FARA: Future-aware Ranking Algorithm for Fairness Optimization

Ranking systems are the key components of modern Information Retrieval (IR) applications, such as search engines and recommender systems. Besides the ranking relevance to users, the exposure fairness to item providers has also been considered an important factor in ranking optimization. Many fair ranking algorithms have been proposed to jointly optimize both ranking relevance and fairness. However, we find that most existing fair ranking methods adopt greedy algorithms that only optimize rankings for the next immediate session or request. As shown in this paper, such a myopic paradigm could limit the upper bound of ranking optimization and lead to suboptimal performance in the long term. To this end, we propose \textbf{FARA}, a novel \textbf{F}uture-\textbf{A}ware \textbf{R}anking \textbf{A}lgorithm for ranking relevance and fairness optimization. Instead of greedily optimizing rankings for the next immediate session, FARA plans ahead by jointly optimizing multiple ranklists together and saving them for future sessions. Specifically, FARA first uses the Taylor expansion to investigate how future ranklists will influence the overall fairness of the system. Then, based on the analysis of the Taylor expansion, FARA adopts a two-phase optimization algorithm where we first solve an optimal future exposure planning problem and then construct the optimal ranklists according to the optimal future exposure planning. Theoretically, we show that FARA is optimal for ranking relevance and fairness joint optimization. Empirically, our extensive experiments on three semi-synthesized datasets show that FARA is efficient, effective, and can deliver significantly better ranking performance compared to state-of-the-art fair ranking methods. We make our implementation public at \href{https://github.com/Taosheng-ty/QP_fairness/}{https://github.com/Taosheng-ty/QP\_fairness/}.Comment: 11 pages, four figures, four tables. CIKM202

An In-depth Investigation of User Response Simulation for Conversational Search

Conversational search has seen increased recent attention in both the IR and NLP communities. It seeks to clarify and solve a user's search need through multi-turn natural language interactions. However, most existing systems are trained and demonstrated with recorded or artificial conversation logs. Eventually, conversational search systems should be trained, evaluated, and deployed in an open-ended setting with unseen conversation trajectories. A key challenge is that training and evaluating such systems both require a human-in-the-loop, which is expensive and does not scale. One strategy for this is to simulate users, thereby reducing the scaling costs. However, current user simulators are either limited to only respond to yes-no questions from the conversational search system, or unable to produce high quality responses in general. In this paper, we show that current state-of-the-art user simulation system could be significantly improved by replacing it with a smaller but advanced natural language generation model. But rather than merely reporting this new state-of-the-art, we present an in-depth investigation of the task of simulating user response for conversational search. Our goal is to supplement existing works with an insightful hand-analysis of what challenges are still unsolved by the advanced model, as well as to propose our solutions for them. The challenges we identified include (1) dataset noise, (2) a blind spot that is difficult for existing models to learn, and (3) a specific type of misevaluation in the standard empirical setup. Except for the dataset noise issue, we propose solutions to cover the training blind spot and to avoid the misevaluation. Our proposed solutions lead to further improvements. Our best system improves the previous state-of-the-art significantly.Comment: 9 page

Correction: Selective particle and cell capture in a continuous flow using micro-vortex acoustic streaming

The authors regret that a citation to a relevant paper was missed. The following sentence and reference (ref. 1 shown below) should be added in the Introduction after the sentence ending "...of the applied flow rate.5,37": "For example, Lee et al. acoustically oscillate air/liquid interfaces using a ∼50 kHz piezoelectric transducer to produce acoustic streaming fields for size-based separation of cells and particles".1 The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers

A blood atlas of COVID-19 defines hallmarks of disease severity and specificity.

Treatment of severe COVID-19 is currently limited by clinical heterogeneity and incomplete description of specific immune biomarkers. We present here a comprehensive multi-omic blood atlas for patients with varying COVID-19 severity in an integrated comparison with influenza and sepsis patients versus healthy volunteers. We identify immune signatures and correlates of host response. Hallmarks of disease severity involved cells, their inflammatory mediators and networks, including progenitor cells and specific myeloid and lymphocyte subsets, features of the immune repertoire, acute phase response, metabolism, and coagulation. Persisting immune activation involving AP-1/p38MAPK was a specific feature of COVID-19. The plasma proteome enabled sub-phenotyping into patient clusters, predictive of severity and outcome. Systems-based integrative analyses including tensor and matrix decomposition of all modalities revealed feature groupings linked with severity and specificity compared to influenza and sepsis. Our approach and blood atlas will support future drug development, clinical trial design, and personalized medicine approaches for COVID-19

Key transcriptional regulators of neutrophil function in inflammation: Transcriptional regulation of neutrophils during inflammation

Neutrophils are important effector cells in innate immunity, possessing a wide range of effector functions, including reactive oxygen species (ROS) production, phagocytosis and chemotaxis. These properties enable neutrophils to rapidly respond to stimulation and orchestrate protective immunity. Currently, the transcriptional regulatory networks underlying neutrophil activation and function remain largely unexplored. Of note, there is partial understanding of transcription factors (TFs) that act as key regulators to control neutrophil development and inflammatory responses. Neutrophils undergo tightly controlled genomic and transcriptional changes while transitioning between bone marrow, blood, and tissue sites. However, the molecular mechanisms underlining neutrophil function during inflammation have yet to be fully elucidated. To determine the transcription factor networks that shape these responses, we have undertaken integrated transcriptional and chromatin analyses of neutrophils during acute inflammation and revealed distinct sets of putative transcription factors (TFs) associated with control of neutrophil differentiation and inflammatory responses. To investigate the regulatory role of the selected TFs in neutrophils, I utilised HoxB8 myeloid progenitors as a model system for in vitro production of neutrophils and generated stable knockout lines for JUNB, RELB, IRF5, RFX2, KLF6, and RUNX1 in Hoxb8 myeloid progenitors, using the CRISPR/Cas9 mediated system. Additionally, I have demonstrated the importance of RUNX1, KLF6 in neutrophil differentiation. RUNX1, KLF6 deletion in HoxB8 progenitors caused a block in neutrophil differentiation and produced lower levels of mature neutrophils. KLF6 and RUNX1 deficient neutrophils also displayed impaired transmigration properties. Consistent with findings in the Hoxb8 in vitro model, conditional deletion of RUNX1 in myeloid populations produced lower mature neutrophils in the bone marrow. To validate the functional role of the selected TFs in neutrophil activation, I examined the consequence of CEBPβ, RELB, IRF5, JUNB knockouts on neutrophil effector functions, such as phagocytosis, cytokine production, generation of ROS, formation of NETs and bacterial killing. I found that RELB, IRF5, JUNB knockout significantly affect the ability of neutrophils to produce inflammatory mediators. JUNB and RELB also contribute to neutrophil phagocytosis, ROS generation and NETosis

Cytokine-Like Protein 1(Cytl1): A Potential Molecular Mediator in Embryo Implantation.

Cytokine-like protein 1 (Cytl1), originally described as a protein expressed in CD34+ cells, was recently identified as a functional secreted protein involved in chondrogenesis and cartilage development. However, our knowledge of Cytl1 is still limited. Here, we determined the Cytl1 expression pattern regulated by ovarian hormones at both the mRNA and protein levels. We found that the endometrial expression of Cytl1 in mice was low before or on the first day of gestation, significantly increased during embryo implantation, and then decreased at the end of implantation. We investigated the effects of Cytl1 on endometrial cell proliferation, and the effects on the secretion of leukemia inhibitory factor (LIF) and heparin-binding epidermal growth factor (HB-EGF). We also explored the effect of Cytl1 on endometrial adhesion properties in cell-cell adhesion assays. Our findings demonstrated that Cytl1 is an ovarian hormone-dependent protein expressed in the endometrium that enhances the proliferation of HEC-1-A and RL95-2 cells, stimulates endometrial secretion of LIF and HB-EGF, and enhances the adhesion of HEC-1-A and RL95-2 cells to JAR spheroids. This study suggests that Cytl1 plays an active role in the regulation of embryo implantation