Dotan Emanuel
Dotan Emanuel works on the Audio biomarkers team in Google research, Tel-Aviv. Prior to this position Dotan managed the cloud network-telemetry and fault detection teams in Google cloud. Before joining Google Dotan held a leading technical position in a few Israeli startups; Genio (news personalization) which was acquired by Somoto, Discretix (mobile security) acquired by ARM, Telmap (mobile navigation) acquired by Intel, Appstream (early SaaS) & Comfy (games for young kids).
Dotan Emanuel received his B.SC and M.Sc in computer science from Tel aviv university 1997 and 2003 respectively, and holds an MBA from Recanati Graduate School of Business, TAU (2008).
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Systematic Data Driven Detection of Unintentional Changes in Traffic Light Plans
Dan Karliner
Eliav Buchnik
Shai Ferster
Tom Kalvari
Omer Litov
Nitzan Tur
Danny Veikherman
Jack Haddad
2024
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Abstract—Traffic light plans determine the time allocated to each movement within an intersection. The plan has high influence on vehicle travel performance such as on the average delay time or the probability to stop in the intersection. Traffic engineers of a city control its traffic lights and can make changes in their plans to improve traffic performance. As it is not always easy to predict the impact of such changes, their potential impact can also be negative. We present an experimental study of real changes in traffic plans in 12 cities with a total of over 12000 intersections within a time period of over 40 days. We focus on changes of the cycle time of plans that highly impacted performance metrics such as delay. We compare the overall impact of such changes and dive into several of them through a careful analysis. To the best of our knowledge, our study is one of the largest in its scope among experimental studies of traffic conditions in recent years.
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QUANTITATIVE APPROACH FOR COORDINATION, AT SCALE, OF SIGNALIZED 2 INTERSECTION PAIRS
Jack Haddad
Nitzan Tur
Danny Veikherman
Eliav Buchnik
Shai Ferster
Tom Kalvari
Dan Karliner
Omer Litov
2024
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The coordination of signalized intersections in urban cities improves both traffic operations and environmental aspects. Traffic signal coordination has a long history, where the impact of offset on delays and emissions at signalized intersections have been investigated through simulations and a limited number of experimental findings. Coordinating intersections is often justified by specific engineering requirements and judgment. However, as a consequence, many intersections in cities remain uncoordinated.
In this paper, we examine the potential benefits of coordinating signalized intersections at scale. Unlike previous studies, our analysis is based on aggregated anonymized probe data analysis and does not need to explicitly model traffic-oriented issues such as queue spillback and platoon dispersion. We follow a decentralized approach by considering intersection pairs, i.e. a system of two signalized intersections which can be spatially coupled, but have different cycle lengths. We introduce a new method for coordinating those signalized intersections. The method first evaluates the effect of different offsets on vehicle travel times and emissions. Then, it coordinates the two intersections by setting a common cycle and finding the optimal offset that minimizes emissions and travel times. We present the analysis for several case studies from real intersections at Jakarta, Rio de Janeiro, Kolkata, and Haifa. Finally, we evaluated our method by implementing it in a real experimental study at Jakarta. We collaborated with the city to implement the optimal offset that we had determined, and we compared the results before and after coordination.
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Computing efficient traffic signal plans is often based on the amount of traffic in an intersection, its distribution over the various intersection movements and hours as well as on performance metrics such as traffic delay. In their simple and typical form plans are fixed in the same hour over weekdays. This allows low operation costs without the necessity for traffic detection and monitoring tools. A critical factor on the potential efficiency of such plans is the similarity of traffic patterns over the days along each of the intersection movements. We refer to such similarity as the traffic stability of the intersection and define simple metrics to measure it based on traffic volume and traffic delay. In this paper, we propose an automatic probe data based method, for city-wide estimation of traffic stability. We discuss how such measures can be used for signal planning such as in selecting plan resolution or as an indication as which intersections can benefit from dynamic but expensive traffic detection tools. We also identify events of major changes in traffic characteristics of an intersection. We demonstrate the framework by using real traffic statistics to study the traffic stability in the city of Haifa along its 162 intersections. We study the impact of the time of day on the stability, detect major changes in traffic and find intersections with high and low stability.
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Shared computational principles for language processing in humans and deep language models
Ariel Goldstein
Zaid Zada
Eliav Buchnik
Amy Price
Bobbi Aubrey
Samuel A. Nastase
Harshvardhan Gazula
Gina Choe
Aditi Rao
Catherine Kim
Colton Casto
Lora Fanda
Werner Doyle
Daniel Friedman
Patricia Dugan
Lucia Melloni
Roi Reichart
Sasha Devore
Adeen Flinker
Liat Hasenfratz
Omer Levy,
Kenneth A. Norman
Orrin Devinsky
Uri Hasson
Nature Neuroscience (2022)
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Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models generate appropriate linguistic responses in a given context. In the current study, nine participants listened to a 30-min podcast while their brain responses were recorded using electrocorticography (ECoG). We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process the same natural narrative: (1) both are engaged in continuous next-word prediction before word onset; (2) both match their pre-onset predictions to the incoming word to calculate post-onset surprise; (3) both rely on contextual embeddings to represent words in natural contexts. Together, our findings suggest that autoregressive DLMs provide a new and biologically feasible computational framework for studying the neural basis of language.
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Towards Learning a Universal Non-Semantic Representation of Speech
Joel Shor
Ronnie Zvi Maor
Omry Tuval
Marco Tagliasacchi
Ira Shavitt
Proc. Interspeech 2020 (2020)
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The ultimate goal of transfer learning is to enable learning with a small amount of data, by using a strong embedding. While significant progress has been made in the visual and language domains, the speech domain does not have such a universal method. This paper presents a new representation of speech signals based on an unsupervised triplet-loss objective, which outperforms both existing state of the art and other representations on a number of transfer learning tasks in the non-semantic speech domain. The embedding is learned on a publicly available dataset, and it is tested on a variety of low-resource downstream tasks, including personalization tasks and medical domain. The model will be publicly released.
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Personalizing ASR for Dysarthric and Accented Speech with Limited Data
Joel Shor
Omry Tuval
Interspeech 2019 (2019)
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Automatic speech recognition (ASR) systems have dramatically
improved over the last few years. ASR systems are most often trained from ‘typical’ speech, which means that underrepresented groups don’t experience the same level of improvement.
In this paper, we present and evaluate finetuning techniques to
improve ASR for users with non standard speech. We focus
on two types of non standard speech: speech from people with
amyotrophic lateral sclerosis (ALS) and accented speech. We
train personalized models that achieve 62% and 35% relative
WER improvement on these two groups, bringing the absolute
WER for ALS speakers, on a test set of message bank phrases,
to 10% for mild dysarthria and 20% for more serious dysarthria.
We show that 76% of the improvement comes from only 5 min
of training data. Finetuning a particular subset of layers (with
many fewer parameters) often gives better results than finetuning the entire model. This is the first step towards building state
of the art ASR models for dysarthric speech
Index Terms: speech recognition, personalization, accessibility
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