CONVERSER: Few-Shot Conversational Dense Retrieval with Synthetic Data Generation

Conversational search provides a natural interface for information retrieval (IR). Recent approaches have demonstrated promising results in applying dense retrieval to conversational IR. However, training dense retrievers requires large amounts of in-domain paired data. This hinders the development of conversational dense retrievers, as abundant in-domain conversations are expensive to collect. In this paper, we propose CONVERSER, a framework for training conversational dense retrievers with at most 6 examples of in-domain dialogues. Specifically, we utilize the in-context learning capability of large language models to generate conversational queries given a passage in the retrieval corpus. Experimental results on conversational retrieval benchmarks OR-QuAC and TREC CAsT 19 show that the proposed CONVERSER achieves comparable performance to fully-supervised models, demonstrating the effectiveness of our proposed framework in few-shot conversational dense retrieval. All source code and generated datasets are available at https://github.com/MiuLab/CONVERSERComment: Accepted to SIGDIAL 202

Unsupervised Multilingual Dense Retrieval via Generative Pseudo Labeling

Dense retrieval methods have demonstrated promising performance in multilingual information retrieval, where queries and documents can be in different languages. However, dense retrievers typically require a substantial amount of paired data, which poses even greater challenges in multilingual scenarios. This paper introduces UMR, an Unsupervised Multilingual dense Retriever trained without any paired data. Our approach leverages the sequence likelihood estimation capabilities of multilingual language models to acquire pseudo labels for training dense retrievers. We propose a two-stage framework which iteratively improves the performance of multilingual dense retrievers. Experimental results on two benchmark datasets show that UMR outperforms supervised baselines, showcasing the potential of training multilingual retrievers without paired data, thereby enhancing their practicality. Our source code, data, and models are publicly available at https://github.com/MiuLab/UMRComment: Accepted to Findings of EACL 202

Model Extraction Attack against Self-supervised Speech Models

Self-supervised learning (SSL) speech models generate meaningful representations of given clips and achieve incredible performance across various downstream tasks. Model extraction attack (MEA) often refers to an adversary stealing the functionality of the victim model with only query access. In this work, we study the MEA problem against SSL speech model with a small number of queries. We propose a two-stage framework to extract the model. In the first stage, SSL is conducted on the large-scale unlabeled corpus to pre-train a small speech model. Secondly, we actively sample a small portion of clips from the unlabeled corpus and query the target model with these clips to acquire their representations as labels for the small model's second-stage training. Experiment results show that our sampling methods can effectively extract the target model without knowing any information about its model architecture

Risk factors and outcomes of carbapenem-nonsusceptible Escherichia coli bacteremia: A matched case–control study

BackgroundInfections due to carbapenem-resistant Enterobacteriaceae have been the emerging problem worldwide. This primary object of this study was to understand the risk factors and clinical outcomes of carbapenem-nonsusceptible Escherichia coli (CNSEc) bacteremia.MethodsWe conducted a matched case–control study in a 3,715-bed tertiary care medical center in northern Taiwan. The controls were selected among patients with carbapenem-susceptible E coli and were matched with CNSEc for bacteremia.ResultsFifty-one patients were included in this study (17 cases and 34 controls). Bivariate analysis showed that prior exposure to carbapenems (p<0.001), stay in intensive care units (p=0.016), placement of central venous catheters (p=0.001), chronic liver diseases (p<0.001), uremia with regular dialysis (p=0.004), and mechanical ventilation (p=0.004) were associated with CNSEc bacteremia. Multivariate analysis revealed that prior exposure to carbapenems [odds ratio (OR), 29.17; 95% confidence interval (CI), 1.76–484.70; p=0.019], uremia with regular dialysis (OR, 98.58; 95% CI, 4.02–999; p=0.005) and chronic liver diseases (OR, 27.86; 95% CI, 2.31–335.83; p=0.009) were independent risk factors for CNSEc bacteremia. Compared with carbapenem-susceptible E coli group, CNSEc group had a longer hospital stay (68.4 days vs. 35.8 days; p=0.04) and a higher disease severity, as indicated by a Pittsburgh bacteremia score greater than or equal to 4 (5.6% vs. 2.5%; p=0.015). Patients with CNSEc bacteremia had a higher overall in-hospital mortality rate (94.12% vs. 50.00%; p=0.002), but there was no difference in the 28-day mortality between these two groups.ConclusionsCNSEc bacteremia would lead to a poor outcome among patients with prior exposure to carbapenems, chronic liver disease, and uremia with regular dialysis

Towards General-Purpose Text-Instruction-Guided Voice Conversion

This paper introduces a novel voice conversion (VC) model, guided by text instructions such as "articulate slowly with a deep tone" or "speak in a cheerful boyish voice". Unlike traditional methods that rely on reference utterances to determine the attributes of the converted speech, our model adds versatility and specificity to voice conversion. The proposed VC model is a neural codec language model which processes a sequence of discrete codes, resulting in the code sequence of converted speech. It utilizes text instructions as style prompts to modify the prosody and emotional information of the given speech. In contrast to previous approaches, which often rely on employing separate encoders like prosody and content encoders to handle different aspects of the source speech, our model handles various information of speech in an end-to-end manner. Experiments have demonstrated the impressive capabilities of our model in comprehending instructions and delivering reasonable results.Comment: Accepted to ASRU 202

BPR1K653, a Novel Aurora Kinase Inhibitor, Exhibits Potent Anti-Proliferative Activity in MDR1 (P-gp170)-Mediated Multidrug-Resistant Cancer Cells

Over-expression of Aurora kinases promotes the tumorigenesis of cells. The aim of this study was to determine the preclinical profile of a novel pan-Aurora kinase inhibitor, BPR1K653, as a candidate for anti-cancer therapy. Since expression of the drug efflux pump, MDR1, reduces the effectiveness of various chemotherapeutic compounds in human cancers, this study also aimed to determine whether the potency of BPR1K653 could be affected by the expression of MDR1 in cancer cells.BPR1K653 specifically inhibited the activity of Aurora-A and Aurora-B kinase at low nano-molar concentrations in vitro. Anti-proliferative activity of BPR1K653 was evaluated in various human cancer cell lines. Results of the clonogenic assay showed that BPR1K653 was potent in targeting a variety of cancer cell lines regardless of the tissue origin, p53 status, or expression of MDR1. At the cellular level, BPR1K653 induced endo-replication and subsequent apoptosis in both MDR1-negative and MDR1-positive cancer cells. Importantly, it showed potent activity against the growth of xenograft tumors of the human cervical carcinoma KB and KB-derived MDR1-positive KB-VIN10 cells in nude mice. Finally, BPR1K653 also exhibited favorable pharmacokinetic properties in rats.BPR1K653 is a novel potent anti-cancer compound, and its potency is not affected by the expression of the multiple drug resistant protein, MDR1, in cancer cells. Therefore, BPR1K653 is a promising anti-cancer compound that has potential for the management of various malignancies, particularly for patients with MDR1-related drug resistance after prolonged chemotherapeutic treatments

The Effects of Aβ1-42 Binding to the SARS-CoV-2 Spike Protein S1 Subunit and Angiotensin-Converting Enzyme 2

Increasing evidence suggests that elderly people with dementia are vulnerable to the development of severe coronavirus disease 2019 (COVID-19). In Alzheimer’s disease (AD), the major form of dementia, β-amyloid (Aβ) levels in the blood are increased; however, the impact of elevated Aβ levels on the progression of COVID-19 remains largely unknown. Here, our findings demonstrate that Aβ1-42, but not Aβ1-40, bound to various viral proteins with a preferentially high affinity for the spike protein S1 subunit (S1) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the viral receptor, angiotensin-converting enzyme 2 (ACE2). These bindings were mainly through the C-terminal residues of Aβ1-42. Furthermore, Aβ1-42 strengthened the binding of the S1 of SARS-CoV-2 to ACE2 and increased the viral entry and production of IL-6 in a SARS-CoV-2 pseudovirus infection model. Intriguingly, data from a surrogate mouse model with intravenous inoculation of Aβ1-42 show that the clearance of Aβ1-42 in the blood was dampened in the presence of the extracellular domain of the spike protein trimers of SARS-CoV-2, whose effects can be prevented by a novel anti-Aβ antibody. In conclusion, these findings suggest that the binding of Aβ1-42 to the S1 of SARS-CoV-2 and ACE2 may have a negative impact on the course and severity of SARS-CoV-2 infection. Further investigations are warranted to elucidate the underlying mechanisms and examine whether reducing the level of Aβ1-42 in the blood is beneficial to the fight against COVID-19 and AD