Multi-sensor Suboptimal Fusion Student's tt Filter

A multi-sensor fusion Student's $t$ filter is proposed for time-series recursive estimation in the presence of heavy-tailed process and measurement noises. Driven from an information-theoretic optimization, the approach extends the single sensor Student's $t$ Kalman filter based on the suboptimal arithmetic average (AA) fusion approach. To ensure computationally efficient, closed-form $t$ density recursion, reasonable approximation has been used in both local-sensor filtering and inter-sensor fusion calculation. The overall framework accommodates any Gaussian-oriented fusion approach such as the covariance intersection (CI). Simulation demonstrates the effectiveness of the proposed multi-sensor AA fusion-based $t$ filter in dealing with outliers as compared with the classic Gaussian estimator, and the advantage of the AA fusion in comparison with the CI approach and the augmented measurement fusion.Comment: 8 pages, 8 figure

Quotatives Indicate Decline in Objectivity in U.S. Political News

According to journalistic standards, direct quotes should be attributed to sources with objective quotatives such as "said" and "told", as nonobjective quotatives, like "argued" and "insisted" would influence the readers' perception of the quote and the quoted person. In this paper, we analyze the adherence to this journalistic norm to study trends in objectivity in political news across U.S. outlets of different ideological leanings. We ask: 1) How has the usage of nonobjective quotatives evolved? and 2) How do news outlets use nonobjective quotatives when covering politicians of different parties? To answer these questions, we developed a dependency-parsing-based method to extract quotatives and applied it to Quotebank, a web-scale corpus of attributed quotes, obtaining nearly 7 million quotes, each enriched with the quoted speaker's political party and the ideological leaning of the outlet that published the quote. We find that while partisan outlets are the ones that most often use nonobjective quotatives, between 2013 and 2020, the outlets that increased their usage of nonobjective quotatives the most were "moderate" centrist news outlets (around 0.6 percentage points, or 20% in relative percentage over 7 years). Further, we find that outlets use nonobjective quotatives more often when quoting politicians of the opposing ideology (e.g., left-leaning outlets quoting Republicans), and that this "quotative bias" is rising at a swift pace, increasing up to 0.5 percentage points, or 25% in relative percentage, per year. These findings suggest an overall decline in journalistic objectivity in U.S. political news.Comment: Repo: https://github.com/epfl-dlab/quotative_bia

Context-Aware Prompt Tuning for Vision-Language Model with Dual-Alignment

Large-scale vision-language models (VLMs), e.g., CLIP, learn broad visual concepts from tedious training data, showing superb generalization ability. Amount of prompt learning methods have been proposed to efficiently adapt the VLMs to downstream tasks with only a few training samples. We introduce a novel method to improve the prompt learning of vision-language models by incorporating pre-trained large language models (LLMs), called Dual-Aligned Prompt Tuning (DuAl-PT). Learnable prompts, like CoOp, implicitly model the context through end-to-end training, which are difficult to control and interpret. While explicit context descriptions generated by LLMs, like GPT-3, can be directly used for zero-shot classification, such prompts are overly relying on LLMs and still underexplored in few-shot domains. With DuAl-PT, we propose to learn more context-aware prompts, benefiting from both explicit and implicit context modeling. To achieve this, we introduce a pre-trained LLM to generate context descriptions, and we encourage the prompts to learn from the LLM's knowledge by alignment, as well as the alignment between prompts and local image features. Empirically, DuAl-PT achieves superior performance on 11 downstream datasets on few-shot recognition and base-to-new generalization. Hopefully, DuAl-PT can serve as a strong baseline. Code will be available

Generative Language Models Exhibit Social Identity Biases

The surge in popularity of large language models has given rise to concerns about biases that these models could learn from humans. In this study, we investigate whether ingroup solidarity and outgroup hostility, fundamental social biases known from social science, are present in 51 large language models. We find that almost all foundational language models and some instruction fine-tuned models exhibit clear ingroup-positive and outgroup-negative biases when prompted to complete sentences (e.g., "We are..."). A comparison of LLM-generated sentences with human-written sentences on the internet reveals that these models exhibit similar level, if not greater, levels of bias than human text. To investigate where these biases stem from, we experimentally varied the amount of ingroup-positive or outgroup-negative sentences the model was exposed to during fine-tuning in the context of the United States Democrat-Republican divide. Doing so resulted in the models exhibiting a marked increase in ingroup solidarity and an even greater increase in outgroup hostility. Furthermore, removing either ingroup-positive or outgroup-negative sentences (or both) from the fine-tuning data leads to a significant reduction in both ingroup solidarity and outgroup hostility, suggesting that biases can be reduced by removing biased training data. Our findings suggest that modern language models exhibit fundamental social identity biases and that such biases can be mitigated by curating training data. Our results have practical implications for creating less biased large-language models and further underscore the need for more research into user interactions with LLMs to prevent potential bias reinforcement in humans.Comment: supplementary material, data, and code see https://osf.io/9ht32/?view_only=f0ab4b23325f4c31ad3e12a7353b55f

Multiple Unfolded Protein Response Pathways Cooperate to Link Cytosolic dsDNA Release to Stimulator of Interferon Gene Activation

The double-stranded DNA (dsDNA) sensor STING has been increasingly implicated in responses to sterile endogenous threats and pathogens without nominal DNA or cyclic di-nucleotide stimuli. Previous work showed an endoplasmic reticulum (ER) stress response, known as the unfolded protein response (UPR), activates STING. Herein, we sought to determine if ER stress generated a STING ligand, and to identify the UPR pathways involved. Induction of IFN-β expression following stimulation with the UPR inducer thapsigargin (TPG) or oxygen glucose deprivation required both STING and the dsDNA-sensing cyclic GMP-AMP synthase (cGAS). Furthermore, TPG increased cytosolic mitochondrial DNA, and immunofluorescence visualized dsDNA punctae in murine and human cells, providing a cGAS stimulus. N-acetylcysteine decreased IFN-β induction by TPG, implicating reactive oxygen species (ROS). However, mitoTEMPO, a mitochondrial oxidative stress inhibitor did not impact TPG-induced IFN. On the other hand, inhibiting the inositol requiring enzyme 1 (IRE1) ER stress sensor and its target transcription factor XBP1 decreased the generation of cytosolic dsDNA. iNOS upregulation was XBP1-dependent, and an iNOS inhibitor decreased cytosolic dsDNA and IFN-β, implicating ROS downstream of the IRE1-XBP1 pathway. Inhibition of the PKR-like ER kinase (PERK) pathway also attenuated cytoplasmic dsDNA release. The PERK-regulated apoptotic factor Bim was required for both dsDNA release and IFN-β mRNA induction. Finally, XBP1 and PERK pathways contributed to cytosolic dsDNA release and IFN-induction by the RNA virus, Vesicular Stomatitis Virus (VSV). Together, our findings suggest that ER stressors, including viral pathogens without nominal STING or cGAS ligands such as RNA viruses, trigger multiple canonical UPR pathways that cooperate to activate STING and downstream IFN-β via mitochondrial dsDNA release

Microbial communities associated with epilithic algal matrix with different morphological characters in Luhuitou fringing reef

The microbiota is an important component of the epilithic algal matrix (EAM) and plays a central role in the biogeochemical cycling of important nutrients in coral reef ecosystems. Insufficient studies on EAM microbiota diversity have led to a limited understanding of the ecological functions of EAMs in different states. To explore the microbial community of EAMs in the Luhuitou fringing reef in Sanya, China, which has undergone the incessant expansion and domination of algae over the past several decades, investigations were conducted in the reef’s intertidal zone. Five types of substrate habitats (dead branching coral, dead massive coral, dead flat coral, granite block, and concrete block) were selected, and their microbial communities were analyzed by high-throughput sequencing of EAM holobionts using the 16S rDNA V4 region. Proteobacteria was the most abundant group, accounting for more than 70% of reads of the microbial composition across all sites, followed by Cyanobacteria (15.89%) and Bacteroidetes (5.93%), respectively. Cluster analysis divided all microbial communities into three groups, namely short, medium, and long EAMs. Algal length was the most important morphological factor impacting the differences in the composition of the EAM microbiota. The three EAM groups had 52 common OTUs and 78.52% common sequences, among which the most abundant were Vibrio spp. and Photobacterium spp. The three types of EAM also had unique OTUs. The short EAMs had 238 unique OTUs and 48.61% unique sequences, mainly in the genera Shewanella and Cyanobacterium. The medium EAMs contained 130 unique OTUs and 4.36% unique sequences, mainly in the genera Pseudomonas and Bacillus. The long EAMs only had 27 unique OTUs and 4.13% unique sequences, mainly in the genus Marinobacter. Compared with short EAM, medium and long EAM had a lower proportion of autotrophic bacteria and higher proportion of potential pathogenic bacteria. It is suggested that EAMs with different phenotypes have different microbial compositions, and the ecological function of the EAM microbiota changes from autotrophic to pathogenic with an increase in algal length. As EAMs have expanded on coastal coral reefs worldwide, it is essential to comprehensively explore the community structure and ecological role of their microbial communities