Predicting Role Relevance with Minimal Domain Expertise in a Financial Domain

Word embeddings have made enormous inroads in recent years in a wide variety of text mining applications. In this paper, we explore a word embedding-based architecture for predicting the relevance of a role between two financial entities within the context of natural language sentences. In this extended abstract, we propose a pooled approach that uses a collection of sentences to train word embeddings using the skip-gram word2vec architecture. We use the word embeddings to obtain context vectors that are assigned one or more labels based on manual annotations. We train a machine learning classifier using the labeled context vectors, and use the trained classifier to predict contextual role relevance on test data. Our approach serves as a good minimal-expertise baseline for the task as it is simple and intuitive, uses open-source modules, requires little feature crafting effort and performs well across roles.Comment: DSMM 2017 workshop at ACM SIGMOD conferenc

The effective temperature for the thermal fluctuations in hot Brownian motion

We revisit the effective parameter description of hot Brownian motion -- a scenario where a colloidal particle is kept at an elevated temperature than the ambient fluid. Due to the time scale separation between heat diffusion and particle motion, a stationary halo of hot fluid is carried along with the particle, resulting in a spatially varying comoving temperature and viscosity profile. The resultant Brownian motion in the overdamped limit can be well described by a Langevin equation with effective parameters such as effective temperature $T_{\rm HBM}$ and friction coefficient $\zeta_{\rm HBM}$ that quantifies the thermal fluctuations and the diffusivity of the particle. These parameters can exactly be calculated using the framework of fluctuating hydrodynamics. Additionally, it was also observed that configurational and the kinetic degrees of freedom admits to different effective temperatures, $T^{\mathbf{x}}_{\rm HBM}$ and $T^{\mathbf{v}}_{\rm HBM}$, respectively, with the former predicted accurately from fluctuating hydrodynamics. A more rigorous calculation by Falasco et. al. Physical Review E , 90, $032131(2014)$ extends the overdamped description to a generalized Langevin equation where the effective temperature becomes frequency dependent and consequently, for any temperature measurement from a Brownian trajectory requires the knowledge of this frequency dependence. We use this framework to expand on this earlier work and look at the first order correction to the effective temperature. The effective temperature is calculated from the weighted average of the temperature field with the dissipation function. Further, we provide a closed form analytical result for effective temperature in the small as well high frequency limit and using this we determine the kinetic temperature from the generalized Langevin equation and the Wiener-Khinchine theorem.Comment: 9 pages, 4 figure

On feedback in network source coding

We consider source coding over networks with unlimited feedback from the sinks to the sources. We first show examples of networks where the rate region with feedback is a strict superset of that without feedback. Next, we find an achievable region for multiterminal lossy source coding with feedback. Finally, we evaluate this region for the case when one of the sources is fully known at the decoder and use the result to show that this region is a strict superset of the best known achievable region for the problem without feedback

On Zero-Error Source Coding with Feedback

We consider the problem of zero error source coding with limited feedback when side information is present at the receiver. First, we derive an achievable rate region for arbitrary joint distributions on the source and the side information. When all source pairs of source and side information symbols are observable with non-zero probability, we show that this characterization gives the entire rate region. Next, we demonstrate a class of sources for which asymptotically zero feedback suffices to achieve zero-error coding at the rate promised by the Slepian-Wolf bound for asymptotically lossless coding. Finally, we illustrate these results with the aid of three simple examples