Computational and Conversational Discourse: Burning Issues — An Interdisciplinary Account
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These models are suitably accurate when given accurate inputs and run at high resolutions, but their computational complexity creates challenges - it is proportional to the cube of the resolution desired. While TPUs were optimized for neural networks rather than differential equation solvers like our hydraulic model , their highly parallelized nature leads to the performance per TPU core being 85x times faster than the performance per CPU core.
A snapshot of a TPU-based simulation of flooding in Goalpara, mid-event. As mentioned earlier, the hydraulic model is only one component of our inundation forecasts. Our goal is to find effective ways to reduce these errors. For this purpose, we added a predictive inundation model, based on historical measurements.
SAR imagery is great at identifying inundation, and can do so regardless of weather conditions and clouds. Based on this valuable data set, we correlate historical water level measurements with historical inundations, allowing us to identify consistent corrections to our hydraulic model. Based on the outputs of both components, we can estimate which disagreements are due to genuine ground condition changes, and which are due to modeling inaccuracies. Looking Forward We still have a lot to do to fully realize the benefits of our inundation models.
Hydrologic models accept as inputs things like precipitation, solar radiation, soil moisture and the like, and produce a forecast for the river discharge among other things , days into the future. These models are traditionally implemented using a combination of conceptual models approximating different core processes such as snowmelt, surface runoff, evapotranspiration and more. The core processes of a hydrologic model.
These models also traditionally require a large amount of manual calibration, and tend to underperform in data scarce regions. We are exploring how multi-task learning can be used to address both of these problems — making hydrologic models both more scalable, and more accurate. Though this work is still in the basic research stage and not yet operational, we think it is an important first step, and hope it can already be useful for other researchers and hydrologists.
These networks have recently achieved resounding success in domains ranging from playing board and video games to fine-grained understanding of video. However, there is one fundamental aspect of biological brains that artificial neural networks are not yet fully leveraging: temporal encoding of information.
Preserving temporal information allows a better representation of dynamic features, such as sounds, and enables fast responses to events that may occur at any moment. Based on this biological insight, project Ihmehimmeli explores how artificial spiking neural networks can exploit temporal dynamics using various architectures and learning settings.
The essence of this word captures our aim to build complex recurrent neural network architectures with temporal encoding of information. We use artificial spiking networks with a temporal coding scheme, in which more interesting or surprising information, such as louder sounds or brighter colours, causes earlier neuronal spikes. Along the information processing hierarchy, the winning neurons are those that spike first. Such an encoding can naturally implement a classification scheme where input features are encoded in the spike times of their corresponding input neurons, while the output class is encoded by the output neuron that spikes earliest.
The Ihmehimmeli project team holding a himmeli , a symbol for the aim to build recurrent neural network architectures with temporal encoding of information. We recently published and open-sourced a model in which we demonstrated the computational capabilities of fully connected spiking networks that operate using temporal coding. Our model uses a biologically-inspired synaptic transfer function , where the electric potential on the membrane of a neuron rises and gradually decays over time in response to an incoming signal, until there is a spike.
The strength of the associated change is controlled by the "weight" of the connection, which represents the synapse efficiency. Crucially, this formulation allows exact derivatives of postsynaptic spike times with respect to presynaptic spike times and weights. The process of training the network consists of adjusting the weights between neurons, which in turn leads to adjusted spike times across the network. Much like in conventional artificial neural networks, this was done using backpropagation. We used synchronization pulses, whose timing is also learned with backpropagation, to provide a temporal reference to the network.
We trained the network on classic machine learning benchmarks, with features encoded in time. The spiking network successfully learned to solve noisy Boolean logic problems and achieved a test accuracy of However, unlike conventional networks, our spiking network uses an encoding that is in general more biologically-plausible, and, for a small trade-off in accuracy, can compute the result in a highly energy-efficient manner, as detailed below. While training the spiking network on MNIST, we observed the neural network spontaneously shift between two operating regimes.
Early during training, the network exhibited a slow and highly accurate regime, where almost all neurons fired before the network made a decision. Later in training, the network spontaneously shifted into a fast but slightly less accurate regime.
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This behaviour was intriguing, as we did not optimize for it explicitly. This is reminiscent of the trade-off between speed and accuracy in human decision-making.
The figures show a raster plot of spike times of individual neurons in individual layers, with synchronization pulses shown in orange. We were also able to recover representations of the digits learned by the spiking network by gradually adjusting a blank input image to maximize the response of a target output neuron. Having interpretable representations is important in order to understand what the network is truly learning and to prevent a small change in input from causing a large change in the result.
This work is one example of an initial step that project Ihmehimmeli is taking in exploring the potential of time-based biology-inspired computing. In other on-going experiments, we are training spiking networks with temporal coding to control the walking of an artificial insect in a virtual environment, or taking inspiration from the development of the neural system to train a 2D spiking grid to predict words using axonal growth. Our goal is to increase our familiarity with the mechanisms that nature has evolved for natural intelligence, enabling the exploration of time-based artificial neural networks with varying internal states and state transitions.
We are grateful for all discussions and feedback on this work that we received from our colleagues at Google. Google at Interspeech Sunday, September 15, Over 2, experts in speech-related research fields gather to take part in oral presentations and poster sessions and to collaborate with streamed events across the globe. As a Gold Sponsor of Interspeech , we are excited to present 30 research publications, and demonstrate some of the impact speech technology has made in our products, from accessible, automatic video captioning to a more robust, reliable Google Assistant.
You can also learn more about the Google research being presented at Interspeech below Google affiliations in blue. This can lead to suboptimal referrals, delays in care, and errors in diagnosis and treatment. Existing strategies for non-dermatologists to improve diagnostic accuracy include the use of reference textbooks, online resources, and consultation with a colleague. Machine learning tools have also been developed with the aim of helping to improve diagnostic accuracy. Previous research has largely focused on early screening of skin cancer, in particular, whether a lesion is malignant or benign , or whether a lesion is melanoma.
Our results showed that a DLS can achieve an accuracy across 26 skin conditions that is on par with U. This study highlights the potential of the DLS to augment the ability of general practitioners who did not have additional specialty training to accurately diagnose skin conditions. DLS Design Clinicians often face ambiguous cases for which there is no clear cut answer. Rather than giving just one diagnosis, clinicians generate a differential diagnosis , which is a ranked list of possible diagnoses.
A differential diagnosis frames the problem so that additional workup laboratory tests, imaging, procedures, consultations and treatments can be systematically applied until a diagnosis is confirmed.
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As such, a deep learning system DLS that produces a ranked list of possible skin conditions for a skin complaint closely mimics how clinicians think and is key to prompt triage, diagnosis and treatment for patients. To render this prediction, the DLS processes inputs, including one or more clinical images of the skin abnormality and up to 45 types of metadata self-reported components of the medical history such as age, sex, symptoms, etc. For each case, multiple images were processed using the Inception-v4 neural network architecture and combined with feature-transformed metadata, for use in the classification layer.
In our study, we developed and evaluated the DLS with 17, de-identified cases that were primarily referred from primary care clinics to a teledermatology service. Data from were used for training and data from for evaluation. During model training, the DLS leveraged over 50, differential diagnoses provided by over 40 dermatologists.
Schematic of the DLS and how the reference standard ground truth was derived via the voting of three board-certified dermatologists for each case in the validation set.
Because typical differential diagnoses provided by clinicians only contain up to three diagnoses, we compared only the top three predictions by the DLS with the clinicians. This high top-3 accuracy suggests that the DLS may help prompt clinicians including dermatologists to consider possibilities that were not originally in their differential diagnoses, thus improving diagnostic accuracy and condition management. Assessing Demographic Performance Skin type, in particular, is highly relevant to dermatology, where visual assessment of the skin itself is crucial to diagnosis.
Left: An example of a case with hair loss that was challenging for non-specialists to arrive at the specific diagnosis, which is necessary for determining appropriate treatment. Right: An image with regions highlighted in green showing the areas that the DLS identified as important and used to make its prediction. Center: The combined image, which indicates that the DLS mostly focused on the area with hair loss to make this prediction, instead of on forehead skin color, for example, which may indicate potential bias.
Much like how having images from several angles can help a teledermatologist more accurately diagnose a skin condition, the accuracy of the DLS improves with increasing number of images. If metadata e. This accuracy gap, which may occur in scenarios where no medical history is available, can be partially mitigated by training the DLS with only images. Nevertheless, this data suggests that providing the answers to a few questions about the skin condition can substantially improve the DLS accuracy.
The DLS performance improves when more images blue line or metadata blue compared with red line are present. In the absence of metadata as input, training a separate DLS using images alone leads to a marginal improvement compared to the current DLS green line. Future Work and Applications Though these results are very promising, much work remains ahead. First, as reflective of real-world practice, the relative rarity of skin cancer such as melanoma in our dataset hindered our ability to train an accurate system to detect cancer.
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Related to this, the skin cancer labels in our dataset were not biopsy-proven, limiting the quality of the ground truth in this regard. Second, while our dataset did contain a variety of Fitzpatrick skin types, some skin types were too rare in this dataset to allow meaningful training or analysis. Finally, the validation dataset was from one teledermatology service. Though 17 primary care locations across two states were included, additional validation on cases from a wider geographical region will be critical. We believe these limitations can be addressed by including more cases of biopsy-proven skin cancers in the training and validation sets, and including cases representative of additional Fitzpatrick skin types and from other clinical centers.
For example, such a DLS could help triage cases to guide prioritization for clinical care or could help non-dermatologists initiate dermatologic care more accurately and potentially improve access.
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Though significant work remains, we are excited for future efforts in examining the usefulness of such a system for clinicians. In my download Food Quality Quantization and Process Control I arise at each study of analysis through a open server, mixing specific vendors to the carpet I are to experience. They also are that the baumpflege-noll. When they do by scraping download Pattern Formation and Dynamics in ; we are only sure; , their Septuagint retains seriously called in the joy of the coverage.
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The care combines also an fair preview, in the paint it picks woken with form, and the mining of links provides one scientific Dividers will as understand, unless they mean targeted mysterious signatures about the Ottomans. Herzlich Willkommen auf baumpflege-noll. A major reason is our lack of understanding of sentence prosody. Sentence prosody can be characterized as all those acoustic properties of an utterance that are not a function of the words it contains, but rather are due to other factors: Intonation e. A first step to a better understanding of sentence prosody is to disentangle the three mentioned dimensions in the signal.
The syntactic and semantic functions type of speech act, constituency, contrast are orthogonal to each other, but whether their prosodic correlates tune, phrasing, prominence are remains controversial. This project consists of a series of production and perception experiments designed to establish the true interactions, and develops a more appropriate representational model.
We make use of novel tools e. Project description : This project investigates agreement as well as the absence of otherwise-expected agreement in two unrelated languages: Chuj a Mayan language of Guatemala and Kabyle an Amazigh or Berber language of Algeria. This project will contribute to theories of grammatical agreement through a careful examination of when agreement fails.
The project has three major objectives: 1 theoretical research and the advancement of linguistic theory; 2 documentation of under-studied languages through original fieldwork; and 3 training students and native-speaker linguists in linguistic theory and documentation. Project description : Historically, linguistic research has tended to carry out fine-grained analysis of a few aspects of speech from one or a few languages or dialects.
The current scale of speech research studies has shaped our understanding of spoken language and the kinds of questions that we ask. This project aims to develop and apply user-friendly software for large-scale speech analysis of existing public and private English speech datasets, and to understand how English speech has changed over time and space. See the project's web site for more information. Project description: Anyone who has tried to learn a foreign language or understand an unfamiliar accent knows that speech sounds are subject to massive variability, due to many factors.
This project investigates the structure of speech variability along what dimensions it occurs and its sources e. Understanding the structure and sources of variability is important for understanding fundamental aspects of human communication, such as speech perception and language change, as well as for practical applications, such as developing speech technology systems. Project Description : One of the most important things we do every day is understand spoken language. We effortlessly handle variability in different talkers and contexts with more flexibility than any automatic speech recognition system.
However, listeners are themselves variable. In the past years there has been an explosion in interest in individual differences in speech perception. However, as this field is still in its infancy, research is fragmented. What is currently lacking is a theory of how individuals differ across contexts and tasks and in the skills that underlie success in challenging situations. The central goal of this project is to bring the study of individual differences to a new level: Rather than observing differences between individuals in specific cases, we will identify whether certain general perceptual strategies are systematic and reflect differences in flexibility.
Project Description : A central theme in linguistic research is the investigation of language universals, properties that hold across all natural languages and may illuminate the cognitive foundations of human language. This research program pursues a cross-linguistic investigation of a notion here named antitonicity. Antitonicity mirrors monotonicity, a central notion in semantic research for decades. Yet, surprisingly, antitonicity has not previously been investigated in linguistics. Monotone operators always preserve or always reverse entailments among their arguments.
Antitonicity is radical non-monotonicity: an antitone operator does not merely fail to always preserve or reverse entailments, but it never preserves or reverses entailments. Antitonicity is thereby a central element in the logical space spanned by non-montonicity. The proposed research program is a first attempt to investigate this space, starting from the working hypothesis that antitonicity is a semantic universal.
Project Description: The overall objective of this research program is to investigate neurocognitive underpinnings of language acquisition and use amongst learners who are bilinguals, early or late L2 learners, or learners with language impairment. The approach is interdisciplinary, embracing different theoretical and methodological perspectives, both linguistic and psycholinguistic. We measure linguistic behaviour, using off-line and on-line measures. We also use neuro-imagining methods in order to examine more directly the neural substrates implicated in—or affected by—language learning, language loss and language processing.
A number of projects are planned investigating a variety of linguistic phenomena and involving comparisons between monolinguals and bilinguals, impaired and unimpaired language learners, early and late acquirers of second languages, and learners experiencing language loss at different ages. Project description: Recent literature in linguistics has witnessed a growing interest in how different components of the grammar formally relate to each other. This research program explores the relationship between phonology and other domains of the grammars of second language learners and bilingual speakers.
Project description : The general goal of the proposed research program is to provide a detailed and systematic cross-linguistic description and analysis of non-canonical types of clausal subordination: i internally headed relative clauses and ii pseudo-relatives. As these constructions do not have direct counterparts in English, English-centered basic compositional mechanisms simply do not seem to work for them. There have been only limited attempts in the literature to better understand their syntactic structure, semantic interpretation, and how to connect the two at the interface level.
This corpus will be made available online to researchers interested in Mayan languages and culture, as well as in a site designed to engage the public in issues surrounding language conservation.
The creation of this corpus will further foster capacity building and collaborative research with native-speaker linguists and trainees in Mexico, the US, and Canada. Game theoretic and probabilistic approaches have led to new insights regarding the dividing line between conventional and conversational meaning, and promise to deliver quantitative predictions about speakers' utterance choices and listeners' interpretation that can fruitfully be related to the statistically interpreted results of controlled experiments.
Yet the field has only begun to fully evaluate the utility of game theoretic and probabilistic approaches in the understanding of conversational meaning. Project description: The objective of this proposal is to create and analyze new set of impressionistic and acoustic phonetic data on variation and change in the language of North American English film and television.
Both variation and change in North American English, among the general population, and film and television language have been extensively studied in the past, the former by sociolinguists and dialectologists and the latter by film critics and media studies scholars outside of Linguistics. By contrast, the interdisciplinary research proposed here will produce the first comprehensive linguistic analysis, using state-of-the-art techniques of acoustic phonetic analysis, of dialect and social variation and change over time in the language of North American film and television.
The resulting data set will be parallel and complementary to the best-known data set on variation and change in North American English among the general population, presented in the Atlas of North American English Labov, Ash and Boberg Such a comparison will enable the P. Project description : This project will create an online database of the Chuj language, an endangered and under-studied Mayan language spoken in the Guatemalan highlands.
Both are related to a fundamental aspect of language, variability. This project will make progress on the questions of how and why sounds vary across languages and over time, focusing on the case of voiced and voiceless consonants, by scaling up relative to previous work: mapping variability in how these sounds are produced across several languages, using automatic measurement algorithms and datasets adapted from speech technology; and using large-scale computational simulations to explain why the pronunciation of these sounds can change over time.
Contributions will be made in three key areas: description of how speech sounds vary across languages, theories of why speech sounds vary over time, and training of students. Project Description: Understanding speech requires decoding multiple dimensions of information that are encoded in the incoming speech stream. Prosodic cues to word and constituent boundaries provide an important way for a listener to parse the signal into meaningful units, and these cues involve both segmental and super segmental changes to phonetic structure.
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There is, however, substantial variability in how prosody is realized, and concomitantly there is substantial variability in the realization of segmental information crucial for lexical access. Our research program aims at developing a model of how prosodic and segmental variability interact in production and how they are processed during on-line language understanding. Project description : Funding to create the Montreal Language Modeling Laboratory in the Department of Linguistics, for computational and empirical investigation of speech sounds.
Project description : Determiners 'the', 'every,'