Improving explainable recommendations by deep review-based explanations

Many e-commerce sites encourage their users to write product reviews, in the knowledge that they exert a considerable influence on users’ decision-making processes. These snippets of real-world experience provide an essential source of data for interpretable recommendations. However, current methods relying on user-generated content to make recommendations can run into problems because of well-known issues with reviews, such as noise, sparsity and irrelevant content. On the other hand, recent advances in text generation methods demonstrate significant text quality improvements and show promise in their ability to address these problems. In this paper, we develop two character-level deep neural network-based personalised review generation models, and improve recommendation accuracy by generating high-quality text which meets the input criteria of text-aware recommender systems. To make fair comparisons, we train review-aware recommender systems by human written reviews and attain advanced recommendations by feeding generated reviews at the inference step. Our experiments are conducted on four large review datasets from multiple domains. We leverage our methods’ performance by comparing with non-review based recommender systems and advanced review-aware recommender systems. The results demonstrate that we beat baselines on a range of metrics and obtain state-of-the-art performance on both rating prediction and top- N ranking. Our sparsity experiments validate that our generation models can produce high-quality text to tackle the sparsity problem. We also demonstrate the generation of useful reviews so that we can achieve up to 13.53% RMSE improvements. For explanation evaluation, quantitative analyses reveal good understandable scores for our generated review-based explanations, and qualitative case studies substantiate we can capture critical aspects in generating explanations.Science Foundation IrelandInsight Research Centre2021-06-14 JG: broken PDF replace

Weakly Supervised Video Representation Learning with Unaligned Text for Sequential Videos

Sequential video understanding, as an emerging video understanding task, has driven lots of researchers' attention because of its goal-oriented nature. This paper studies weakly supervised sequential video understanding where the accurate time-stamp level text-video alignment is not provided. We solve this task by borrowing ideas from CLIP. Specifically, we use a transformer to aggregate frame-level features for video representation and use a pre-trained text encoder to encode the texts corresponding to each action and the whole video, respectively. To model the correspondence between text and video, we propose a multiple granularity loss, where the video-paragraph contrastive loss enforces matching between the whole video and the complete script, and a fine-grained frame-sentence contrastive loss enforces the matching between each action and its description. As the frame-sentence correspondence is not available, we propose to use the fact that video actions happen sequentially in the temporal domain to generate pseudo frame-sentence correspondence and supervise the network training with the pseudo labels. Extensive experiments on video sequence verification and text-to-video matching show that our method outperforms baselines by a large margin, which validates the effectiveness of our proposed approach. Code is available at https://github.com/svip-lab/WeakSVRComment: CVPR 2023. Code: https://github.com/svip-lab/WeakSV

Research progress in brain-targeted nasal drug delivery

The unique anatomical and physiological connections between the nasal cavity and brain provide a pathway for bypassing the blood–brain barrier to allow for direct brain-targeted drug delivery through nasal administration. There are several advantages of nasal administration compared with other routes; for example, the first-pass effect that leads to the metabolism of orally administered drugs can be bypassed, and the poor compliance associated with injections can be minimized. Nasal administration can also help maximize brain-targeted drug delivery, allowing for high pharmacological activity at lower drug dosages, thereby minimizing the likelihood of adverse effects and providing a highly promising drug delivery pathway for the treatment of central nervous system diseases. The aim of this review article was to briefly describe the physiological structures of the nasal cavity and brain, the pathways through which drugs can enter the brain through the nose, the factors affecting brain-targeted nasal drug delivery, methods to improve brain-targeted nasal drug delivery systems through the application of related biomaterials, common experimental methods used in intranasal drug delivery research, and the current limitations of such approaches, providing a solid foundation for further in-depth research on intranasal brain-targeted drug delivery systems (see Graphical Abstract)

Removal of Contaminants from Oracle Bones During Sample Pretreatment

Animal bones and tortoise shells were used for divination by the Chinese royal family during the Shang Dynasty (similar to w16th-11th century BC), and the divination results were recorded as inscriptions on oracle bones and shells, which are very valuable cultural remains and record many important events in the Shang Dynasty period. Thus, radiocarbon dating of oracle bones was used to build a precise chronology of the late Shang Dynasty. Due to their original burial conditions and the fact that in subsequent decades the pieces were traded or archived in museums, oracle bones are expected to be contaminated with exogenous materials from the environment and the conservation process. During dating, we found that some samples were contaminated by conservation chemical reagents. The contaminated samples were purified by removing exogenous chemicals with a series of organic solvents, in a method modified from Bruhn et al. (2001). Both whole bone and gelatin samples were processed with this purification method, resulting in satisfactory improvements in dating results.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000251221300005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Geochemistry & GeophysicsSCI(E)CPCI-S(ISTP)

Efficacy and safety of zanubrutinib plus R-CHOP in treatment of non-GCB DLBCL with extranodal involvement

IntroductionTreatment with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) shows poor response rates in non–germinal center B cell–like (non-GCB) diffuse large B-cell lymphoma (DLBCL) patients with multiple extranodal involvement. This study aims to evaluate anti-tumor activity and safety of zanubrutinib with R-CHOP (ZR-CHOP) in treatment naïve non-GCB DLBCL with extranodal involvement.MethodsIn this single-arm, phase 2, prospective, single-center study, patients with newly diagnosed non-GCB DLBCL with extranodal involvement enrolled between October 2020 to March 2022 received ZR-CHOP for 6 cycles followed by 2 cycles of maintenance treatment with rituximab and zanubrutinib. The primary endpoint included progression-free survival (PFS) in the intent-to-treat (ITT) population whereas the secondary endpoints included overall response rate (ORR), complete response (CR), and duration of response. Further, next-generation sequencing (NGS) was used for detection of different oncogenic mutations closely related to DLBCL pathogenesis.ResultsFrom October 2020 to March 2022, 26 patients were enrolled, and 23 of them were evaluated for efficacy after receiving 3 cycles of ZR-CHOP treatment. 1-year PFS and OS were 80.8% and 88.5% respectively while expected PFS and OS for 2-years are 74.0% and 88.5% respectively with median follow-up of 16.7 months and ORR was 91.3% (CR: 82.61%; PR: 8.70%). Oncogenic mutations closely related to DLBCL pathogenesis were assessed in 20 patients using NGS. B-cell receptor and NF-κB pathway gene mutations were detected in 10 patients, which occurred in MYD88 (7/19), CD79B (4/19), CARD11 (5/19), and TNFAIP3 (2/19). Hematological adverse events (AEs) ≥ grade 3 included neutropenia (50%), thrombocytopenia (23.1%), and anemia (7.7%) whereas non-hematological AEs ≥ grade 3 included pulmonary infection (19.2%).ConclusionZR-CHOP is safe and effective for treating treatment naïve non-GCB DLBCL patients with extranodal involvement.Clinical Trial RegistrationClinicaltrials.gov, NCT0483587

How can students-as-partners work address challenges to student, faculty, and staff mental health and well-being?

Mental health has emerged as a critical area of attention in higher education, and educational research over the last 15 years has focused increasingly on emotions and wellbeing at all stages of education (Hill et al., 2021). While definitions of well-being vary, most are premised on “good quality of life” (Nair et al., 2018, p. 69). Within the last few years, we have experienced an intersection of several forces that undermine or threaten good quality of life. These include the uncertainties prompted by the COVID-19 pandemic (Hews et al., 2022, U.S. Surgeon General, n.d.), climate change (Charlson et al., 2021), racism and social injustices (Williams & Etkins, 2021), the cost-of-living crisis (Montacute, 2023), and the lack of motivation and higher incidence of mental health issues associated with growing concerns about job prospects and income (Chowdhury et al., 2022). This fifth iteration of Voices from the Field explores some of the ways in which students-as-partners work can address challenges to the mental health and well-being of students, faculty, and staff. This focus, proposed by members of the IJSaP Editorial Board, both responds to the intersecting realities named above and remains true to the goal of this section of the journal, which is to offer a venue for a wide range of contributors to address important questions around and aspects of students-as-partners work without going through the intensive submission, peer-review, and revision processes. The prompt we included in the call for this iteration of Voices was: “In what ways can students-as-partners work address challenges to the mental health and well-being of students, staff, and faculty posed by the current realities in the wider world (socio-political, environmental, economic, etc.) that affect higher education?

Improving recommendation by deep latent factor-based explanation

The Thirty-Fourth AAAI Conference on Artificial Intelligence: Interactive and Conversational Recommendation Systems (WICRS) Workshop, New York, United States of America, 7-12 February 2020The latent factor methods and explanation algorithms constitute the foundation of many advanced explainable recommender systems. However, interpreting the high-dimensional latent factors has not been sufficiently addressed and continuously becomes a challenging work. Besides, only a few works have researched the use of explanation to improve recommendations. In this paper, we propose a deep learning method that generates high-quality latent factor-based explanations and efficiently ameliorating recommendations. We conduct top- K items ranking experiment on two real-world datasets and show that our method outperforms nine currently state-of-theart recommender systems in five ranking metrics. Moreover, we conduct a qualitative and quantitative analysis of users’ latent factors and reveal that we continually offer the best latent representations.Science Foundation IrelandInsight Research Centr