"We Demand Justice!": Towards Grounding Political Text in Social Context

Social media discourse from US politicians frequently consists of 'seemingly similar language used by opposing sides of the political spectrum'. But often, it translates to starkly contrasting real-world actions. For instance, "We need to keep our students safe from mass shootings" may signal either "arming teachers to stop the shooter" or "banning guns to reduce mass shootings" depending on who says it and their political stance on the issue. In this paper, we define and characterize the context that is required to fully understand such ambiguous statements in a computational setting and ground them in real-world entities, actions, and attitudes. To that end, we propose two challenging datasets that require an understanding of the real-world context of the text to be solved effectively. We benchmark these datasets against baselines built upon large pre-trained models such as BERT, RoBERTa, GPT-3, etc. Additionally, we develop and benchmark more structured baselines building upon existing 'Discourse Contextualization Framework' and 'Political Actor Representation' models. We perform analysis of the datasets and baseline predictions to obtain further insights into the pragmatic language understanding challenges posed by the proposed social grounding tasks.Comment: Was accepted to and withdrawn from Findings of EMNLP 202

On-Device Model Fine-Tuning with Label Correction in Recommender Systems

To meet the practical requirements of low latency, low cost, and good privacy in online intelligent services, more and more deep learning models are offloaded from the cloud to mobile devices. To further deal with cross-device data heterogeneity, the offloaded models normally need to be fine-tuned with each individual user's local samples before being put into real-time inference. In this work, we focus on the fundamental click-through rate (CTR) prediction task in recommender systems and study how to effectively and efficiently perform on-device fine-tuning. We first identify the bottleneck issue that each individual user's local CTR (i.e., the ratio of positive samples in the local dataset for fine-tuning) tends to deviate from the global CTR (i.e., the ratio of positive samples in all the users' mixed datasets on the cloud for training out the initial model). We further demonstrate that such a CTR drift problem makes on-device fine-tuning even harmful to item ranking. We thus propose a novel label correction method, which requires each user only to change the labels of the local samples ahead of on-device fine-tuning and can well align the locally prior CTR with the global CTR. The offline evaluation results over three datasets and five CTR prediction models as well as the online A/B testing results in Mobile Taobao demonstrate the necessity of label correction in on-device fine-tuning and also reveal the improvement over cloud-based learning without fine-tuning

Enhanced anticancer activity of a combination of docetaxel and Aneustat (OMN54) in a patient-derived, advanced prostate cancer tissue xenograft model.

The current first-line treatment for advanced metastatic prostate cancer, i.e. docetaxel-based therapy, is only marginally effective. The aim of the present study was to determine whether such therapy can be improved by combining docetaxel with Aneustat (OMN54), a multivalent botanical drug candidate shown to have anti-prostate cancer activity in preliminary in vitro experiments, which is currently undergoing a Phase-I Clinical Trial. Human metastatic, androgen-independent C4-2 prostate cancer cells and NOD-SCID mice bearing PTEN-deficient, metastatic and PSA-secreting, patient-derived subrenal capsule LTL-313H prostate cancer tissue xenografts were treated with docetaxel and Aneustat, alone and in combination. In vitro, Aneustat markedly inhibited C4-2 cell replication in a dose-dependent manner. When Aneustat was combined with docetaxel, the growth inhibitions of the drugs were essentially additive. In vivo, however, the combination of docetaxel and Aneustat enhanced anti-tumor activity synergistically and very markedly, without inducing major host toxicity. Complete growth inhibition and shrinkage of the xenografts could be obtained with the combined drugs as distinct from the drugs on their own. Analysis of the gene expression of the xenografts using microarray indicated that docetaxel + Aneustat led to expanded anticancer activity, in particular to targeting of cancer hallmarks that were not affected by the single drugs. Our findings, obtained with a highly clinically relevant prostate cancer model, suggest, for the first time, that docetaxel-based therapy of advanced human prostate cancer may be improved by combining docetaxel with Aneustat

Toward Understanding the Influence of Individual Clients in Federated Learning

Federated learning allows mobile clients to jointly train a global model without sending their private data to a central server. Extensive works have studied the performance guarantee of the global model, however, it is still unclear how each individual client influences the collaborative training process. In this work, we defined a new notion, called {\em Fed-Influence}, to quantify this influence over the model parameters, and proposed an effective and efficient algorithm to estimate this metric. In particular, our design satisfies several desirable properties: (1) it requires neither retraining nor retracing, adding only linear computational overhead to clients and the server; (2) it strictly maintains the tenets of federated learning, without revealing any client's local private data; and (3) it works well on both convex and non-convex loss functions, and does not require the final model to be optimal. Empirical results on a synthetic dataset and the FEMNIST dataset demonstrate that our estimation method can approximate Fed-Influence with small bias. Further, we show an application of Fed-Influence in model debugging.Comment: Accepted at AAAI 202

Proteostasis by STUB1/HSP70 complex controls sensitivity to androgen receptor targeted therapy in advanced prostate cancer.

Protein homeostasis (proteostasis) is a potential mechanism that contributes to cancer cell survival and drug resistance. Constitutively active androgen receptor (AR) variants confer anti-androgen resistance in advanced prostate cancer. However, the role of proteostasis involved in next generation anti-androgen resistance and the mechanisms of AR variant regulation are poorly defined. Here we show that the ubiquitin-proteasome-system (UPS) is suppressed in enzalutamide/abiraterone resistant prostate cancer. AR/AR-V7 proteostasis requires the interaction of E3 ubiquitin ligase STUB1 and HSP70 complex. STUB1 disassociates AR/AR-V7 from HSP70, leading to AR/AR-V7 ubiquitination and degradation. Inhibition of HSP70 significantly inhibits prostate tumor growth and improves enzalutamide/abiraterone treatments through AR/AR-V7 suppression. Clinically, HSP70 expression is upregulated and correlated with AR/AR-V7 levels in high Gleason score prostate tumors. Our results reveal a novel mechanism of anti-androgen resistance via UPS alteration which could be targeted through inhibition of HSP70 to reduce AR-V7 expression and overcome resistance to AR-targeted therapies

The incidence and risk of venous thromboembolism associated with peripherally inserted central venous catheters in hospitalized patients: A systematic review and meta-analysis

BackgroundVenous thromboembolism (VTE) can be fatal if not treated promptly, and individual studies have reported wide variability in rates of VTE associated with peripherally inserted central catheters (PICC). We thus conducted this meta-analysis to investigate the overall incidence and risk of developing PICC-related VTE in hospitalized patients.MethodsWe searched PubMed, Embase, Scopus, and Web of Science databases from inception until January 26, 2022. In studies with a non-comparison arm, the pooled incidence of PICC-related VTE was calculated. The pooled odds ratio (OR) was calculated to assess the risk of VTE in the studies that compared PICC to the central venous catheter (CVC). The Newcastle-Ottawa Scale was used to assess methodological quality.ResultsA total of 75 articles (58 without a comparison arm and 17 with), including 109292 patients, were included in the meta-analysis. The overall pooled incidence of symptomatic VTE was 3.7% (95% CI: 3.1–4.4) in non-comparative studies. In the subgroup meta-analysis, the incidence of VTE was highest in patients who were in a critical care setting (10.6%; 95% CI: 5.0–17.7). Meta-analysis of comparative studies revealed that PICC was associated with a statistically significant increase in the odds of VTE events compared with CVC (OR, 2.48; 95% CI, 1.83–3.37; P < 0.01). However, in subgroup analysis stratified by the study design, there was no significant difference in VTE events between the PICC and CVC in randomized controlled trials (OR, 2.28; 95% CI, 0.77–6.74; P = 0.13).ConclusionBest practice standards such as PICC tip verification and VTE prophylaxis can help reduce the incidence and risk of PICC-related VTE. The risk-benefit of inserting PICC should be carefully weighed, especially in critically ill patients. Cautious interpretation of our results is important owing to substantial heterogeneity among the studies included in this study