The Standard Model of the electroweak interaction is modified to include the Stueckelburg-Feynman parameter. The parametrized Higgs potential (7) and parametrized Higgs to fermion couplings such as (18) yield every feature of the model. They also pa…
The formulation of quantum electrodynamics as parametrized relativistic quantum mechanics [Ann. Phys. {\bf 345} (2014) 1-16] is extended here to The …
Open category detection is the problem of detecting "alien" test instances that belong to categories or classes that were not present in the training…
In this paper, we propose new location privacy preserving schemes for database-driven cognitive radio networks that protect secondary users' (SUs) lo…
In this work we examine the dispersion of conservative tracers (bromide and fluorescein) in an experimentally-constructed three-dimensional dual-poro…
We present a new algorithm for identifying the transition and emission probabilities of a hidden Markov model (HMM) from the emitted data. Expectatio…
Energy harvesting emerges as a potential solution for prolonging the lifetime of the energy-constrained mobile wireless devices. In this paper, we fo…
Sentence-level classification and sequential labeling are two fundamental tasks in language understanding. While these two tasks are usually modeled …
Machine learning applied to architecture design presents a promising opportunity with broad applications. Recent deep reinforcement learning (DRL) te…
The 3GPP suggests to combine dual polarized (DP) antenna arrays with the double directional (DD) channel model for downlink channel estimation. This …
The data deluge comes with high demands for data labeling. Crowdsourcing (or, more generally, ensemble learning) techniques aim to produce accurate l…
Recently, neural network approaches for parsing have largely automated the combination of individual features, but still rely on (often a larger numb…
Some researchers have speculated that capable reinforcement learning (RL) agents pursuing misspecified objectives are often incentivized to seek reso…
In 2005 DARPA labeled the realization of viable autonomous vehicles (AVs) a grand challenge; a short time later the idea became a moonshot that could…
Large urban communication networks such as smart cities are an ecosystem of devices and services cooperating to address multiple issues that greatly …
Recently, several networks that operate directly on point clouds have been proposed. There is significant utility in understanding them better, so th…
We investigated the problem of learning directly over heterogeneous data and proposed a new method that leverages constraints in learning to represent inconsistencies. Since most of these quality problems have been modeled using declarative constrai…
Real-world datasets are dirty and contain many errors. Examples of these issues are violations of integrity constraints, duplicates, and inconsistenc…
There have been significant efforts to interpret the encoder of Transformer-based encoder-decoder architectures for neural machine translation (NMT);…
We study an approach to offline reinforcement learning (RL) based on optimally solving finitely-represented MDPs derived from a static dataset of exp…
Deep Reinforcement Learning (DRL) algorithms have been successfully applied to a range of challenging control tasks. However, these methods typically…
This paper addresses 3D shape recognition. Recent work typically represents a 3D shape as a set of binary variables corresponding to 3D voxels of a u…
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