178,236 research outputs found
INTERGENERATIONAL MOBILITY and SOCIAL STATUS in A MODEL with HUMAN CAPITAL INVESTMENTS and TRAIT INHERITANCE
No full textWe study a model in which parents care about the economic and social status of their offspring. The chances of an individual achieving social status depends on innate traits, that is, IQ, ability, social and cultural environment, and other price-insensitive endowments, passed on by their parents, on human capital investments and on chance events. Parents can, through human capital investments, increase the offspring's probability of climbing the social ladder, although they cannot borrow against the children's perspective earning. Consequently, income and trait heterogeneity are the determinants of unequal opportunities and of intergenerational mobility
Fast Filtering of Search Results Sorted by Attribute
No full textModern search services often provide multiple options to rank the search results, e.g., sort "by relevance", "by price"or "by discount"in e-commerce. While the traditional rank by relevance effectively places the relevant results in the top positions of the results list, the rank by attribute could place many marginally relevant results in the head of the results list leading to poor user experience. In the past, this issue has been addressed by investigating the relevance-aware filtering problem, which asks to select the subset of results maximizing the relevance of the attribute-sorted list. Recently, an exact algorithm has been proposed to solve this problem optimally. However, the high computational cost of the algorithm makes it impractical for the Web search scenario, which is characterized by huge lists of results and strict time constraints. For this reason, the problem is often solved using efficient yet inaccurate heuristic algorithms. In this article, we first prove the performance bounds of the existing heuristics. We then propose two efficient and effective algorithms to solve the relevance-aware filtering problem. First, we propose OPT-Filtering, a novel exact algorithm that is faster than the existing state-of-the-art optimal algorithm. Second, we propose an approximate and even more efficient algorithm, -Filtering, which, given an allowed approximation error , finds a (1-)-optimal filtering, i.e., the relevance of its solution is at least (1-) times the optimum. We conduct a comprehensive evaluation of the two proposed algorithms against state-of-the-art competitors on two real-world public datasets. Experimental results show that OPT-Filtering achieves a significant speedup of up to two orders of magnitude with respect to the existing optimal solution, while -Filtering further improves this result by trading effectiveness for efficiency. In particular, experiments show that -Filtering can achieve quasi-optimal solutions while being faster than all state-of-the-art competitors in most of the tested configurations
Learning bivariate scoring functions for ranking
No full textState-of-the-art Learning-to-Rank algorithms, e.g., λMART, rely on univariate scoring functions to score a list of items. Univariate scoring functions score each item independently, i.e., without considering the other available items in the list. Nevertheless, ranking deals with producing an effective ordering of the items and comparisons between items are helpful to achieve this task. Bivariate scoring functions allow the model to exploit dependencies between the items in the list as they work by scoring pairs of items. In this paper, we exploit item dependencies in a novel framework—we call it the Lambda Bivariate (LB) framework—that allows to learn effective bivariate scoring functions for ranking using gradient boosting trees. We discuss the three main ingredients of LB: (i) the invariance to permutations property, (ii) the function aggregating the scores of all pairs into the per-item scores, and (iii) the optimization process to learn bivariate scoring functions for ranking using any differentiable loss functions. We apply LB to the λRank loss and we show that it results in learning a bivariate version of λMART—we call it Bi-λMART—that significantly outperforms all neural-network-based and tree-based state-of-the-art algorithms for Learning-to-Rank. To show the generality of LB with respect to other loss functions, we also discuss its application to the Softmax loss
Cycle-trend Dynamics in a Fixwage Neo-Austrian Model of Traverse
No full textThis paper analyses a neo-Austrian growth model under a new assumption on the evolution of the extrawage consumption, which is supposed to depend on a suitable wealth index of the system. In this contest the traverse path turns out to be two fold: in the short run it exhibits an upward jump which primes a series of cyclical oscillations while in the long run it converges towards a new steady state
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Efficient and Effective Multi-Vector Dense Retrieval with EMVB
No full textDense retrieval techniques utilize large pre-trained language models to construct a high-dimensional representation of queries and passages. These representations assess the relevance of a passage concerning a query through efficient similarity measures. Multi-vector representations, while enhancing effectiveness, cause a one-order-of-magnitude increase in memory footprint and query latency by encoding queries and documents on a per-token level. The current state-of-the-art approach, namely PLAID, has introduced a centroid-based term representation to mitigate the memory impact of multi-vector systems. By employing a centroid interaction mechanism, PLAID filters out non-relevant documents, reducing the cost of subsequent ranking stages. This paper1 introduces "Efficient Multi-Vector dense retrieval with Bit vectors" (EMVB), a novel framework for efficient query processing in multi-vector dense retrieval. Firstly, EMVB utilizes an optimized bit vector pre-filtering step for passages, enhancing efficiency. Secondly, the computation of centroid interaction occurs column-wise, leveraging SIMD instructions to reduce latency. Thirdly, EMVB incorporates Product Quantization (PQ) to decrease the memory footprint of storing vector representations while facilitating fast late interaction. Lastly, a per-document term filtering method is introduced, further improving the efficiency of the final step. Experiments conducted on MS MARCO and LoTTE demonstrate that EMVB achieves up to a 2.8× speed improvement while reducing the memory footprint by 1.8×, without compromising retrieval accuracy compared to PLAID
The H2A/H2B-like histone-fold domain proteins at the crossroad between chromatin and different DNA metabolisms
No full textCore histones are the building block of chromatin and among the most highly conserved proteins in eukaryotes. The related "deviant" histones share the histone-fold domain, and serve various roles in DNA metabolism. We provide here a structural and functional outlook of H2A/H2B-like deviant histones in transcription, replication and remodeling
Efficient Multi-vector Dense Retrieval with Bit Vectors
No full textDense retrieval techniques employ pre-trained large language models to build a high-dimensional representation of queries and passages. These representations compute the relevance of a passage w.r.t. to a query using efficient similarity measures. In this line, multi-vector representations show improved effectiveness at the expense of a one-order-of-magnitude increase in memory footprint and query latency by encoding queries and documents on a per-token level. Recently, PLAID has tackled these problems by introducing a centroid-based term representation to reduce the memory impact of multi-vector systems. By exploiting a centroid interaction mechanism, PLAID filters out non-relevant documents, thus reducing the cost of the successive ranking stages. This paper proposes “Efficient Multi-Vector dense retrieval with Bit vectors” (EMVB), a novel framework for efficient query processing in multi-vector dense retrieval. First, EMVB employs a highly efficient pre-filtering step of passages using optimized bit vectors. Second, the computation of the centroid interaction happens column-wise, exploiting SIMD instructions, thus reducing its latency. Third, EMVB leverages Product Quantization (PQ) to reduce the memory footprint of storing vector representations while jointly allowing for fast late interaction. Fourth, we introduce a per-document term filtering method that further improves the efficiency of the last step. Experiments on MS MARCO and LoTTE show that EMVB is up to 2.8× faster while reducing the memory footprint by 1.8× with no loss in retrieval accuracy compared to PLAID
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