ANTIQUE: A Non-Factoid Question Answering Benchmark

Considering the widespread use of mobile and voice search, answer passage retrieval for non-factoid questions plays a critical role in modern information retrieval systems. Despite the importance of the task, the community still feels the significant lack of large-scale non-factoid question answering collections with real questions and comprehensive relevance judgments. In this paper, we develop and release a collection of 2,626 open-domain non-factoid questions from a diverse set of categories. The dataset, called ANTIQUE, contains 34,011 manual relevance annotations. The questions were asked by real users in a community question answering service, i.e., Yahoo! Answers. Relevance judgments for all the answers to each question were collected through crowdsourcing. To facilitate further research, we also include a brief analysis of the data as well as baseline results on both classical and recently developed neural IR models

Training Curricula for Open Domain Answer Re-Ranking

In precision-oriented tasks like answer ranking, it is more important to rank many relevant answers highly than to retrieve all relevant answers. It follows that a good ranking strategy would be to learn how to identify the easiest correct answers first (i.e., assign a high ranking score to answers that have characteristics that usually indicate relevance, and a low ranking score to those with characteristics that do not), before incorporating more complex logic to handle difficult cases (e.g., semantic matching or reasoning). In this work, we apply this idea to the training of neural answer rankers using curriculum learning. We propose several heuristics to estimate the difficulty of a given training sample. We show that the proposed heuristics can be used to build a training curriculum that down-weights difficult samples early in the training process. As the training process progresses, our approach gradually shifts to weighting all samples equally, regardless of difficulty. We present a comprehensive evaluation of our proposed idea on three answer ranking datasets. Results show that our approach leads to superior performance of two leading neural ranking architectures, namely BERT and ConvKNRM, using both pointwise and pairwise losses. When applied to a BERT-based ranker, our method yields up to a 4% improvement in MRR and a 9% improvement in P@1 (compared to the model trained without a curriculum). This results in models that can achieve comparable performance to more expensive state-of-the-art techniques.Comment: Accepted at SIGIR 2020 (long

Temporal patterns of cancer burden in Asia, 1990–2019: a systematic examination for the Global Burden of Disease 2019 study

BackgroundCancers represent a challenging public health threat in Asia. This study examines the temporal patterns of incidence, mortality, disability and risk factors of 29 cancers in Asia in the last three decades. MethodsThe age, sex and year-wise estimates of incidence, mortality, and disability-adjusted life years (DALYs) of 29 cancers for 49 Asian countries from 1990 through 2019 were generated as a part of the Global Burden of Disease, Injuries and Risk Factors 2019 study. Besides incidence, mortality and DALYs, we also examined the cancer burden measured in terms of DALYs and deaths attributable to risk factors, which had evidence of causation with different cancers. The development status of countries was measured using the socio-demographic index. Decomposition analysis was performed to gauge the change in cancer incidence between 1990 and 2019 due to population growth, aging and age-specific incidence rates. FindingsAll cancers combined claimed an estimated 5.6 million [95% uncertainty interval, 5.1–6.0 million] lives in Asia with 9.4 million [8.6–10.2 million] incident cases and 144.7 million [132.7–156.5 million] DALYs in 2019. The age-standardized incidence rate (ASIR) of all cancers combined in Asia was 197.6/100,000 [181.0–214.4] in 2019, varying from 99.2/100,000 [76.1–126.0] in Bangladesh to 330.5/100,000 [298.5–365.8] in Cyprus. The age-standardized mortality rate (ASMR) was 120.6/100,000 [110.1–130.7] in 2019, varying 4-folds across countries from 71.0/100,000 [59.9–83.5] in Kuwait to 284.2/100,000 [229.2–352.3] in Mongolia. The age-standardized DALYs rate was 2970.5/100,000 [2722.6–3206.5] in 2019, varying from 1578.0/100,000 [1341.2–1847.0] in Kuwait to 6574.4/100,000 [5141.7–8333.0] in Mongolia. Between 1990 and 2019, deaths due to 17 of the 29 cancers either doubled or more, and 20 of the 29 cancers underwent an increase of 150% or more in terms of new cases. Tracheal, bronchus, and lung cancer (both sexes), breast cancer (among females), colon and rectum cancer (both sexes), stomach cancer (both sexes) and prostate cancer (among males) were among top-5 cancers in most Asian countries in terms of ASIR and ASMR in 2019 and cancers of liver, stomach, hodgkin lymphoma and esophageal cancer posted the most significant decreases in age-standardized rates between 1990 and 2019. Among the modifiable risk factors, smoking, alcohol use, ambient particulate matter (PM) pollution and unsafe sex remained the dominant risk factors between 1990 and 2019. Cancer DALYs due to ambient PM pollution, high body mass index and fasting plasma glucose has increased most notably between 1990 and 2019. InterpretationWith growing incidence, cancer has become more significant public health threat in Asia, demanding urgent policy attention and guidance. Its heightened risk calls for increased cancer awareness, preventive measures, affordable early-stage detection, and cost-effective therapeutics in Asia. The current study can serve as a useful resource for policymakers and researchers in Asia for devising interventions for cancer management and control. FundingThe GBD study is funded by the Bill and Melinda Gates Foundation.This work is supported by: - University Grants Commission - Chandigarh University - National Science and Technology Council - grant no. [112-2410-H-003-031] - Bill and Melinda Gates Foundation - grant no. [OPP1152504] - Fundamental Research Funds for the Central Universities - grant no. [30923011101] - Social Science Foundation of Jiangsu Province - grant no. [21GLD008] - National Natural Science Foundation of China - grant no. [72204112

Retrieval Augmented Representation Learning for Information Retrieval

Information retrieval (IR) is a scientific discipline within the fields of computer and information sciences that enables billions of users to efficiently access the information they need. Applications of information retrieval include, but are not limited to, search engines, question answering, and recommender systems. Decades of IR research have demonstrated that learning accurate query and document representations plays a vital role in the effectiveness of IR systems. State-of the-art representation learning solutions for information retrieval heavily rely on deep neural networks. However, despite their effective performance, current approaches are not quite optimal for all IR settings. For example, information retrieval systems often deal with inputs that are not clear and self-sufficient, e.g., many queries submitted to search engines. In such cases, current state-of-the-art models cannot learn an optimal representation of the input or even an accurate set of all representations. To address this major issue, we develop novel approaches by augmenting neural representation learning models using a retrieval module that guides the model towards learning more effective representations. We study our retrieval augmentation approaches in a diverse set of somewhat novel and emerging information retrieval ap plications. First, we introduce Guided Transformer—an extension to the Transformer network that adjusts the input representations using multiple documents provided by a retrieval module—and demonstrate its effectiveness in learning representations for conversational search problems. Next, we propose novel representation learning models that learn multiple representations for queries that may carry multiple intents, including ambiguous and faceted queries. For doing so, we also introduce a novel optimization approach that enables encoder-decoder architectures to generate a per mutation invariant set of query intents. Furthermore, we study retrieval-augmented data generation for domain adaptation in IR, which concerns applying a retrieval model trained on a source domain to a target domain that often suffers from unavailability of training data. We introduce a novel adaptive IR task, in which only a textual description of the target domain is available. We define a taxonomy of domain attributes in information retrieval to identify different properties of a source domain that can be adapted to a target domain. We introduce a novel automatic data construction pipeline for adapting dense retrieval models to the target domain. We believe that the applications of the developed retrieval augmentation methods can be expanded to many more real-world IR tasks.Doctor of Philosophy (PhD)2025-05-1

Final report : life cycle assessment of UBC Faculty of Pharmaceutical

The Life Cycle Assessment of the UBC Faculty of Pharmaceutical Sciences and Center for Drug Research and Development was performed in order to evaluate its environmental impacts. This building is currently under construction and in order to attain the most reliable data and to evaluate their performance and impacts on the environment, more accurate data collection is required. Which itself requires more accurate and up to date drawings and models. This project was done through modeling the building using On-Screen Takeoff and Athena Impact Estimator software. Since this building is under construction, BIM model was found helpful and more updated than structural and architectural drawings and was used as a supplement to these drawings. According to the Bill of Materials obtained from On-Screen Takeoff and Athena Impact Estimator, five most significant materials of this building were recognized to be concrete 30Mpa, 5/8" Fire-Rated Type X Gypsum Board, glazing panels, galvanized studs and rebar rod, and light sections. The output from the Impact Estimator (IE) is a list of impact category during the manufacturing and construction phases to the end-of-life stage of the building. The results of the study in terms of the impact categories are as follow: •Global warming potential: 1.04E+07 kg CO₂ eq •Ozone layer depletion: 1.51E-02 kg CFC-11 eq •Acidification potential: 4.12E+06 moles of H⁺ eq •Eutrophication potential: 5.16E+03 kg N eq •Smog potential: 4.99E+04 kg NOx eq •Human health respiratory effects: 4.14E+04 kg PM2.5 eq •Weighted resource use: 5.60E+07 ecologically weighted kg •Fossil fuel use: 1.08E+08 MJ After performing Sensitivity Analysis on the five most common materials in the building and evaluating their effects on each impact category, walls show great impacts on global warming, ozone layer depletion, acidification potential, smog potential, human health respiratory effects, and fossil fuel use more than other assemblies. Also, columns and beams have the major contribution to eutrophication potential impact category since they mainly consist of concrete and rebar. Floors play the main role in impact potential of weighted resource use. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”Applied Science, Faculty ofCivil Engineering, Department ofUnreviewedUndergraduat

Play therapy and storytelling intervention on children's social skills with attention deficit-hyperactivity disorder

BACKGROUND: Attention deficit-hyperactivity disorder (ADHD) is a common neuro-behavioral disorder that negatively affects educational, relational, and occupational aspects of one's life. Although many children diagnosed with this disorder can benefit from taking medication, particularly for core symptoms, play therapy and storytelling can be seen as engaging, stimulating, and more compatible with children's developmental needs. The social skills of these children are as vital as other symptoms and can be better addressed with cognitive-based art therapy interventions. Because little research has been focused on the combination of play therapy and storytelling and the social interactions of children with ADHD are highly important in academic settings, this study aimed to determine the effects of this combination on children's social skills with ADHD.MATERIALS AND METHODS: This survey was a quasi-experimental study with a pre-test–post-test design and a control group. Participants were 7–11-year-old girls and boys with ADHD based on DSM-V referred to child and adolescent psychiatrists' clinics. Selected children were randomly allocated into intervention and control groups. The intervention group received an individual combined intervention of play therapy and storytelling, whereas the control group did not receive any therapeutic intervention for social skills at that time and was on the waiting list. The research tool was the Social Skills Rating System (SSRS), and data were computer-analyzed using SPSS-20 and a couple of descriptive and analytic tests including ANCOVA.RESULTS: In this study, 30 children with ADHD were included. The combined intervention of play therapy and storytelling has had a significant effect on post-test results of ADHD patients in terms of social skills as well as all test subscales (P < 0/05). There was a significant improvement in the subscales of self-expression, self-control, responsibility, and cooperation (P < 0.05).CONCLUSIONS: Results show promise for combined play therapy and storytelling intervention to enhance the social skills of elementary school children diagnosed with ADHD

Alpha-Pinene Effect on the Improvement of Working and Spatial memory in Rats

Background and Aim: Oxidative stress is an important factor in the development of memory and learning disorder which can cause neuronal damage in the hippocampus. Alpha-pinene is a polyphenolic compound from the terpene family that has shown important anti-inflammatory, anti-anxiety, antioxidant and neuroprotective effects in the central nervous system and can affect memory. The aim of the present study was to investigate the effect of alpha-pinene on the improvement of working and spatial memory in rats.  Materials and Methods: In this study, 24 male rats were randomly divided into 3 groups: control and 2 alpha-pinene groups (5 and 10 mg/kg IP) for 3 weeks. Spatial and working memories were assessed by Morris water maze and Y maze, respectively. Then, malondialdehyde level and total antioxidant capacity in hippocampal tissue were measured. Data were analyzed using one-way analysis of variance and Tukey's post hoc test. Results: The percentage of alternation in the Y maze increased in the group which had received 10 mg/kg alpha-pinene group compared to those in the control group and the group which had received 5 mg/kg alpha-pinene. The time spent in the target area at the dose of 10 mg/kg of alpha-pinene showed a significant increase compared to that in the control group, but there was no significant difference among the groups in terms of the time to reach the target platform. Alpha-pinene at the dose of 10 mg/kg decreased the level of malondialdehyde in hippocampal tissue compared to the control group, but no significant difference was observed between the groups in terms of total antioxidant capacity. Conclusion: Alpha-pinene increased spatial and working memory performance in rats. One of the possible mechanisms of memory improvement in the present study could be due to the reduction of malondialdehyde in the hippocampal tissue, as one of the important indicators of oxidative stress in the central nervous system

Artificial Intelligence in Cancer Care: From Diagnosis to Prevention and Beyond

&lt;p&gt;Artificial Intelligence (AI) has made significant strides in revolutionizing cancer care, encompassing various aspects from diagnosis to prevention and beyond. With its ability to analyze vast amounts of data, recognize patterns, and make accurate predictions, AI has emerged as a powerful tool in the fight against cancer. This article explores the applications of AI in cancer care, highlighting its role in diagnosis, treatment decision-making, prevention, and ongoing management. In the realm of cancer diagnosis, AI has demonstrated remarkable potential. By processing patient data, including medical imaging, pathology reports, and genetic profiles, AI algorithms can assist in early detection and accurate diagnosis. Image recognition algorithms can analyze radiological images, such as mammograms or CT scans, to detect subtle abnormalities and assist radiologists in identifying potential tumors. AI can also aid pathologists in analyzing tissue samples, leading to more precise and efficient cancer diagnoses. AI's impact extends beyond diagnosis into treatment decision-making. The integration of AI algorithms with clinical data allows for personalized treatment approaches. By analyzing patient characteristics, disease stage, genetic markers, and treatment outcomes, AI can provide valuable insights to oncologists, aiding in treatment planning and predicting response to specific therapies. This can lead to more targeted and effective treatment strategies, improving patient outcomes and reducing unnecessary treatments and side effects. Furthermore, AI plays a crucial role in cancer prevention. By analyzing genetic and environmental risk factors, AI algorithms can identify individuals at higher risk of developing certain cancers. This enables targeted screening programs and early interventions, allowing for timely detection and prevention of cancer. Additionally, AI can analyze population-level data to identify trends and patterns, contributing to the development of public health strategies for cancer prevention and control. AI's involvement in cancer care goes beyond diagnosis and treatment, encompassing ongoing management and survivorship. AI-powered systems can monitor treatment response, track disease progression, and detect recurrence at an early stage. By continuously analyzing patient data, including imaging, laboratory results, and clinical assessments, AI algorithms can provide real-time insights, facilitating timely interventions and adjustments to treatment plans. This proactive approach to disease management improves patient outcomes and enhances quality of life.&lt;/p&gt