ProTeCt: Prompt Tuning for Hierarchical Consistency

Large visual-language models, like CLIP, learn generalized representations and have shown promising zero-shot performance. Few-shot adaptation methods, based on prompt tuning, have also been shown to further improve performance on downstream datasets. However, these models are not hierarchically consistent. Frequently, they infer incorrect labels at coarser taxonomic class levels, even when the inference at the leaf level (original class labels) is correct. This is problematic, given their support for open set classification and, in particular, open-grained classification, where practitioners define label sets at various levels of granularity. To address this problem, we propose a prompt tuning technique to calibrate the hierarchical consistency of model predictions. A set of metrics of hierarchical consistency, the Hierarchical Consistent Accuracy (HCA) and the Mean Treecut Accuracy (MTA), are first proposed to benchmark model performance in the open-granularity setting. A prompt tuning technique, denoted as Prompt Tuning for Hierarchical Consistency (ProTeCt), is then proposed to calibrate classification across all possible label set granularities. Results show that ProTeCt can be combined with existing prompt tuning methods to significantly improve open-granularity classification performance without degradation of the original classification performance at the leaf level

Anticipating Daily Intention using On-Wrist Motion Triggered Sensing

Anticipating human intention by observing one's actions has many applications. For instance, picking up a cellphone, then a charger (actions) implies that one wants to charge the cellphone (intention). By anticipating the intention, an intelligent system can guide the user to the closest power outlet. We propose an on-wrist motion triggered sensing system for anticipating daily intentions, where the on-wrist sensors help us to persistently observe one's actions. The core of the system is a novel Recurrent Neural Network (RNN) and Policy Network (PN), where the RNN encodes visual and motion observation to anticipate intention, and the PN parsimoniously triggers the process of visual observation to reduce computation requirement. We jointly trained the whole network using policy gradient and cross-entropy loss. To evaluate, we collect the first daily "intention" dataset consisting of 2379 videos with 34 intentions and 164 unique action sequences. Our method achieves 92.68%, 90.85%, 97.56% accuracy on three users while processing only 29% of the visual observation on average

Single-Stage Visual Relationship Learning using Conditional Queries

Research in scene graph generation (SGG) usually considers two-stage models, that is, detecting a set of entities, followed by combining them and labeling all possible relationships. While showing promising results, the pipeline structure induces large parameter and computation overhead, and typically hinders end-to-end optimizations. To address this, recent research attempts to train single-stage models that are computationally efficient. With the advent of DETR, a set based detection model, one-stage models attempt to predict a set of subject-predicate-object triplets directly in a single shot. However, SGG is inherently a multi-task learning problem that requires modeling entity and predicate distributions simultaneously. In this paper, we propose Transformers with conditional queries for SGG, namely, TraCQ with a new formulation for SGG that avoids the multi-task learning problem and the combinatorial entity pair distribution. We employ a DETR-based encoder-decoder design and leverage conditional queries to significantly reduce the entity label space as well, which leads to 20% fewer parameters compared to state-of-the-art single-stage models. Experimental results show that TraCQ not only outperforms existing single-stage scene graph generation methods, it also beats many state-of-the-art two-stage methods on the Visual Genome dataset, yet is capable of end-to-end training and faster inference.Comment: Accepted to NeurIPS 202

4β-Hydroxywithanolide E from Physalis peruviana (golden berry) inhibits growth of human lung cancer cells through DNA damage, apoptosis and G2/M arrest

<p>Abstract</p> <p>Background</p> <p>The crude extract of the fruit bearing plant, <it>Physalis peruviana </it>(golden berry), demonstrated anti-hepatoma and anti-inflammatory activities. However, the cellular mechanism involved in this process is still unknown.</p> <p>Methods</p> <p>Herein, we isolated the main pure compound, 4β-Hydroxywithanolide (4βHWE) derived from golden berries, and investigated its antiproliferative effect on a human lung cancer cell line (H1299) using survival, cell cycle, and apoptosis analyses. An alkaline comet-nuclear extract (NE) assay was used to evaluate the DNA damage due to the drug.</p> <p>Results</p> <p>It was shown that DNA damage was significantly induced by 1, 5, and 10 μg/mL 4βHWE for 2 h in a dose-dependent manner (<it>p </it>< 0.005). A trypan blue exclusion assay showed that the proliferation of cells was inhibited by 4βHWE in both dose- and time-dependent manners (<it>p </it>< 0.05 and 0.001 for 24 and 48 h, respectively). The half maximal inhibitory concentrations (IC₅₀) of 4βHWE in H1299 cells for 24 and 48 h were 0.6 and 0.71 μg/mL, respectively, suggesting it could be a potential therapeutic agent against lung cancer. In a flow cytometric analysis, 4βHWE produced cell cycle perturbation in the form of sub-G₁accumulation and slight arrest at the G₂/M phase with 1 μg/mL for 12 and 24 h, respectively. Using flow cytometric and annexin V/propidium iodide immunofluorescence double-staining techniques, these phenomena were proven to be apoptosis and complete G₂/M arrest for H1299 cells treated with 5 μg/mL for 24 h.</p> <p>Conclusions</p> <p>In this study, we demonstrated that golden berry-derived 4βHWE is a potential DNA-damaging and chemotherapeutic agent against lung cancer.</p

Comparative Analysis of Fecal Microbiota in Infants with and without Eczema

Eczema is a chronic form of childhood disorder that is gaining in prevalence in affluent societies. Previous studies hypothesized that the development of eczema is correlated with changes in microbial profile and composition of early life endemic microbiota, but contradictory conclusions were obtained, possibly due to the lack of minimization of apparent non-health related confounders (e.g., age, antibiotic consumption, diet and mode of delivery). In this study, we recruited seven caesarean-delivered and total formula-fed infants, and comparatively examined the early-life endemic microbiota in these infants with and without eczema. Using 16S pyrosequencing, infants' fecal microbiota were observed to comprise Proteobacteria, Firmicutes, Actinobacteria and Bacteroidetes as the four main phyla, and the presence and absence of specific populations within these four phyla are primarily mediated by ageing. Quantitative analysis of bacterial targets on a larger sample size (n = 36 at 1, 3, and 12 months of age) revealed that the abundances of Bifidobacterium and Enterobacteriaceae were different among caesarean-delivered infants with and without eczema, and the bacterial targets may be potential biomarkers that can correlate to the health status of these infants. Our overall findings suggest that the minimization of possible confounders is essential prior to comparative evaluation and correlation of fecal microbiota to health status, and that stool samples collected from caesarean-delivered infants at less than 1 year of age may represent a good cohort to study for potential biomarkers that can distinguish infants with eczema from those without. These findings would greatly facilitate future efforts in understanding the possible pathogenesis behind certain bacterial targets, and may lead to a timely intervention that reduces the occurrence of early life eczema and possibly allergic disorders in later life