1,721,142 research outputs found
Elevated residential exposure to power frequency magnetic field associated with greater average age at diagnosis for patients with brain tumors
No full textTo explore whether the age at cancer diagnosis was associated with residential exposure to magnetic field, we compared average ages at diagnosis for cases of leukemia, brain tumor, or female breast cancer with elevated exposure( magnetic flux density greater than or equal to 0.2 muT, or residential distance from major power lines less than or equal to 100 m) to average ages at diagnosis for cancer cases with same diagnoses but with a background exposure ( 100 m from major power lines). Comparing with brain tumor cases with background magnetic field exposure (n = 506), brain tumor cases with elevated exposure (n = 7 1) were 6 years older on average at diagnosis (P = 0.01). The difference was greater for males (45.2 vs. 52.1 years, P = 0 .01) than for females (44.3 vs. 48.2 years, P = 0.27). No such phenomena at a significant level was observed for leukemia, female breast cancer, or a random sample of general population. We noted an association between magnetic field exposure and a greater mean age at diagnosis for brain tumors. Whether or not these phenomena suggest a delayed occurrence of brain tumors following a higher than background residential magnetic field exposure deserves further investigation. (C) 2003 Wiley-Liss, Inc
Matching Users and Items for Transfer Learning in Collaborative Filtering
No full text本篇論文的研究目標是具有類似使用者和類似物品的同質性評分矩陣：即使不知道使用者的匹配關係和物品的匹配關係時，是否仍能在矩陣間進行轉移學習。更精確地說，我們假設有兩個評分矩陣，它們表達了同樣的喜好，而且兩個評分矩陣的使用者集合、物品集合都有很大一部份是重疊的。我們的目標便是找出這些使用者的匹配關係和物品的匹配關係，進而利用這樣的關係把一個矩陣的資訊傳遞到另一個矩陣上並改善其評分預測。
為了解出對應關係，我們會將稀疏的大型評分矩陣用低秩矩陣來近似，並用分解出來的因子來辨認使用者和物品。在解匹配問題的演算法中，一開始會把因子轉為奇異值分解的形式，並執行近鄰演算法。之後我們會指出奇異值分解的缺點，並用另一個目標函數來修正結果，以獲得更準確的匹配關係。最後，我們修改了協同式過濾常用的矩陣分解模型，使其能利用解出的匹配關係連結兩個矩陣，並做評分預測。我們的實驗顯示，在匹配問題中，我們能得到相當準的解。而即便匹配問題得到的解並不完美，我們仍能用其來改善評分預測模型。This paper investigates the possibility of transferring information between homogeneous datasets of similar users and items but both user correspondence and item correspondence are unknown. More specifically, we assume there are two rating matrices that model the same kind of preferences, and there is a significant degree of overlap between the two user sets and between the two item sets. Our goal is to find out the user correspondence and item correspondence between the two rating matrices, and utilize the correspondence for exploiting the information of one matrix to improve the quality of rating prediction in the other matrix.
For finding out the correspondence, we factorize both rating matrices and exploit the latent factors to identify the users and items. The algorithm for solving the correspondence is initially based on singular value decomposition and nearest neighbor search, and then we point out the drawbacks of singular value decomposition and use another formulation to refine its result. Finally, we introduce a simple modification of regular matrix factorization model for transferring information across matrices with the obtained correspondence. In our experiment, we show that it is possible to solve the correspondence with decently high accuracy, and even with non-perfect correspondence obtained from our method, it is still possible to improve the quality of rating prediction.致謝ii
中文摘要iii
Abstract iv
1 Introduction 1
2 Related Work 4
2.1 Transfer Learning in Collaborative Filtering Given Correspondence . . . 4
2.2 Transfer Learning in Collaborative Filtering When Correspondence is
Unknown . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3 Methodology 8
3.1 Solving the Correspondence Problem . . . . . . . . . . . . . . . . . . . 8
3.2 Matching from Singular Value Decomposition . . . . . . . . . . . . . . . 9
3.2.1 The Idea of Singular Value Decomposition . . . . . . . . . . . . 9
3.2.2 From Matrix Factorization to Singular Value Decomposition . . . 9
3.2.3 Objective Function of SVD Matching . . . . . . . . . . . . . . . 11
3.2.4 Remarks on SVD Matching . . . . . . . . . . . . . . . . . . . . 13
3.3 Refined Search Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.4 Rating Prediction Given Noisy Matching . . . . . . . . . . . . . . . . . . 14
4 Experiment 16
4.1 Experiment Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.2 Implementation Details . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.3 Experiment Result of User and Item Matching . . . . . . . . . . . . . . . 18
4.4 Experiment Result for Rating Prediction . . . . . . . . . . . . . . . . . . 19
5 Conclusion and Future Work 21
Reference 2
Occupational Exposures of Pharmacists and Pharmaceutical Assistants to 60 Hz Magnetic Fields
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Mechanisms of Social Interaction and Virtual Connections as Strong Predictors of Wellbeing of Older Adults
No full textArticle examining the domains of social determinants of health, specifically the social and community context per Healthy People 2030 objectives. These mechanisms of social interaction, in the form of group activities, community engagement, and virtual interactions via email or text message, were assessed using hierarchical regression analysis to find out their association with wellbeing, depression symptoms, and cognition of older adults
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