Judgment on Unfair Competition Dispute Between Baidu Online Network Technology (Beijing) Ltd. Co. and Beijing 3721 Technology Ltd. Co.

On October 20, 2003, Baidu Online Network Technology (Beijing) Ltd., Co. (“Baidu”), a Nasdaq-listed company known as the “Google of China,” filed a suit against its competitor Beijing 3721 Technology Ltd. Co. (“3721”) in Beijing Chaoyang District Court for copyright infringement and unfair competition. The case is regarded as China’s first copyright-infringement dispute involving website search-engine technology. Legal experts, the Chinese media, and the Supreme Court of China have paid close attention to the case, especially as it is related to China’s ongoing legislative effort to improve protection of intellectual property. The translation below is the appellate opinion in this case issued by Beijing No. 2 Intermediate People’s Court in April 2004

Improving Contrastive Learning of Sentence Embeddings with Focal-InfoNCE

The recent success of SimCSE has greatly advanced state-of-the-art sentence representations. However, the original formulation of SimCSE does not fully exploit the potential of hard negative samples in contrastive learning. This study introduces an unsupervised contrastive learning framework that combines SimCSE with hard negative mining, aiming to enhance the quality of sentence embeddings. The proposed focal-InfoNCE function introduces self-paced modulation terms in the contrastive objective, downweighting the loss associated with easy negatives and encouraging the model focusing on hard negatives. Experimentation on various STS benchmarks shows that our method improves sentence embeddings in terms of Spearman's correlation and representation alignment and uniformity.Comment: Findings of emnlp 202

A Machine Learning and Computer Vision Application to Robustly Extract Winnings from Multiple Lottery Tickets in One Shot

Mega Millions and Powerball are among the most popular American lottery games. This article provides a practical software application that can conveniently examine and evaluate several lottery tickets for prizes using just the images. The application accepts as input a directory containing the images of lottery tickets and utilizes machine learning and computer vision to extract lottery ticket data, lottery name, lottery draw date, 5-digit lottery numbers, 2-digit lottery "ball" numbers, and the lottery multiplier. The application also retrieves winning lottery data that corresponds to the lottery draw date using a public database API. This is compared with data collected from each lottery ticket image to establish matches, and the corresponding prize amount is computed. The current version of the application supports GPU usage, and image orientation has no impact on its functionality.  It is believed that a considerable portion of the U.S. public participating in the Powerball and Mega Millions lotteries will find such an application beneficial and handy

A Machine Learning and Computer Vision Application to Robustly Extract Winnings from Multiple Lottery Tickets in One Shot

Mega Millions and Powerball are among the most popular American lottery games. This article provides a practical software application that can conveniently examine and evaluate several lottery tickets for prizes using just the images. The application accepts as input a directory containing the images of lottery tickets and utilizes machine learning and computer vision to extract lottery ticket data, lottery name, lottery draw date, 5-digit lottery numbers, 2-digit lottery "ball" numbers, and the lottery multiplier. The application also retrieves winning lottery data that corresponds to the lottery draw date using a public database API. This is compared with data collected from each lottery ticket image to establish matches, and the corresponding prize amount is computed. The current version of the application supports GPU usage, and image orientation has no impact on its functionality.  It is believed that a considerable portion of the U.S. public participating in the Powerball and Mega Millions lotteries will find such an application beneficial and handy

Designing An Instrument For Gauging Equity Literacy

Equity literacy refers to the skills and mindsets needed to recognize, respond to, and redress conditions that deny equitable access to education. It involves understanding how identities such as ethnicity, gender, sexual orientation, language, religion, immigration status, and disability intersect and contribute to class inequities. More than mere awareness, equity literacy demands a commitment to deepening individual and institutional understandings of the dynamics of equity and injustice within organizations and communities. Its goal is to pinpoint disparities, eradicate inequities, and actively foster a culture of equity. Evaluating equity literacy is essential to understand how educational disparities impact access to equitable opportunities free from bias and discrimination. Considering the existing deficiency in tools for assessing equity literacy, this study introduces a survey instrument designed to assess equity literacy in educational institutions. This survey was developed based on Gorski's equity literacy framework (2016). To establish its validity, the survey was reviewed by experts and refined using Lawshe’s Content Validity Ratio (CVR). Items with CVR scores below the established threshold were removed. The revised 20-item survey was administered to 34 individuals to assess reliability using Cronbach’s alpha. The survey demonstrated robust reliability with an alpha of 0.87. Additionally, the survey categorizes total scores into four rubric levels of equity literacy: exceptional, fair, developing, and little/none. This survey serves as a foundational tool for implementing this framework,  thus empowering educators to challenge prevailing mindsets and cultural deficits

Designing An Instrument For Gauging Equity Literacy

Equity literacy refers to the skills and mindsets needed to recognize, respond to, and redress conditions that deny equitable access to education. It involves understanding how identities such as ethnicity, gender, sexual orientation, language, religion, immigration status, and disability intersect and contribute to class inequities. More than mere awareness, equity literacy demands a commitment to deepening individual and institutional understandings of the dynamics of equity and injustice within organizations and communities. Its goal is to pinpoint disparities, eradicate inequities, and actively foster a culture of equity. Evaluating equity literacy is essential to understand how educational disparities impact access to equitable opportunities free from bias and discrimination. Considering the existing deficiency in tools for assessing equity literacy, this study introduces a survey instrument designed to assess equity literacy in educational institutions. This survey was developed based on Gorski's equity literacy framework (2016). To establish its validity, the survey was reviewed by experts and refined using Lawshe’s Content Validity Ratio (CVR). Items with CVR scores below the established threshold were removed. The revised 20-item survey was administered to 34 individuals to assess reliability using Cronbach’s alpha. The survey demonstrated robust reliability with an alpha of 0.87. Additionally, the survey categorizes total scores into four rubric levels of equity literacy: exceptional, fair, developing, and little/none. This survey serves as a foundational tool for implementing this framework, thus empowering educators to challenge prevailing mindsets and cultural deficits. &nbsp

Mining directional drug interaction effects on myopathy using the FAERS database

Mining high-order drug-drug interaction (DDI) induced adverse drug effects from electronic health record (EHR) databases is an emerging area, and very few studies have explored the relationships between high-order drug combinations. We investigate a novel pharmacovigilance problem for mining directional DDI effects on myopathy using the FDA Adverse Event Reporting System (FAERS) database. Our work provides information on the risk of myopathy associated with adding new drugs on the already prescribed medication, and visualizes the identified directional DDI patterns as user-friendly graphical representation. We utilize the Apriori algorithm to extract frequent drug combinations from the FAERS database. We use odds ratio (OR) to estimate the risk of myopathy associated with directional DDI. We create a tree-structured graph to visualize the findings for easy interpretation. Our method confirmed myopathy association with previously reported HMG-CoA reductase inhibitors like rosuvastatin, fluvastatin, simvastatin and atorvastatin. New, previously unidentified but mechanistically plausible associations with myopathy were also observed, such as the DDI between pamidronate and levofloxacin. Additional top findings are gadolinium-based imaging agents, which however are often used in myopathy diagnosis. Other DDIs with no obvious mechanism are also reported, such as that of sulfamethoxazole with trimethoprim and potassium chloride. This study shows the feasibility to estimate high-order directional DDIs in a fast and accurate manner. The results of the analysis could become a useful tool in the specialists' hands through an easy-to-understand graphic visualization

MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion

Query expansion is a commonly-used technique in many search systems to better represent users' information needs with additional query terms. Existing studies for this task usually propose to expand a query with retrieved or generated contextual documents. However, both types of methods have clear limitations. For retrieval-based methods, the documents retrieved with the original query might not be accurate enough to reveal the search intent, especially when the query is brief or ambiguous. For generation-based methods, existing models can hardly be trained or aligned on a particular corpus, due to the lack of corpus-specific labeled data. In this paper, we propose a novel Large Language Model (LLM) based mutual verification framework for query expansion, which alleviates the aforementioned limitations. Specifically, we first design a query-query-document generation pipeline, which can effectively leverage the contextual knowledge encoded in LLMs to generate sub-queries and corresponding documents from multiple perspectives. Next, we employ a mutual verification method for both generated and retrieved contextual documents, where 1) retrieved documents are filtered with the external contextual knowledge in generated documents, and 2) generated documents are filtered with the corpus-specific knowledge in retrieved documents. Overall, the proposed method allows retrieved and generated documents to complement each other to finalize a better query expansion. We conduct extensive experiments on three information retrieval datasets, i.e., TREC-DL-2020, TREC-COVID, and MSMARCO. The results demonstrate that our method outperforms other baselines significantly