Measuring impurity of water by ultrasound and water treatment

The aim of this paper is to demonstrate the idea of a system to measure the quality of water and treatwater. The impurities of water can be divided into two parts, solvable and insolvable generally. The devicesof measuring the impurity solvable and insolvable using different characteristics of acoustic wave,propagating velocity in water and reflection of ultrasound, will be introduced respectively in this paper. Alsothe device of treating water by pulsed discharge as the feedback of the system will be illustrated

Enhancing General Face Forgery Detection via Vision Transformer with Low-Rank Adaptation

Nowadays, forgery faces pose pressing security concerns over fake news, fraud, impersonation, etc. Despite the demonstrated success in intra-domain face forgery detection, existing detection methods lack generalization capability and tend to suffer from dramatic performance drops when deployed to unforeseen domains. To mitigate this issue, this paper designs a more general fake face detection model based on the vision transformer(ViT) architecture. In the training phase, the pretrained ViT weights are freezed, and only the Low-Rank Adaptation(LoRA) modules are updated. Additionally, the Single Center Loss(SCL) is applied to supervise the training process, further improving the generalization capability of the model. The proposed method achieves state-of-the-arts detection performances in both cross-manipulation and cross-dataset evaluations

Heterogeneous Domain Generalization via Domain Mixup

One of the main drawbacks of deep Convolutional Neural Networks (DCNN) is that they lack generalization capability. In this work, we focus on the problem of heterogeneous domain generalization which aims to improve the generalization capability across different tasks, which is, how to learn a DCNN model with multiple domain data such that the trained feature extractor can be generalized to supporting recognition of novel categories in a novel target domain. To solve this problem, we propose a novel heterogeneous domain generalization method by mixing up samples across multiple source domains with two different sampling strategies. Our experimental results based on the Visual Decathlon benchmark demonstrates the effectiveness of our proposed method. The code is released in \url{https://github.com/wyf0912/MIXALL

Mechanism of Action and Clinical Potential of Fingolimod for the Treatment of Stroke

Fingolimod (FTY720) is an orally bio-available immunomodulatory drug currently approved by the FDA for the treatment of multiple sclerosis. Currently, there is a significant interest in the potential benefits of FTY720 on stroke outcomes. FTY720 and the sphingolipid signaling pathway it modulates has a ubiquitous presence in the central nervous system and both rodent models and pilot clinical trials seem to indicate that the drug may improve overall functional recovery in different stroke subtypes. Although the precise mechanisms behind these beneficial effects are yet unclear, there is evidence that FTY720 has a role in regulating cerebrovascular responses, blood brain barrier permeability, and cell survival in the event of cerebrovascular insult. In this article, we critically review the data obtained from the latest laboratory findings and clinical trials involving both ischemic and hemorrhagic stroke, and attempt to form a cohesive picture of FTY720’s mechanisms of action in strok

Cross-Lingual Adaptation for Type Inference

Deep learning-based techniques have been widely applied to the program analysis tasks, in fields such as type inference, fault localization, and code summarization. Hitherto deep learning-based software engineering systems rely thoroughly on supervised learning approaches, which require laborious manual effort to collect and label a prohibitively large amount of data. However, most Turing-complete imperative languages share similar control- and data-flow structures, which make it possible to transfer knowledge learned from one language to another. In this paper, we propose cross-lingual adaptation of program analysis, which allows us to leverage prior knowledge learned from the labeled dataset of one language and transfer it to the others. Specifically, we implemented a cross-lingual adaptation framework, PLATO, to transfer a deep learning-based type inference procedure across weakly typed languages, e.g., Python to JavaScript and vice versa. PLATO incorporates a novel joint graph kernelized attention based on abstract syntax tree and control flow graph, and applies anchor word augmentation across different languages. Besides, by leveraging data from strongly typed languages, PLATO improves the perplexity of the backbone cross-programming-language model and the performance of downstream cross-lingual transfer for type inference. Experimental results illustrate that our framework significantly improves the transferability over the baseline method by a large margin

Privacy-Preserving Constrained Domain Generalization via Gradient Alignment

Deep neural networks (DNN) have demonstrated unprecedented success for medical imaging applications. However, due to the issue of limited dataset availability and the strict legal and ethical requirements for patient privacy protection, the broad applications of medical imaging classification driven by DNN with large-scale training data have been largely hindered. For example, when training the DNN from one domain (e.g., with data only from one hospital), the generalization capability to another domain (e.g., data from another hospital) could be largely lacking. In this paper, we aim to tackle this problem by developing the privacy-preserving constrained domain generalization method, aiming to improve the generalization capability under the privacy-preserving condition. In particular, We propose to improve the information aggregation process on the centralized server-side with a novel gradient alignment loss, expecting that the trained model can be better generalized to the "unseen" but related medical images. The rationale and effectiveness of our proposed method can be explained by connecting our proposed method with the Maximum Mean Discrepancy (MMD) which has been widely adopted as the distribution distance measurement. Experimental results on two challenging medical imaging classification tasks indicate that our method can achieve better cross-domain generalization capability compared to the state-of-the-art federated learning methods

Generalization Beyond Feature Alignment: Concept Activation-Guided Contrastive Learning

Learning invariant representations via contrastive learning has seen state-of-the-art performance in domain generalization (DG). Despite such success, in this paper, we find that its core learning strategy -- feature alignment -- could heavily hinder model generalization. Drawing insights in neuron interpretability, we characterize this problem from a neuron activation view. Specifically, by treating feature elements as neuron activation states, we show that conventional alignment methods tend to deteriorate the diversity of learned invariant features, as they indiscriminately minimize all neuron activation differences. This instead ignores rich relations among neurons -- many of them often identify the same visual concepts despite differing activation patterns. With this finding, we present a simple yet effective approach, Concept Contrast (CoCo), which relaxes element-wise feature alignments by contrasting high-level concepts encoded in neurons. Our CoCo performs in a plug-and-play fashion, thus it can be integrated into any contrastive method in DG. We evaluate CoCo over four canonical contrastive methods, showing that CoCo promotes the diversity of feature representations and consistently improves model generalization capability. By decoupling this success through neuron coverage analysis, we further find that CoCo potentially invokes more meaningful neurons during training, thereby improving model learning

Insights Into Forensic Features and Genetic Structures of Guangdong Maoming Han Based on 27 Y-STRs

Maoming is located in the southwest region of Guangdong Province and is the cradle of Gaoliang culture, which is the representative branch of Lingnan cultures. Historical records showed that the amalgamations between Gaoliang aborigines and distinct ethnic minorities had some influences on the shaping of Gaoliang culture, especially for the local Tai-kadai language-speaking Baiyue and Han Chinese from Central China. However, there is still no exact genetic evidence for the influences on the genetic pool of Maoming Han, and the genetic relationships between Maoming Han and other Chinese populations are still unclear. Hence, in order to get a better understanding of the paternal genetic structures and characterize the forensic features of 27 Y-chromosomal short tandem repeats (Y-STRs) in Han Chinese from Guangdong Maoming, we firstly applied the AmpFLSTR® Yfiler® Plus PCR Amplification Kit (Thermo Fisher Scientific, Waltham, MA, United States) to genotype the haplotypes in 431 Han males residing in Maoming. A total of 263 different alleles were determined across all 27 Y-STRs with the corresponding allelic frequencies from 0.0004 to 0.7401, and the range of genetic diversity (GD) was 0.4027 (DYS391) to 0.9596 (DYS385a/b). In the first batch of 27 Yfiler data in Maoming Han, 417 distinct haplotypes were discovered, and nine off-ladder alleles were identified at six Y-STRs; in addition, no copy number variant or null allele was detected. The overall haplotype diversity (HD) and discrimination capacity (DC) of 27 Yfiler were 0.9997 and 0.9675, respectively, which demonstrated that the 6-dye and 27-plex system has sufficient system effectiveness for forensic applications in Maoming Han. What is more, the phylogenetic analyses indicated that Maoming Han, which is a Southern Han Chinese population, has a close relationship with Meizhou Kejia, which uncovered that the role of the gene flows from surrounding Han populations in shaping the genetic pool of Maoming Han cannot be ignored. From the perspectives of genetics, linguistics, and geographies, the genetic structures of Han populations correspond to the patterns of the geographical-scale spatial distributions and the relationships of language families. Nevertheless, no exact genetic evidence supports the intimate relationships between Maoming Han and Tai-Kadai language-speaking populations and Han populations of Central Plains in the present study