Finding Support Documents with a Logistic Regression Approach

Entity retrieval finds the relevant results for a user’s information needs at a finer unit called “entity”. To retrieve such entity, people usually first locate a small set of support documents which contain answer entities, and then further detect the answer entities in this set. In the literature, people view the support documents as relevant documents, and their findings as a conventional document retrieval problem. In this paper, we will state that finding support documents and that of relevant documents, although sounds similar, have important differences. Further, we propose a logistic regression approach to find support documents. Our experiment results show that the logistic regression method performs significantly better than a baseline system that treat the support document finding as a conventional document retrieval problem

Counting Value Sets: Algorithm and Complexity

Let $p$ be a prime. Given a polynomial in \F_{p^m}[x] of degree $d$ over the finite field \F_{p^m}, one can view it as a map from \F_{p^m} to \F_{p^m}, and examine the image of this map, also known as the value set. In this paper, we present the first non-trivial algorithm and the first complexity result on computing the cardinality of this value set. We show an elementary connection between this cardinality and the number of points on a family of varieties in affine space. We then apply Lauder and Wan's $p$-adic point-counting algorithm to count these points, resulting in a non-trivial algorithm for calculating the cardinality of the value set. The running time of our algorithm is $(pmd)^{O(d)}$. In particular, this is a polynomial time algorithm for fixed $d$ if $p$ is reasonably small. We also show that the problem is #P-hard when the polynomial is given in a sparse representation, $p=2$, and $m$ is allowed to vary, or when the polynomial is given as a straight-line program, $m=1$ and $p$ is allowed to vary. Additionally, we prove that it is NP-hard to decide whether a polynomial represented by a straight-line program has a root in a prime-order finite field, thus resolving an open problem proposed by Kaltofen and Koiran in \cite{Kaltofen03,KaltofenKo05}

Research on the competitiveness of crediting rating industry using PCA method

Purpose: This study investigates the industry competitiveness problem, which plays an important role in crediting rating industry safety. Based on a comprehensive literatures review, we found that there is much room to improve regarding of competitiveness assessment in crediting rating industry. Design/methodology/approach: In this study, we propose the PCA (Principal Component Analysis) method to illustrate the problems. Findings: America and Canada’s companies (such as S&P and DBRS) take the leading place in credit rating industry, and Japan’ agencies have made great progress in industry competition (such as JCR), while China’ agencies are lagging behind (Such as CCXI). Research limitations/implications: It requires multi-year data for analysis, but the empirical analysis is carried out based on one-year data instead of multi-year data. Practical implications: The research can fill the gaps for credit rating industry safety research. And study findings and feasible suggestions are provided for academics and practitioners. Originality/value: This paper puts forward the competitive indicators of credit rating industry, and indicators of cause and outcome are consideredPeer Reviewe

Computing zeta functions of large polynomial systems over finite fields

In this paper, we improve the algorithms of Lauder-Wan \cite{LW} and Harvey \cite{Ha} to compute the zeta function of a system of $m$ polynomial equations in $n$ variables over the finite field \FF_q of $q$ elements, for $m$ large. The dependence on $m$ in the original algorithms was exponential in $m$. Our main result is a reduction of the exponential dependence on $m$ to a polynomial dependence on $m$. As an application, we speed up a doubly exponential time algorithm from a software verification paper \cite{BJK} (on universal equivalence of programs over finite fields) to singly exponential time. One key new ingredient is an effective version of the classical Kronecker theorem which (set-theoretically) reduces the number of defining equations for a "large" polynomial system over \FF_q when $q$ is suitably large

Cross-Modal Contrastive Learning for Robust Reasoning in VQA

Multi-modal reasoning in visual question answering (VQA) has witnessed rapid progress recently. However, most reasoning models heavily rely on shortcuts learned from training data, which prevents their usage in challenging real-world scenarios. In this paper, we propose a simple but effective cross-modal contrastive learning strategy to get rid of the shortcut reasoning caused by imbalanced annotations and improve the overall performance. Different from existing contrastive learning with complex negative categories on coarse (Image, Question, Answer) triplet level, we leverage the correspondences between the language and image modalities to perform finer-grained cross-modal contrastive learning. We treat each Question-Answer (QA) pair as a whole, and differentiate between images that conform with it and those against it. To alleviate the issue of sampling bias, we further build connected graphs among images. For each positive pair, we regard the images from different graphs as negative samples and deduct the version of multi-positive contrastive learning. To our best knowledge, it is the first paper that reveals a general contrastive learning strategy without delicate hand-craft rules can contribute to robust VQA reasoning. Experiments on several mainstream VQA datasets demonstrate our superiority compared to the state of the arts. Code is available at \url{https://github.com/qizhust/cmcl_vqa_pl}

Assessment of the agriculture supply chain risks for investments of agricultural small and mediumsized enterprises (SMEs) using the decision support model

A key challenge in responding to the emerging challenges in agri-food supply chains is encouraging continued new investment. This is related to the recognition that agricultural production is often a lengthy process requiring ongoing investments that may not produce expected returns for a prolonged period, thereby being highly sensitive tomarket risks. Agricultural productions are generally susceptible to different serious risks such as crop diseases, weather conditions, and pest infections. Many practitioners in this domain, particularly small and medium-sized enterprises (SMEs), have shifted toward digitalization to address such problems. To help with this situation, the current paper develops an integrated decision-making framework, with the Pythagorean fuzzy sets (PFSs), the method for removal effects of criteria (MEREC), the ranksum (RS) and the gained and Lost dominance score (GLDS) termed as PF-MEREC-RS-GLDS approach. In this approach, the PF-MEREC-RS method is applied to compute the subjective and objective weights of the main risks to assess the agriculture supply chain for investments of SMEs, and the PF-GLDS model is used to assess the preferences of enterprises over different the main risks to assess of the agriculture supply chain for investments of SMEs. An empirical case study is taken to evaluate the main risks to assess the agriculture supply chain for SME investments. Also, comparison and sensitivity investigation are made to show the superiority of the developed framework

Preventive Effects of Collagen Peptide from Deer Sinew on Bone Loss in Ovariectomized Rats

Deer sinew (DS) has been used traditionally for various illnesses, and the major active constituent is collagen. In this study, we assessed the effects of collagen peptide from DS on bone loss in the ovariectomized rats. Wister female rats were randomly divided into six groups as follows: sham-operated (SHAM), ovariectomized control (OVX), OVX given 1.0 mg/kg/week nylestriol (OVX + N), OVX given 0.4 g/kg/day collagen peptide (OVX + H), OVX given 0.2 g/kg/day collagen peptide (OXV + M), and OVX given 0.1 g/kg/day collagen peptide (OXV + L), respectively. After 13 weeks of treatment, the rats were euthanized, and the effects of collagen peptide on body weight, uterine weight, bone mineral density (BMD), serum biochemical indicators, bone histomorphometry, and bone mechanics were observed. The data showed that BMD and concentration of serum hydroxyproline were significantly increased and the levels of serum calcium, phosphorus, and alkaline phosphatase were decreased. Besides, histomorphometric parameters and mechanical indicators were improved. However, collagen peptide of DS has no effect on estradiol level, body weight, and uterine weight. Therefore, these results suggest that the collagen peptide supplementation may also prevent and treat bone loss