AutoSense Model for Word Sense Induction

Word sense induction (WSI), or the task of automatically discovering multiple senses or meanings of a word, has three main challenges: domain adaptability, novel sense detection, and sense granularity flexibility. While current latent variable models are known to solve the first two challenges, they are not flexible to different word sense granularities, which differ very much among words, from aardvark with one sense, to play with over 50 senses. Current models either require hyperparameter tuning or nonparametric induction of the number of senses, which we find both to be ineffective. Thus, we aim to eliminate these requirements and solve the sense granularity problem by proposing AutoSense, a latent variable model based on two observations: (1) senses are represented as a distribution over topics, and (2) senses generate pairings between the target word and its neighboring word. These observations alleviate the problem by (a) throwing garbage senses and (b) additionally inducing fine-grained word senses. Results show great improvements over the state-of-the-art models on popular WSI datasets. We also show that AutoSense is able to learn the appropriate sense granularity of a word. Finally, we apply AutoSense to the unsupervised author name disambiguation task where the sense granularity problem is more evident and show that AutoSense is evidently better than competing models. We share our data and code here: https://github.com/rktamplayo/AutoSense.Comment: AAAI 201

Asymmetric-valued Spectrum Auction and Competition in Wireless Broadband Services

We study bidding and pricing competition between two spiteful mobile network operators (MNOs) with considering their existing spectrum holdings. Given asymmetric-valued spectrum blocks are auctioned off to them via a first-price sealed-bid auction, we investigate the interactions between two spiteful MNOs and users as a three-stage dynamic game and characterize the dynamic game's equilibria. We show an asymmetric pricing structure and different market share between two spiteful MNOs. Perhaps counter-intuitively, our results show that the MNO who acquires the less-valued spectrum block always lowers his service price despite providing double-speed LTE service to users. We also show that the MNO who acquires the high-valued spectrum block, despite charing a higher price, still achieves more market share than the other MNO. We further show that the competition between two MNOs leads to some loss of their revenues. By investigating a cross-over point at which the MNOs' profits are switched, it serves as the benchmark of practical auction designs

Evaluation and control of mechanical degradation of austenitic stainless 310S steel substrate during coated superconductor processing

The superconductor industry considers cold-rolled austenitic stainless 310S steel a less expensive substitute for Hastelloy X as a substrate for coated superconductor. However, the mechanical properties of cold-rolled 310S substrate degrade significantly in the superconductor deposition process. To overcome this, we applied hot rolling at 900 A degrees C (or 1000 A degrees C) to the 310S substrate. To check the property changes, a simulated annealing condition equivalent to that used in manufacturing was determined and applied. The effects of the hot rolling on the substrate were evaluated by analyzing its physical properties and texture.Web of Science24345444

Naphthalimide Trifluoroacetyl Acetonate: A Hydrazine-Selective Chemodosimetric Sensor

The trifluoroacetyl acetonate naphthalimide derivative 1 has been synthesized in good yield. In acetonitrile solution, compound 1 reacts selectively with hydrazine (NH2NH2) to give a five-membered ring. This leads to OFF-ON fluorescence with a maximum intensity at 501 nm as well as easily discernible color changes. Based on a readily discernible and reproducible 3.9% change in overall fluorescence intensity, the limit of detection for 1 is 3.2 ppb (0.1 mu M), which is below the accepted limit for hydrazine set by the U.S. Environmental Protection Agency (EPA). Compound 1 is selective for hydrazine over other amines, including NH4OH, NH2OH, ethylenediamine, methylamine, n-butylamine, piperazine, dimethylamine, triethylamine, pyridine, and is not perturbed by environmentally abundant metal ions. When supported on glass-backed silica gel TLC plates, compound 1 acts as a fluorimetric and colorimetric probe for hydrazine vapor at a partial pressure of 9.0 mm Hg, with selectivity over other potentially interfering volatile analytes, including ammonia, methylamine, n-butylamine, formaldehyde, acetaldehyde, H2O2, HCl, and CO2 being observed. Probe 1 can also be used for the detection of hydrazine in HeLa cells and does so without appreciable interference from other biologically abundant amines and metal ions.U.S. National Science Foundation CHE-1057904Robert A. Welch Foundation F-1018CRI project grant from National Research Foundation of Korea (NRF)Korea government (MSIP) 2009-0081566Chemistr

Enhancement on Radon Adsorption Property of GAC using Nano-size Carbon Colloids

Granular activated carbon (GAC) is well-known as an efficient adsorbent against a number of gaseous pollutants. Radon is one of those pollutants, and radon has been classified as the second leading cause of lung cancer in USA. This study was to enhance the radon removal efficiency with applying nano-technology. Nano-size carbon colloids (NCC) was produced through electrolysis which is simple and cheap. NCC was used for impregnation with activated carbon. Surface areas of both NCC-treated and non-treated activated carbon did not show a significant difference. However, the results of radon removal efficiency show that impregnated carbon with NCC could capture about 1.3 ~ 2 times of more radon gas compared to non-treated activated carbon. It is assumed that nano-size carbon colloids might have filled up meso-pores, and meso-pores turned into micro-pores eventually. Because meso-pores initially accounted for large portion of whole pores, more radon could be captured to NCC-impregnated activated carbon. Keywords: Radon, Nano-Size Carbon Collid, Activated Carbo