Improving Term Frequency Normalization for Multi-topical Documents, and Application to Language Modeling Approaches

Term frequency normalization is a serious issue since lengths of documents are various. Generally, documents become long due to two different reasons - verbosity and multi-topicality. First, verbosity means that the same topic is repeatedly mentioned by terms related to the topic, so that term frequency is more increased than the well-summarized one. Second, multi-topicality indicates that a document has a broad discussion of multi-topics, rather than single topic. Although these document characteristics should be differently handled, all previous methods of term frequency normalization have ignored these differences and have used a simplified length-driven approach which decreases the term frequency by only the length of a document, causing an unreasonable penalization. To attack this problem, we propose a novel TF normalization method which is a type of partially-axiomatic approach. We first formulate two formal constraints that the retrieval model should satisfy for documents having verbose and multi-topicality characteristic, respectively. Then, we modify language modeling approaches to better satisfy these two constraints, and derive novel smoothing methods. Experimental results show that the proposed method increases significantly the precision for keyword queries, and substantially improves MAP (Mean Average Precision) for verbose queries.Comment: 8 pages, conference paper, published in ECIR '0

Therapeutic potentials of hypoxic- and baicalein-enriched fraction-preconditioned human neural stem cells for in vitro ischemic stroke model

Ischemic stroke is the third leading cause of death in Malaysia, closely after heart disease and cancer. Standard treatments for stroke are not totally efficient to repair and regenerate the damaged brain tissue and there are possibilities for the recurrence. Replacement by endogenous adult neural stem cells (NSCs) during ischemic stroke was insufficient to repair injury site due to low neuronal turnover that could integrate into functional neuron network. Therefore, it is imperative to develop alternative therapeutic strategies to improve stroke recovery. Recently, human NSC grafting has emerged as encouraging approach for treating stroke. Nonetheless, the therapeutic potential of NSC-based treatment is limited, mainly due to a large number of implanted cells died after grafting into the injury site. To circumvent this problem, this study aimed to enhance therapeutic potentials of human NSCs prior to transplantation through hypoxic and baicalein-enriched fraction (F5) preconditioning. Hypoxic preconditioning under 2% O2 for 24 h enhanced NSC self-renewal, survival and multipotency. 60S ribosomal protein large P1 (RPLP1) and ribosomal protein L13A (RPL13A) were the most reliable reference genes for qPCR normalization of normoxic- and hypoxic-preconditioned NSCs. Hypoxic preconditioning induced innate neuroprotective signaling through transcriptional activation of hypoxia-inducible factor-1 alpha (HIF-1α), vascular endothelial growth factor A (VEGFA), angiopoietin 1 (ANGPT1), neurogenic locus notch homolog protein 1 (Notch 1), nuclear factor erythroid 2-related factor 2 (Nrf2) and sodium dismutase 1 (SOD1). Based on the HIF-1α stabilization potential of baicalein at ambient conditions, F5 was postulated to trigger effects mimic hypoxic preconditioning under normoxia. Interestingly, preconditioning with 1.56 μg/mL of F5 for 24 h increased NSC proliferation, viability and lineage specific differentiation. Hypoxanthine phosphoribosyl transferase 1 (HPRT1) and RPL13A were the most stably expressed reference genes for qPCR normalization of control (0.1% DMSO) and F5-preconditioned NSCs. Moreover, F5 preconditioning stimulated hypoxiamimetic signaling intrinsically via HIF-1α, VEGFA, ANGPT1, Notch 1, Nrf2 and SOD1 upregulation. Both hypoxic- and F5-preconditioned NSCs were applied to in vitro ischemic stroke (IVIS) model on wound-healing based culture slide for 72 h of live imaging. F5-preconditioned NSCs accelerated migration and homing towards IVIS model over an experimental period of 72 h compared to hypoxicpreconditioned NSCs. The neuroprotective factors induced by hypoxic preconditioning are postulated to degrade rapidly when exposed to oxygen. Contrarily, F5-preconditioned NSCs attained intrinsic neuroprotective mechanisms without compromising their stability under normoxia. In conclusion, both the hypoxic and F5 preconditioning had successfully enhanced therapeutic potentials of NSCs for ischemic stroke. F5-preconditioned NSCs with enhanced therapeutic efficacy was more likely to be applicable in clinical setting and thus could be a promising therapeutic tool for ischemic stroke in the future

The value of relationship banking during financial crises : evidence from the Republic of Korea

A systemic financial crisis with monetary restriction is probably the most promising occasion for assessing whether, and to what extent, relationship banking is valuable to borrowers. The authors take this question to a unique database of credit bureau, microeconomic information covering the pervasive financial crisis the Republic of Korea experienced in 1997-98. The database includes all corporate borrowers surveyed by the Korean Credit Bureau, providing details on the structure of their borrowings, and on their relationship with lending banks. The authors did not have access to the identity of the corporate borrower, and their only non-financial control variable was the borrower's Standard Industrial Classification (SIC). This restriction limited their analysis to smaller borrowers, keeping their sample focused on small, and medium-size enterprises, which were likely to rely on banks for external financing. Their findings: 1) Outstanding loans plunge more for firms with weaker pre-crisis relationship banking. 2) The drop in credit lines - arguably a proxy identifying shifts in the loan supply - is larger for firms relying less on strong relationship banking. 3) More intense pre-crisis relationship banking reduces the probability that a previously non-delinquent firm would build (increase) its loans in arrears in 1998, the year of the sharpest liquidity constraints. 4) All things equal, this probability depends on whether firms were borrowing from one (or more) of the five banks foreclosed in June 1998, showing that it might be particularly difficult for borrowers to replace distressed lending banks during a financial crisis. The authors'findings support the hypothesis that relationship banking = with surviving banks - has a positive value during a systemic financial crisis. They argue that for many viable small, and medium-size businesses in Korea, relationship banking reduced liquidity constraints, and thus, diminished the probability of unwarranted bankruptcy.Financial Intermediation,Banks&Banking Reform,Financial Crisis Management&Restructuring,Economic Adjustment and Lending,Housing Finance

Topics In Forward Stepwise Logistic Regression

In this dissertation, five topics related to the process and prediction of forward stepwise logistic regression are investigated.;Forward stepwise logistic regression is involved with selection and stopping criteria. Seven selection criteria are used: the likelihood ratio statistic, Lawless and Singhal (1978)\u27s statistic, the Wald statistic, the score statistic, Peduzzi, Hardy, and Holford (1980)\u27s statistic, Lee and Koval\u27s statistic (LK), and a sweep operator\u27s statistic (SW). Five stopping criteria are used: {dollar}\chi\sp2{dollar} test based on a fixed {dollar}\alpha{dollar} level, minimum value of ERR, minimum value of the C{dollar}\sb{lcub}\rm p{rcub}{dollar} statistic (Hosmer, 1989), minimum value of the Akaike information criterion (Akaike, 1974), and minimum value of Schwarz\u27s criterion (Schwarz, 1978).;Apparent error tate (ARR) tends to underestimate true error rate (ERR). In our study, estimated true error rate (ERR) is obtained by ERR = ARR + {dollar}\\omega{dollar}, where {dollar}\\omega{dollar} is from Efron (1986)\u27s parametric estimate of bias for ARR.;We use Monte Carlo simulation with both multivariate normal and multivariate binary independent variables; we implement the simulation with SAS/IML programs. We then analyze the experimental design to see which factors of the distribution of independent variables affect various outcomes.;As a result, we recommend the best {dollar}\alpha{dollar} level for the {dollar}\chi\sbsp{lcub}(\alpha){rcub}{lcub}2{rcub}{dollar} stopping criterion. Second, we compare the order of variables selected by different selection criteria. Third, we investigate the effects of different structures of predictor variables on ARR, {dollar}\\omega{dollar}, and ERR. Fourth, we compare the sizes of subset models determined by different stopping criteria. Finally, we compare the performances of selection and stopping criteria in terms of ERR

Towards Secure Blockchain-enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory

In Internet of Vehicles (IoV), data sharing among vehicles is essential to improve driving safety and enhance vehicular services. To ensure data sharing security and traceability, highefficiency Delegated Proof-of-Stake consensus scheme as a hard security solution is utilized to establish blockchain-enabled IoV (BIoV). However, as miners are selected from miner candidates by stake-based voting, it is difficult to defend against voting collusion between the candidates and compromised high-stake vehicles, which introduces serious security challenges to the BIoV. To address such challenges, we propose a soft security enhancement solution including two stages: (i) miner selection and (ii) block verification. In the first stage, a reputation-based voting scheme for the blockchain is proposed to ensure secure miner selection. This scheme evaluates candidates' reputation by using both historical interactions and recommended opinions from other vehicles. The candidates with high reputation are selected to be active miners and standby miners. In the second stage, to prevent internal collusion among the active miners, a newly generated block is further verified and audited by the standby miners. To incentivize the standby miners to participate in block verification, we formulate interactions between the active miners and the standby miners by using contract theory, which takes block verification security and delay into consideration. Numerical results based on a real-world dataset indicate that our schemes are secure and efficient for data sharing in BIoV.Comment: 12 pages, submitted for possible journal publicatio

Effect Of Changes In The Korean Accounting Environment On The Productivity Of Accounting Firms

To investigate how changes in the accounting environment in Korea affect firm productivity, this study analyzes productivity by firm size and labor type from 2000 to 2014, using a Cobb–Douglas production function. We find that (1) the greater the management advisory (tax) revenue, the greater the total revenue in large (small) accounting firms; and (2) marginal revenue is greatest for partners, followed by certified public accountants and general employees. In particular, partners’ contribution to large accounting firms improved after 2007, whereas general employees made a significant positive contribution to total revenue before 2007

Personalized Cinemagraphs using Semantic Understanding and Collaborative Learning

Cinemagraphs are a compelling way to convey dynamic aspects of a scene. In these media, dynamic and still elements are juxtaposed to create an artistic and narrative experience. Creating a high-quality, aesthetically pleasing cinemagraph requires isolating objects in a semantically meaningful way and then selecting good start times and looping periods for those objects to minimize visual artifacts (such a tearing). To achieve this, we present a new technique that uses object recognition and semantic segmentation as part of an optimization method to automatically create cinemagraphs from videos that are both visually appealing and semantically meaningful. Given a scene with multiple objects, there are many cinemagraphs one could create. Our method evaluates these multiple candidates and presents the best one, as determined by a model trained to predict human preferences in a collaborative way. We demonstrate the effectiveness of our approach with multiple results and a user study.Comment: To appear in ICCV 2017. Total 17 pages including the supplementary materia

Effective Route Maintenance and Restoration Schemes in Mobile Ad Hoc Networks

This study proposes a location-based hybrid routing protocol to improve data packet delivery and to reduce control message overhead in mobile ad hoc networks. In mobile environments, where nodes move continuously at a high speed, it is generally difficult to maintain and restore route paths. Therefore, this study suggests a new flooding mechanism to control route paths. The essence of the proposed scheme is its effective tracking of the destination’s location based on the beacon messages of the main route nodes. Through experiments based on an NS-2 simulator, the proposed scheme shows improvements in the data packet delivery ratio and reduces the amount of routing control message overhead compared with existing routing protocols such as AODV, LAR, ZRP and AODV-DFR