Quantile and pseudo-Huber Tensor Decomposition

This paper studies the computational and statistical aspects of quantile and pseudo-Huber tensor decomposition. The integrated investigation of computational and statistical issues of robust tensor decomposition poses challenges due to the non-smooth loss functions. We propose a projected sub-gradient descent algorithm for tensor decomposition, equipped with either the pseudo-Huber loss or the quantile loss. In the presence of both heavy-tailed noise and Huber's contamination error, we demonstrate that our algorithm exhibits a so-called phenomenon of two-phase convergence with a carefully chosen step size schedule. The algorithm converges linearly and delivers an estimator that is statistically optimal with respect to both the heavy-tailed noise and arbitrary corruptions. Interestingly, our results achieve the first minimax optimal rates under Huber's contamination model for noisy tensor decomposition. Compared with existing literature, quantile tensor decomposition removes the requirement of specifying a sparsity level in advance, making it more flexible for practical use. We also demonstrate the effectiveness of our algorithms in the presence of missing values. Our methods are subsequently applied to the food balance dataset and the international trade flow dataset, both of which yield intriguing findings

Personal Attribute Prediction from Conversations

Personal knowledge bases (PKBs) are critical to many applications, such as Web-based chatbots and personalized recommendation. Conversations containing rich personal knowledge can be regarded as a main source to populate the PKB. Given a user, a user attribute, and user utterances from a conversational system, we aim to predict the personal attribute value for the user, which is helpful for the enrichment of PKBs. However, there are three issues existing in previous studies: (1) manually labeled utterances are required for model training; (2) personal attribute knowledge embedded in both utterances and external resources is underutilized; (3) the performance on predicting some difficult personal attributes is unsatisfactory. In this paper, we propose a framework DSCGN based on the pre-trained language model with a noise-robust loss function to predict personal attributes from conversations without requiring any labeled utterances. We yield two categories of supervision, i.e., document-level supervision via a distant supervision strategy and contextualized word-level supervision via a label guessing method, by mining the personal attribute knowledge embedded in both unlabeled utterances and external resources to fine-tune the language model. Extensive experiments over two real-world data sets (i.e., a profession data set and a hobby data set) show our framework obtains the best performance compared with all the twelve baselines in terms of nDCG and MRR.Comment: Accepted by WWW'22 Companio

Low-resource Personal Attribute Prediction from Conversation

Personal knowledge bases (PKBs) are crucial for a broad range of applications such as personalized recommendation and Web-based chatbots. A critical challenge to build PKBs is extracting personal attribute knowledge from users' conversation data. Given some users of a conversational system, a personal attribute and these users' utterances, our goal is to predict the ranking of the given personal attribute values for each user. Previous studies often rely on a relative number of resources such as labeled utterances and external data, yet the attribute knowledge embedded in unlabeled utterances is underutilized and their performance of predicting some difficult personal attributes is still unsatisfactory. In addition, it is found that some text classification methods could be employed to resolve this task directly. However, they also perform not well over those difficult personal attributes. In this paper, we propose a novel framework PEARL to predict personal attributes from conversations by leveraging the abundant personal attribute knowledge from utterances under a low-resource setting in which no labeled utterances or external data are utilized. PEARL combines the biterm semantic information with the word co-occurrence information seamlessly via employing the updated prior attribute knowledge to refine the biterm topic model's Gibbs sampling process in an iterative manner. The extensive experimental results show that PEARL outperforms all the baseline methods not only on the task of personal attribute prediction from conversations over two data sets, but also on the more general weakly supervised text classification task over one data set.Comment: Accepted by AAAI'2

Capturing Popularity Trends: A Simplistic Non-Personalized Approach for Enhanced Item Recommendation

Recommender systems have been gaining increasing research attention over the years. Most existing recommendation methods focus on capturing users' personalized preferences through historical user-item interactions, which may potentially violate user privacy. Additionally, these approaches often overlook the significance of the temporal fluctuation in item popularity that can sway users' decision-making. To bridge this gap, we propose Popularity-Aware Recommender (PARE), which makes non-personalized recommendations by predicting the items that will attain the highest popularity. PARE consists of four modules, each focusing on a different aspect: popularity history, temporal impact, periodic impact, and side information. Finally, an attention layer is leveraged to fuse the outputs of four modules. To our knowledge, this is the first work to explicitly model item popularity in recommendation systems. Extensive experiments show that PARE performs on par or even better than sophisticated state-of-the-art recommendation methods. Since PARE prioritizes item popularity over personalized user preferences, it can enhance existing recommendation methods as a complementary component. Our experiments demonstrate that integrating PARE with existing recommendation methods significantly surpasses the performance of standalone models, highlighting PARE's potential as a complement to existing recommendation methods. Furthermore, the simplicity of PARE makes it immensely practical for industrial applications and a valuable baseline for future research.Comment: 9 pages, 5 figure

Vimentin intermediate filaments and filamentous actin form unexpected interpenetrating networks that redefine the cell cortex

The cytoskeleton of eukaryotic cells is primarily composed of networks of filamentous proteins, F-actin, microtubules, and intermediate filaments. Interactions among the cytoskeletal components are important in determining cell structure and in regulating cell functions. For example, F-actin and microtubules work together to control cell shape and polarity, while the subcellular organization and transport of vimentin intermediate filament (VIF) networks depend on their interactions with microtubules. However, it is generally thought that F-actin and VIFs form two coexisting but separate networks that are independent due to observed differences in their spatial distribution and functions. In this paper, we present a closer investigation of both the structural and functional interplay between the F-actin and VIF cytoskeletal networks. We characterize the structure of VIFs and F-actin networks within the cell cortex using structured illumination microscopy and cryo-electron tomography. We find that VIFs and F-actin form an interpenetrating network (IPN) with interactions at multiple length scales, and VIFs are integral components of F-actin stress fibers. From measurements of recovery of cell contractility after transient stretching, we find that the IPN structure results in enhanced contractile forces and contributes to cell resilience. Studies of reconstituted networks and dynamic measurements in cells suggest direct and specific associations between VIFs and F-actin. From these results, we conclude that VIFs and F-actin work synergistically, both in their structure and in their function. These results profoundly alter our understanding of the contributions of the components of the cytoskeleton, particularly the interactions between intermediate filaments and F-actin

On-Chip Super-Resolution Imaging with Fluorescent Polymer Films

Wide field of view (FOV), label-free super-resolution imaging is demonstrated using a specially designed waveguide chip that can illuminate a sample with multi-colour evanescent waves travelling along different directions. The method is enabled by a polymer fluorescent film which emits over a broad wavelength range. Its polygonal geometry ensures coverage over all illumination directions, enabling high fidelity image reconstruction whilst minimizing distortion and image blurring. By frequency shifting and iterative stitching of different spatial frequencies in Fourier space, the reconstruction of two dimensional samples is achieved without distortion over wide FOVs. The fabrication process is facile and compatible with conventional semiconductor-fabrication methods. The super-resolution chip (SRC) can thus be produced with high yield, offer opportunities for potential conjunction of super-resolution techniques integrated optical circuits or for the development of single-use diagnostic kits

A geostationary orbit microwave multi-channel radiometer

The geostationary orbit microwave multi-channel radiometer has the advantages of high real-time performance and large coverage, which plays an important role in typhoon, strong precipitation detection, and medium-to-short-term meteorological/oceanic forecasting. However, due to the difficulty in engineering development of the payload, its application on-orbit has not yet been achieved at present. To satisfy the requirements of fine and quantitative application of satellite observation data, a geostationary orbit microwave multi-channel radiometer with a 10-m-caliber is developed, in which the spatial resolution at horizontal polarization is better than 24 km at 54 GHz. In geostationary orbit microwave multi-channel radiometer, a quasi-optical feed network covering nearly 28 frequency octave bands and ranging from 23.8 to 664 GHz is proposed to solve the technical problem of multi-frequency sharing in the system. Meanwhile, a high-precision reflector preparation method and a high-precision unfolding scheme are proposed, which are considered as a solution for the large-diameter reflector with a high maintaining surface accuracy. A high-precision antenna prototype with 0.54-m is developed, and the tests are performed to verify the key technologies, such as the preparation of high-precision grating reflectors at the micron level, high surface accuracy detection, and sub-millimeter wave antenna electrical performance testing. The results indicate that measured main beam efficiency of the 664 GHz antenna is better than 95.5%. In addition, the system sensitivity is greater than 1.5 K, and the calibration accuracy is better than 1.8 K, according to the results of an analysis of the multi-channel radiometer’s essential parameters and calibration errors

An ethylene biosynthesis enzyme controls quantitative variation in maize ear length and kernel yield.

Maize ear size and kernel number differ among lines, however, little is known about the molecular basis of ear length and its impact on kernel number. Here, we characterize a quantitative trait locus, qEL7, to identify a maize gene controlling ear length, flower number and fertility. qEL7 encodes 1-aminocyclopropane-1- carboxylate oxidase2 (ACO2), a gene that functions in the final step of ethylene biosynthesis and is expressed in specific domains in developing inflorescences. Confirmation of qEL7 by gene editing of ZmACO2 leads to a reduction in ethylene production in developing ears, and promotes meristem and flower development, resulting in a ~13.4% increase in grain yield per ear in hybrids lines. Our findings suggest that ethylene serves as a key signal in inflorescence development, affecting spikelet number, floral fertility, ear length and kernel number, and also provide a tool to improve grain productivity by optimizing ethylene levels in maize or in other cereals