Learning task-specific bilexical embeddings

We present a method that learns bilexical operators over distributional representations of words and leverages supervised data for a linguistic relation. The learning algorithm exploits lowrank bilinear forms and induces low-dimensional embeddings of the lexical space tailored for the target linguistic relation. An advantage of imposing low-rank constraints is that prediction is expressed as the inner-product between low-dimensional embeddings, which can have great computational benefits. In experiments with multiple linguistic bilexical relations we show that our method effectively learns using embeddings of a few dimensions.Peer ReviewedPostprint (published version

Reducing Electricity Demand Charge for Data Centers with Partial Execution

Data centers consume a large amount of energy and incur substantial electricity cost. In this paper, we study the familiar problem of reducing data center energy cost with two new perspectives. First, we find, through an empirical study of contracts from electric utilities powering Google data centers, that demand charge per kW for the maximum power used is a major component of the total cost. Second, many services such as Web search tolerate partial execution of the requests because the response quality is a concave function of processing time. Data from Microsoft Bing search engine confirms this observation. We propose a simple idea of using partial execution to reduce the peak power demand and energy cost of data centers. We systematically study the problem of scheduling partial execution with stringent SLAs on response quality. For a single data center, we derive an optimal algorithm to solve the workload scheduling problem. In the case of multiple geo-distributed data centers, the demand of each data center is controlled by the request routing algorithm, which makes the problem much more involved. We decouple the two aspects, and develop a distributed optimization algorithm to solve the large-scale request routing problem. Trace-driven simulations show that partial execution reduces cost by $3\%--10.5\%$ for one data center, and by $15.5\%$ for geo-distributed data centers together with request routing.Comment: 12 page

Why is the Arkavathy River drying? A multiple-hypothesis approach in a data-scarce region

Water planning decisions are only as good as our ability to explain historical trends and make reasonable predictions of future water availability. But predicting water availability can be a challenge in rapidly growing regions, where human modifications of land and waterscapes are changing the hydrologic system. Yet, many regions of the world lack the long-term hydrologic monitoring records needed to understand past changes and predict future trends. We investigated this “predictions under change” problem in the data-scarce Thippagondanahalli (TG Halli) catchment of the Arkavathy sub-basin in southern India. Inflows into TG Halli reservoir have declined sharply since the 1970s. The causes of the drying are poorly understood, resulting in misdirected or counter-productive management responses. Five plausible hypotheses that could explain the decline were tested using data from field surveys and secondary sources: (1) changes in rainfall amount, seasonality and intensity; (2) increases in temperature; (3) groundwater extraction; (4) expansion of eucalyptus plantations; and (5) fragmentation of the river channel. Our results suggest that groundwater pumping, expansion of eucalyptus plantations and, to a lesser extent, channel fragmentation are much more likely to have caused the decline in surface flows in the TG Halli catchment than changing climate

Read, spot and translate

We propose multimodal machine translation (MMT) approaches that exploit the correspondences between words and image regions. In contrast to existing work, our referential grounding method considers objects as the visual unit for grounding, rather than whole images or abstract image regions, and performs visual grounding in the source language, rather than at the decoding stage via attention. We explore two referential grounding approaches: (i) implicit grounding, where the model jointly learns how to ground the source language in the visual representation and to translate; and (ii) explicit grounding, where grounding is performed independent of the translation model, and is subsequently used to guide machine translation. We performed experiments on the Multi30K dataset for three language pairs: English–German, English–French and English–Czech. Our referential grounding models outperform existing MMT models according to automatic and human evaluation metrics

Layer 1-informed Internet Topology Measurement

Understanding the Internet’s topological structure continues to be fraught with challenges. In this paper, we investigate the hypothesis that physical maps of service provider infras-tructure can be used to effectively guide topology discov-ery based on network layer TTL-limited measurement. The goal of our work is to focus layer 3-based probing on broadly identifying Internet infrastructure that has a fixed geographic location such as POPs, IXPs and other kinds of hosting fa-cilities. We begin by comparing more than 1.5 years of TTL-limited probe data from the Ark [25] project with maps of service provider infrastructure from the Internet Atlas [15] project. We find that there are substantially more nodes and links identified in the service provider map data ver-sus the probe data. Next, we describe a new method for probe-based measurement of physical infrastructure called POPsicle that is based on careful selection of probe source-destination pairs. We demonstrate the capability of our method through an extensive measurement study using ex-isting “looking glass ” vantage points distributed throughout the Internet and show that it reveals 2.4 times more phys-ical node locations versus standard probing methods. To demonstrate the deployability of POPsicle we also conduct tests at an IXP. Our results again show that POPsicle can identify more physical node locations compared with stan-dard layer 3 probes, and through this deployment approach it can be used to measure thousands of networks world wide

Deoxynivalenol and its toxicity

Deoxynivalenol (DON) is one of several mycotoxins produced by certain Fusarium species that frequently infect corn, wheat, oats, barley, rice, and other grains in the field or during storage. The exposure risk to human is directly through foods of plant origin (cereal grains) or indirectly through foods of animal origin (kidney, liver, milk, eggs). It has been detected in buckwheat, popcorn, sorgum, triticale, and other food products including flour, bread, breakfast cereals, noodles, infant foods, pancakes, malt and beer. DON affects animal and human health causing acute temporary nausea, vomiting, diarrhea, abdominal pain, headache, dizziness, and fever. This review briefly summarizes toxicities of this mycotoxin as well as effects on reproduction and their antagonistic and synergic actions