Drop Loss for Person Attribute Recognition With Imbalanced Noisy-Labeled Samples

Person attribute recognition (PAR) aims to simultaneously predict multiple attributes of a person. Existing deep learning-based PAR methods have achieved impressive performance. Unfortunately, these methods usually ignore the fact that different attributes have an imbalance in the number of noisy-labeled samples in the PAR training datasets, thus leading to suboptimal performance. To address the above problem of imbalanced noisy-labeled samples, we propose a novel and effective loss called drop loss for PAR. In the drop loss, the attributes are treated differently in an easy-to-hard way. In particular, the noisy-labeled candidates, which are identified according to their gradient norms, are dropped with a higher drop rate for the harder attribute. Such a manner adaptively alleviates the adverse effect of imbalanced noisy-labeled samples on model learning. To illustrate the effectiveness of the proposed loss, we train a simple ResNet-50 model based on the drop loss and term it DropNet. Experimental results on two representative PAR tasks (including facial attribute recognition and pedestrian attribute recognition) demonstrate that the proposed DropNet achieves comparable or better performance in terms of both balanced accuracy and classification accuracy over several state-of-the-art PAR methods

The Phonological Process with Two Patterns of Simplified Chinese Characters

This paper analyzed word recognition in two patterns of Chinese characters, cross referenced with word frequency. The patterns were defined as uni-part (semantic radical/component only) and bi-part (including the phonetic radical/component and the semantic radical/component) characters. The interactions of semantic and phonological access in both patterns were inspected. It was observed that in the naming task and the pronunciation-matching task, the subject performance involving the uni-part characters showed longer RT than the bi-part characters. However, with the lexical decision and meaning-matching tasks the uni-part characters showed shorter RT than the bi-part characters. It was also observed that the frequency, which is regarded as a lexical variable, displayed a strong influence. This suggests that Chinese characters require lexical access in all tasks. This study also suggested that the phonological process is primary in visual word recognition; as there is a significant phonological effect in processing the Chinese bi-part characters, resulting in either the facilitation or inhibition of phonology due to the differing demands of the two task

Mobile Foundation Model as Firmware

In today's landscape, smartphones have evolved into hubs for hosting a multitude of deep learning models aimed at local execution. A key realization driving this work is the notable fragmentation among these models, characterized by varied architectures, operators, and implementations. This fragmentation imposes a significant burden on the comprehensive optimization of hardware, system settings, and algorithms. Buoyed by the recent strides in large foundation models, this work introduces a pioneering paradigm for mobile AI: a collaborative management approach between the mobile OS and hardware, overseeing a foundational model capable of serving a broad spectrum of mobile AI tasks, if not all. This foundational model resides within the NPU and remains impervious to app or OS revisions, akin to firmware. Concurrently, each app contributes a concise, offline fine-tuned "adapter" tailored to distinct downstream tasks. From this concept emerges a concrete instantiation known as \sys. It amalgamates a curated selection of publicly available Large Language Models (LLMs) and facilitates dynamic data flow. This concept's viability is substantiated through the creation of an exhaustive benchmark encompassing 38 mobile AI tasks spanning 50 datasets, including domains such as Computer Vision (CV), Natural Language Processing (NLP), audio, sensing, and multimodal inputs. Spanning this benchmark, \sys unveils its impressive performance. It attains accuracy parity in 85\% of tasks, demonstrates improved scalability in terms of storage and memory, and offers satisfactory inference speed on Commercial Off-The-Shelf (COTS) mobile devices fortified with NPU support. This stands in stark contrast to task-specific models tailored for individual applications.Comment: 17 pages, 15 figures, published to ACM MobiCom'2

Liposomal Curcumin Targeting Endometrial Cancer Through the NF-κB Pathway

Background/Aims: Emerging evidence suggests that curcumin possesses chemopreventive properties against various cancers. However, its poor bioavailability limits its clinical application. In this study, we aimed to utilize encapsulation in liposomes (Lipo) as a strategy for the clinical administration of curcumin for endometrial carcinoma (EC). Methods: Curcumin was encapsulated in a liposomal delivery system to prepare a formulation of liposomal curcumin (LC). EC cell lines Ishikawa and HEC-1 were treated with the compound and cell proliferation was measured using MTT assay. Hoechst 33258 staining assay and flow cytometry were used to detect apoptosis of the cells. Wound healing and cell invasion assays were employed to monitor cell motility. Underlying target signaling, such as NF-κB, caspases, and MMPs, were further studied via qRT-PCR and western blot. Thereafter, a zebrafish model was used to assess the toxicity of LC. Finally, a zebrafish transplantation tumor model of EC was grown and treated with LC. Tumors were monitored and harvested to study the expression of NF-κB. Results: The formation of LC was successfully developed with excellent purity and physical properties. In vitro, LC resulted in dose-dependent inhibition of proliferation, induction of apoptosis, and suppression of Ishikawa and HEC-1 cell motility. LC treatment also suppressed the activation and/or expression of NF-κB, caspase-3, and MMP-9. No demonstrable toxicity was found in the zebrafish model and tumors were suppressed after treatment with LC. PCR analysis also showed down-regulated expression of NF-κB. Conclusions: LC was successfully prepared and played biological roles against EC probably through negative regulation of the NF-κB pathway in vitro and in vivo, which demonstrates its potential therapeutic effects in EC

X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in Classification

In recent years, few-shot and zero-shot learning, which learn to predict labels with limited annotated instances, have garnered significant attention. Traditional approaches often treat frequent-shot (freq-shot; labels with abundant instances), few-shot, and zero-shot learning as distinct challenges, optimizing systems for just one of these scenarios. Yet, in real-world settings, label occurrences vary greatly. Some of them might appear thousands of times, while others might only appear sporadically or not at all. For practical deployment, it is crucial that a system can adapt to any label occurrence. We introduce a novel classification challenge: X-shot, reflecting a real-world context where freq-shot, few-shot, and zero-shot labels co-occur without predefined limits. Here, X can span from 0 to positive infinity. The crux of X-shot centers on open-domain generalization and devising a system versatile enough to manage various label scenarios. To solve X-shot, we propose BinBin (Binary INference Based on INstruction following) that leverages the Indirect Supervision from a large collection of NLP tasks via instruction following, bolstered by Weak Supervision provided by large language models. BinBin surpasses previous state-of-the-art techniques on three benchmark datasets across multiple domains. To our knowledge, this is the first work addressing X-shot learning, where X remains variable

Dosimetric Comparison between 9F-IMRT, Single Arc VMAT and Dual Arc VMAT for Postoperative Cervical Cancer Patients with Para-aortic Lymph Node Metastasis

Background: The aim of this study was to compare the dosimetric parameters of 9 field intensity modulated radiotherapy (9F-IMRT) and two kinds of volumetric modulated arc therapy (VMAT) for postoperative cervical cancer patients with paraaortic lymph node (PALN) metastasis, and to provide the reasonable selection of the cervical cancer patients with para-aortic lymph node metastasis who need postoperative radiotherapy in clinic. Methods: Fifteen patients of postoperative cervical cancer patients with para-aortic lymph node metastasis were planed with the same dose prescription and optimization parameters by means of 9-field IMRT (9F-IMRT), a single arc (Arcl) and dual arc (Arc2) VMAT, respectively. The dosimetric differences of planning target volume (PTV), organs at risk(OAR), the number of monitor units (MUs) and treatment time were compared among the three treatment plans. Results: The conformity index (CI) and the homogeneity index (HI) of PTV for Arc2 plans were superior to 9F-IMRT (P < 0.05). The V40, V50 of the rectum, V50 of the intestine, bladder and femoral head for Arc2 plans were better than 9F-IMRT plans. V50 of the femoral head and the mean dose of kidney for Arc1 and Arc2 plans were all better than 9F-IMRT plans. The number of MUs of 9F-IMRT plans (1,105.27 ± 107.12) was significantly higher than the Arc1 plans (755.23 ± 225.98) and Arc2 plans (967.24 ± 198.41), the difference was statistically significant (F = 12.736, P < 0.01). The treatment time of 9F-IMRT plans (430.47 ± 36.45) was significantly higher than the Arc1 plans (304.20 ± 48.42) and Arc2 plans (332.93 ± 47.62), the difference was statistically significant (F = 33.180, P < 0.01). Conclusion: Compared to 9F-IMRT, Arc1 and Arc2 plans can better to meet the clinical requirements for the postoperative cervical cancer patients with para-aortic lymph node metastasis. And the Arc2 VMAT plans is superior to Arc1 VMAT plans, there will significantly improve the treatment efficiency

Establishment of a Novel Bladder Cancer Xenograft Model in Humanized Immunodeficient Mice

Background/Aims: The aim of this study was to develop a novel model by transplanting human bladder cancer xenografts into humanized immunodeficient mice (SCID). Methods: The animals first underwent sublethal irradiation and then were subjected to simultaneous transplantation of human lymphocytes (5 × 107 cells/mouse i.p.) and human bladder cancer cells (3 × 106 cells/mouse s.c.). Results: The xenografts developed in all 12 mice that had received bladder cancer BIU-87 cells, and the tumor specimens were evaluated histologically. All 6 model mice expressed human CD3 mRNA and/or protein in the peripheral blood, spleens and xenografts. The mean proportion of human CD3+ cells was 19% with a level of human IgG 532.4µ/ml in the peripheral blood at Week 6 after transplant inoculation. The re-constructed human immune system in these mice was confirmed to be functional by individual in vitro testing of their proliferative, secretory and cytotoxic responses. Conclusion: The successful engraftment of the human bladder cancer xenografts and the establishment of the human immune system in our in vivo model described here may provide a useful tool for the development of novel therapeutic strategies targeting at bladder cancer

Circular RNA hsa_circ_0007444 inhibits ovarian cancer progression through miR-23a-3p/DICER1 axis

Ovarian cancer is the second leading cause of death in women with gynecological malignancy in China. Circular RNAs are a class of noncoding regulatory RNAs reported to be involved in cancer development and progression. Previous studies, including our own, have indicated that hsa_circ_0007444 is downregulated in ovarian cancer tissues. This study aims to elucidate the function and mechanism of hsa_circ_0007444 in ovarian cancer progression. The expression of hsa_circ_0007444 is determined by quantitative real-time PCR (qRT-PCR). Cell proliferation, invasion, migration and apoptosis are examined by cell counting-kit 8 (CCK-8), transwell and flow cytometry assays. Tumor growth and metastasis are assessed in vivo using Balb/c nude mouse xenograft model and tail vein injection model. And the mechanism of action of hsa_circ_0007444 is analysed by RNA-binding protein immunoprecipitation (RIP), luciferase reporter and rescue assays. hsa_circ_0007444 is downregulated in ovarian cancer tissues and cell lines compared with that in normal ovarian tissues and normal epithelial cell line. Gain- and loss-of-function results indicate that hsa_circ_0007444 inhibits cell proliferation, invasion, migration and increases cell apoptosis of ovarian cancer cells in vitro, and inhibits tumor growth and lung metastasis in vivo. Mechanistically, hsa_circ_0007444 can interact with AGO2 and sponge miR-23a-3p, thereby upregulating DICER1 expression, which is an important tumor suppressor in ovarian cancer. And miR-23a-3p mimics can rescue the inhibitory effect of hsa_circ_0007444 on ovarian cancer cell proliferation, invasion and migration. Therefore, hsa_circ_0007444 can inhibit ovarian cancer progression through the hsa_circ_0007444/miR-23a-3p/DICER1 axis

Robustness of Learning from Task Instructions

Traditional supervised learning mostly works on individual tasks and requires training on a large set of task-specific examples. This paradigm seriously hinders the development of task generalization since preparing a task-specific example set is costly. To build a system that can quickly and easily generalize to new tasks, task instructions have been adopted as an emerging trend of supervision recently. These instructions give the model the definition of the task and allow the model to output the appropriate answer based on the instructions and inputs. However, task instructions are often expressed in different forms, which can be interpreted from two threads: first, some instructions are short sentences and are pretrained language model (PLM) oriented, such as prompts, while other instructions are paragraphs and are human-oriented, such as those in Amazon MTurk; second, different end-users very likely explain the same task with instructions of different textual expressions. A robust system for task generalization should be able to handle any new tasks regardless of the variability of instructions. However, the system robustness in dealing with instruction-driven task generalization is still unexplored. This work investigates the system robustness when the instructions of new tasks are (i) manipulated, (ii) paraphrased, or (iii) from different levels of conciseness. To our knowledge, this is the first work that systematically studies how robust a PLM is when it is supervised by instructions with different factors of variability.Comment: ACL'23 Finding Accepte