengthen the learning capability, it is crucial to explore the diversity of latent structure in sequential signals and learn the stochastic trajectory of signal transitions to improve sequential prediction. This paper proposes the stochastic modeling of transitions in deep sequential learning. Our idea is to enhance latent variable representation by discovering the Markov state transitions in sequential data based on a K-state long short-term memory (LSTM) model. Such a latent state machine is capable of learning the complicated latent semantics in highly structured and heterogeneous sequential data. Gumbel-softmax is introduced to implement stochastic learning procedure with discrete states. Experimental results on visual and text language modeling illustrate the merit of the proposed stochastic transitions in sequential prediction with limited amount of parameters.
RTM results for Predicting Translation Performance

With improved prediction combination using weights based on their training performance and stacking and multilayer perceptrons to build deeper prediction models, RTMs become the 3rd system in general at the sentence-level prediction of translation scores and achieve the lowest RMSE in English to German NMT QET results. For the document-level task, we compare document-level RTM models with sentence-level RTM models obtained with the concatenation of document sentences and obtain similar results.
Ultrasonic Adaptive Sound Speed Estimation for the Diagnosis and Quantification of Hepatic Steatosis: A Pilot Study

Abstract Purpose To evaluate the ability of a new ultrasound (US) method based on sound speed estimation (SSE) with respect to the detection, quantification, and grading of hepatic steatosis using magnetic resonance (MR) proton density fat fraction (PDFF) as the reference standard and to calculate one US fat index based on the patient’s SSE. Materials and Methods This study received local IRB approval. Written informed consent was obtained from patients. We consecutively included N = 50 patients as the training cohort and a further N = 50 as the validation cohort who underwent both SSE and abdominal MR. Hepatic steatosis was classified according to MR-PDFF cutoffs as: S0 ≤ 6.5 %, S1 6.5 to 16.5 %, S2 16.5 to 22 %, S3 ≥ 22 %. Receiver operating curve analysis was performed to evaluate the diagnostic performance of SSE in the diagnosis of steatosis (S1–S3). Based on the optimal data fit derived from our study, we proposed a correspondence between the MR-PDFF and a US fat index. Coefficient of determination R2 was used to evaluate fit quality and was considered robust when R2 > 0.6. Results The training and validation cohorts presented mean SSE values of 1.570 ± 0.026 and 1.568 ± 0.023 mm/µs for S0 and 1.521 ± 0.031 and 1.514 ± 0.019 mm/µs for S1–S3 (p < 0.01) patients, respectively. An SSE threshold of ≤ 1.537 mm/µs had a sensitivity of 80 % and a specificity of 85.7 % in the diagnosis of steatosis (S1-S3) in the training cohort. Robust correspondence between MR-PDFF and the US fat index was found both for the training (R2 = 0.73) and the validation cohort (R2 = 0.76). Conclusion SSE can be used to detect, quantify and grade liver steatosis and to calculate a US fat index.
Real-Time Pulse Waveform Profiling Algorithm for Wearable Applications

A pulse waveform profiling algorithm is introduced for processing real-time photoplethysmography (PPG) data in mobile applications. The real PPG waveform is discriminated from artifacts based on rules of probability distribution and physiological significance. The swift background is removed through a two-step method to minimize its effect on the final waveform analysis: interpolating the midpoint of the amplitude of the PPG AC part and then interpolating the minimum of each pulse period. The pulse waveform features are extracted from the stratified PPG wave data. The implementation of the proposed algorithm in a real-time mobile application demonstrated its high efficiency and robustness with a sensitivity of around 96% and a positive specificity of 100%. The potential applications of these extracted features are discussed in terms of long-term trends in physiology such as circadian and arrhythmia real-time monitoring.
Making Open Data Work for Civic Advocacy

The value of data in supporting citizen participation in processes of place-making and community building is widely recognised. While the open data movement now permits citizens to acquire governmental data relating to their communities, little to no effort is made to ensure that these datasets are accessible and interpretable by non-professionals. Through a series of community engagements spanning an 18-month period, we co-designed Data:In Place, an open source web tool which supports citizens in accessing, interpreting and making sense of open data. Leveraging visual map-based querying, citizens can access official statistics about their community, interrogate the data, and map their own data sources to create data visualisations. Reflecting on the participatory design process and the designed technology, we provide a framing to make open data work for civic advocacy.
Mixed Fidelity Aerodynamics and Aero-Structural Optimization for Wings

Automatic multidisciplinary design optimization is one of the challenges that are faced in the processes involved in designing efficient wings for aircraft. In this paper we present mixed fidelity aerodynamic and aero-structural optimization methods for designing wings. A novel shape design methodology has been developed - it is based on a mix of the automatic aerodynamic optimization for a reference aircraft model, and the aero-structural optimization for an uninhabited air vehicle (UAV) with a high aspect ratio wing. This paper is a significant step towards making it possible to perform all the core processes for aerodynamic and aero-structural optimization that require special skills in a fully automatic manner - this covers all the processes from creating the mesh for the wing simulation to executing the high-fidelity computational fluid dynamics (CFD) analysis code. Our results confirm that the simulation tools can make it possible for a far broader range of engineering researchers and developers to design aircraft in much simpler and more efficient ways. This is a vital step in the evolution of wing design processes as it means that the extremely expensive laboratory experiments that were traditionally used when designing the wings can now be replaced with more cost effective high performance computing (HPC) simulation that utilize accurate numerical methods.
Expression of ABCA4 in the retinal pigment epithelium and its implications for Stargardt macular degeneration

Significance Recessive Stargardt macular degeneration (STGD1) and a subset of cone–rod dystrophies are caused by mutations in the Abca4 gene. The ABCA4 protein is a flippase in photoreceptor cells that helps eliminate retinaldehyde, a toxic photoproduct of vision. Here we found that ABCA4 is additionally present in the retinal pigment epithelium (RPE) of mice at approximately 1% of its abundance in the neural retina. Genetically modified mice that express ABCA4 in RPE but not in photoreceptor cells showed partial rescue of both the lipofuscin accumulation and photoreceptor degeneration observed in Abca4−/− mice and in STGD1 patients. These observations suggest that ABCA4 in the RPE prevents photoreceptor degeneration in Abca4−/− mice and possibly in STGD1 patients. Recessive Stargardt disease (STGD1) is an inherited blinding disorder caused by mutations in the Abca4 gene. ABCA4 is a flippase in photoreceptor outer segments (OS) that translocates retinaldehyde conjugated to phosphatidylethanolamine across OS disc membranes. Loss of ABCA4 in Abca4−/− mice and STGD1 patients causes buildup of lipofuscin in the retinal pigment epithelium (RPE) and degeneration of photoreceptors, leading to blindness. No effective treatment currently exists for STGD1. Here we show by several approaches that ABCA4 is additionally expressed in RPE cells. (i) By in situ hybridization analysis and by RNA-sequencing analysis, we show the Abca4 mRNA is expressed in human and mouse RPE cells. (ii) By quantitative immunoblotting, we show that the level of ABCA4 protein in homogenates of wild-type mouse RPE is about 1% of the level in neural retina homogenates. (iii) ABCA4 immunofluorescence is present in RPE cells of wild-type and Mertk−/− but not Abca4−/− mouse retina sections, where it colocalizes with endolysosomal proteins. To elucidate the role of ABCA4 in RPE cells, we generated a line of genetically modified mice that express ABCA4 in RPE cells but not in photoreceptors. Mice from this line on the Abca4−/− background showed partial rescue of photoreceptor degeneration and decreased lipofuscin accumulation compared with nontransgenic Abca4−/− mice. We propose that ABCA4 functions to recycle retinaldehyde released during proteolysis of rhodopsin in RPE endolysosomes following daily phagocytosis of distal photoreceptor OS. ABCA4 deficiency in the RPE may play a role in the pathogenesis of STGD1.
Risk factors for social withdrawal in amyotrophic lateral sclerosis/motor neurone disease

Abstract Background: Greater social withdrawal is related to higher levels of psychological distress and poorer adaptation to a diagnosis of amyotrophic lateral sclerosis. Objectives: To examine whether demographics and functional deficits can be used to assess which patients may be at risk of social withdrawal and whether symptoms including depression and anxiety can provide additional information for identifying individuals at risk. Furthermore, to examine whether patient-perceived stigma has a role in mediating the effects of any of the predictors of social withdrawal. Methods: A total of 559 participants in the ongoing Trajectories of Outcomes in Neurological Conditions (TONiC) study completed a questionnaire pack collecting data on demographics and a range of patient reported measures. Multiple regression analysis was employed to assess associations of functional ability, demographics, physical symptoms, anxiety, and depression with social withdrawal. The mediating role of stigma was assessed through the development of a bivariate linear regression model for stigma and social withdrawal. Results: Disability in the bulbar and motor domains, anxiety and depression were found to be significant predictors of social withdrawal. Stigma was the strongest single predictor of social withdrawal and was found to partially mediate the effects of functional deficits and mood on social withdrawal. Conclusions: Social withdrawal is associated with worse motor disability and poorer bulbar function, as well as increased anxiety and depression. Stigma is a powerful predictor for social withdrawal; further work should investigate whether stigma is a potential target for psychological interventions aimed at reducing social withdrawal and improving quality of life.
D-Pruner: Filter-Based Pruning Method for Deep Convolutional Neural Network

The emergence of augmented reality devices such as Google Glass and Microsoft Hololens has opened up a new class of vision sensing applications. Those applications often require the ability to continuously capture and analyze contextual information from video streams. They often adopt various deep learning algorithms such as convolutional neural networks (CNN) to achieve high recognition accuracy while facing severe challenges to run computationally intensive deep learning algorithms on resource-constrained mobile devices. In this paper, we propose and explore a new class of compression technique called D-Pruner to efficiently prune redundant parameters within a CNN model to run the model efficiently on mobile devices. D-Pruner removes redundancy by embedding a small additional network. This network evaluates the importance of filters and removes them during the fine-tuning phase to efficiently reduce the size of the model while maintaining the accuracy of the original model. We evaluated D-Pruner on various datasets such as CIFAR-10 and CIFAR-100 and showed that D-Pruner could reduce a significant amount of parameters up to 4.4 times on many existing models while maintaining accuracy drop less than 1%.
Departing and Returning

Recent work has identified a variety of motivations for various forms of technology use and non-use. However, less work has closely examined relationships between those motivations and the experiences of transiting among these different forms of use and non-use. This paper fills that gap by conducting a qualitative interview- and diary-based study where participants were asked to deactivate their Facebook account. An abductive analysis suggests participants' experiences can be organized under the conceptual umbrella of sense of agency, which refers to an individual's perception that their actions are under their own control. The analysis shows how, across disparate motivations, all participants took actions that increased their own subjective sense of agency, regardless of whether they returned to Facebook or not. The discussion applies this conceptual lens to prior studies of technology use and non-use. Doing so shows how sense of agency may provide an organizing orientation for understanding subjective experiences of use and non-use.
The MIT Alewife machine: architecture and performance

Alewife is a multiprocessor architecture that supports up to 512 processing nodes connected over a scalable and cost-effective mesh network at a constant cost per node. The MIT Alewife machine, a prototype implementation of the architecture, demonstrates that a parallel system can be both scalable and programmable. Four mechanisms combine to achieve these goals: software-extended coherent shared memory provides a global, linear address space; integrated message passing allows compiler and operating system designers to provide efficient communication and synchronization; support for fine-grain computation allows many processors to cooperate on small problem sizes; and latency tolerance mechanisms-including block multithreading and prefetching-mask unavoidable delays due to communication. Microbenchmarks, together with over a dozen complete applications running on the 32-node prototype, help to analyze the behavior of the system. Analysis shows that integrating message passing with shared memory enables a cost-efficient solution to the cache coherence problem and provides a rich set of programming primitives. Block multithreading and prefetching improve performance by up to 25%, individually, and 35% together. Finally, language constructs that allow programmers to express fine-grain synchronization can improve performance by over a factor of two.
Weakly Supervised Scene Parsing with Point-based Distance Metric Learning

Semantic scene parsing is suffering from the fact that pixellevel annotations are hard to be collected. To tackle this issue, we propose a Point-based Distance Metric Learning (PDML) in this paper. PDML does not require dense annotated masks and only leverages several labeled points that are much easier to obtain to guide the training process. Concretely, we leverage semantic relationship among the annotated points by encouraging the feature representations of the intra- and intercategory points to keep consistent, i.e. points within the same category should have more similar feature representations compared to those from different categories. We formulate such a characteristic into a simple distance metric loss, which collaborates with the point-wise cross-entropy loss to optimize the deep neural networks. Furthermore, to fully exploit the limited annotations, distance metric learning is conducted across different training images instead of simply adopting an image-dependent manner. We conduct extensive experiments on two challenging scene parsing benchmarks of PASCALContext and ADE 20K to validate the effectiveness of our PDML, and competitive mIoU scores are achieved.
An audio-only method for advertisement detection in broadcast television content

We address the task of advertisement detection in broadcast television content. While typically approached from a video-only or audio-visual perspective, we present an audio-only method. Our approach centres on the detection of short silences which exist at the boundaries between programming and advertising, as well as between the advertisements themselves. To identify advertising regions we first locate all points within the broadcast content with very low signal energy. Next, we use a multiple linear regression model to reject non-boundary silences based on features extracted from the local context immediately surrounding the silence. Finally, we determine the advertising regions based on the long-term grouping of detected boundary silences. When evaluated over a 26 hour annotated database covering national and commercial Portuguese television channels we obtain a Matthews correlation coefficient in excess of 0.87 and outperform a freely available audio-visual approach.
A WiSARD-based multi-term memory framework for online tracking of objects

In this paper it is proposed a generic object tracker with real- time performance. The proposed tracker is inspired on the hierarchical short-term and medium-term memories for which patterns are stored as discriminators of a WiSARD weightless neural network. This approach is evaluated through benchmark video sequences published by Babenko et al. Experiments show that the WiSARD-based approach outperforms most of the previous results in the literature, with respect to the same dataset.