Model-Based Cross-Correlation Search for Gravitational Waves from Scorpius X-1

We consider the cross-correlation search for periodic GWs and its potential application to the LMXB Sco X-1. This method coherently combines data from different detectors at the same time, as well as different times from the same or different detectors. By adjusting the maximum time offset between a pair of data segments to be coherently combined, one can tune the method to trade off sensitivity and computing costs. In particular, the detectable signal amplitude scales as the inverse fourth root of this coherence time. The improvement in amplitude sensitivity for a search with a coherence time of 1hr, compared with a directed stochastic background search with 0.25Hz wide bins is about a factor of 5.4. We show that a search of 1yr of data from Advanced LIGO and Advanced Virgo with a coherence time of 1hr would be able to detect GWs from Sco X-1 at the level predicted by torque balance over a range of signal frequencies from 30-300Hz; if the coherence time could be increased to 10hr, the range would be 20-500Hz. In addition, we consider several technical aspects of the cross-correlation method: We quantify the effects of spectral leakage and show that nearly rectangular windows still lead to the most sensitive search. We produce an explicit parameter-space metric for the cross-correlation search in general and as applied to a neutron star in a circular binary system. We consider the effects of using a signal template averaged over unknown amplitude parameters: the search is sensitive to a combination of the intrinsic signal amplitude and the inclination of the neutron star rotation axis, and the peak of the expected detection statistic is systematically offset from the true signal parameters. Finally, we describe the potential loss of SNR due to unmodelled effects such as signal phase acceleration within the Fourier transform timescale and gradual evolution of the spin frequency.Comment: 27 pages, 12 figures, 4 tables, pdflatex; synchronized to final version published in Phys Rev

Vistaar: Diverse Benchmarks and Training Sets for Indian Language ASR

Improving ASR systems is necessary to make new LLM-based use-cases accessible to people across the globe. In this paper, we focus on Indian languages, and make the case that diverse benchmarks are required to evaluate and improve ASR systems for Indian languages. To address this, we collate Vistaar as a set of 59 benchmarks across various language and domain combinations, on which we evaluate 3 publicly available ASR systems and 2 commercial systems. We also train IndicWhisper models by fine-tuning the Whisper models on publicly available training datasets across 12 Indian languages totalling to 10.7K hours. We show that IndicWhisper significantly improves on considered ASR systems on the Vistaar benchmark. Indeed, IndicWhisper has the lowest WER in 39 out of the 59 benchmarks, with an average reduction of 4.1 WER. We open-source all datasets, code and models.Comment: Accepted in INTERSPEECH 202

Geospatial technologies and climate change

'Geospatial Technologies and Climate Change' is a scholarly compilation of seventeen chapters from researchers working on climate change related research in five countries of four continents. Geospatial technologies, synergetic applications of remote sensing and geographical information systems, offer versatile cross-scale tools to study climate change, the climate system's changes over decades, and their impacts on social- and ecological systems. A wide variety of climate change applications and the most advanced tools for climate change research are presented in this volume. The detailed treatment of the topic is framed in the paradigm of spatial planning for mitigation and adaptation. Through multifunctional and flexible thinking the authors investigate the dynamics of natural systems and suggest planning ahead for longer terms, as changes of the climate unfold only over longer periods. The book argues that technological innovations for climate change mitigation and adaptation should begin locally. Three strands of spatially defined climate change research come together in this volume. The first part explores geospatial technologies as assessment tools that play important roles in scoping and monitoring climate change impacts. The second part reviews geospatial technologies as decision support tools applied in planning for adaptation and mitigation. The third part provides an introduction to the basics of geospatial technologies and uncovers their technical potential in advanced climate change research. Designed for students, academics and decision-makers, the volume accounts for the leading currents of thought in applying geospatial technologies in climate change research and adaptation. By demonstrating how diversity of discovery methods can broaden our knowledge, from design charettes through hands-on engagement with the local environment to interpreting satellite imagery, the authors emphasize the importance of inter-disciplinary approaches in addressing uncertainties over climate change. The broad and fresh perspectives of the authors make this volume an invaluable guide in innovative application of geospatial technologies in climate change research

Neighbor oblivious and finite-state algorithms for circumventing local minima in geographic forwarding

We propose distributed link reversal algorithms to circumvent communication voids in geographic routing. We also solve the attendant problem of integer overflow in these algorithms. These are achieved in two steps. First, we derive partial and full link reversal algorithms that do not require one-hop neighbor information, and convert a destination-disoriented directed acyclic graph (DAG) to a destination-oriented DAG. We embed these algorithms in the framework of Gafni and Bertsekas 1] in order to establish their termination properties. We also analyze certain key properties exhibited by our neighbor oblivious link reversal algorithms, e.g., for any two neighbors, their t-states are always consecutive integers, and for any node, its t-state size is upper bounded by log(N). In the second step, we resolve the integer overflow problem by analytically deriving one-bit full link reversal and two-bit partial link reversal versions of our neighbor oblivious link reversal algorithms. We also discuss the work and time complexities of the proposed algorithms. (C) 2016 Elsevier B.V. All rights reserved