Fiber-optic refractometer based on a phase-shifted fiber Bragg grating on a side-hole fiber

A fiber-optic refractive index (RI) sensor based on a π-phaseshifted fiber-Bragg-grating (πFBG) inscribed on a side-hole fiber is presented. The reflection spectrum of the πFBG features two narrow notches associated with the two polarization modes and the spectral spacing of the notches is used for high-sensitivity RI sensing with little temperature cross-sensitivity. The side-hole fiber maintains its outer diameter and mechanical strength. The side-hole fiber is also naturally integrated into a microfluidic system for convenient sample delivery and reduced sample amount. A novel demodulation method based on laser frequency modulation to enhance the sensor dynamic range is proposed and demonstrated

Initial value generation for matchmaking in middle agents

One of the major challenges that agents used in open environments must face is that they must be able to find each other. This is because in an open environment, agents might appear and disappear unpredictably. To address this issue, middle agents have been proposed. The performance of middle agents relies heavily on the matchmaking algorithms used. Matchmaking is the process of finding an appropriate provider for a requester through a middle agent. The practical performance of service provider agents has a significant impact on the matchmaking outcomes of middle agents. Thus the track records of agents in accomplishing similar tasks in the past should be taken into account in matchmaking process. Considering that there are no track records available at the launching of an agent system, this paper discusses some ways to provide reasonable initial values for the track&nbsp; records. With the agents\u27 history and the initial vallies ofthe track records, the performance of matchmaking algorithms can be improved significantly.<br /

Model Inversion Attack via Dynamic Memory Learning

Model Inversion (MI) attacks aim to recover the private training data from the target model, which has raised security concerns about the deployment of DNNs in practice. Recent advances in generative adversarial models have rendered them particularly effective in MI attacks, primarily due to their ability to generate high-fidelity and perceptually realistic images that closely resemble the target data. In this work, we propose a novel Dynamic Memory Model Inversion Attack (DMMIA) to leverage historically learned knowledge, which interacts with samples (during the training) to induce diverse generations. DMMIA constructs two types of prototypes to inject the information about historically learned knowledge: Intra-class Multicentric Representation (IMR) representing target-related concepts by multiple learnable prototypes, and Inter-class Discriminative Representation (IDR) characterizing the memorized samples as learned prototypes to capture more privacy-related information. As a result, our DMMIA has a more informative representation, which brings more diverse and discriminative generated results. Experiments on multiple benchmarks show that DMMIA performs better than state-of-the-art MI attack methods

Robust Automatic Speech Recognition via WavAugment Guided Phoneme Adversarial Training

Developing a practically-robust automatic speech recognition (ASR) is challenging since the model should not only maintain the original performance on clean samples, but also achieve consistent efficacy under small volume perturbations and large domain shifts. To address this problem, we propose a novel WavAugment Guided Phoneme Adversarial Training (wapat). wapat use adversarial examples in phoneme space as augmentation to make the model invariant to minor fluctuations in phoneme representation and preserve the performance on clean samples. In addition, wapat utilizes the phoneme representation of augmented samples to guide the generation of adversaries, which helps to find more stable and diverse gradient-directions, resulting in improved generalization. Extensive experiments demonstrate the effectiveness of wapat on End-to-end Speech Challenge Benchmark (ESB). Notably, SpeechLM-wapat outperforms the original model by 6.28% WER reduction on ESB, achieving the new state-of-the-art

TransAudio: Towards the Transferable Adversarial Audio Attack via Learning Contextualized Perturbations

In a transfer-based attack against Automatic Speech Recognition (ASR) systems, attacks are unable to access the architecture and parameters of the target model. Existing attack methods are mostly investigated in voice assistant scenarios with restricted voice commands, prohibiting their applicability to more general ASR related applications. To tackle this challenge, we propose a novel contextualized attack with deletion, insertion, and substitution adversarial behaviors, namely TransAudio, which achieves arbitrary word-level attacks based on the proposed two-stage framework. To strengthen the attack transferability, we further introduce an audio score-matching optimization strategy to regularize the training process, which mitigates adversarial example over-fitting to the surrogate model. Extensive experiments and analysis demonstrate the effectiveness of TransAudio against open-source ASR models and commercial APIs

Enhance the Visual Representation via Discrete Adversarial Training

Adversarial Training (AT), which is commonly accepted as one of the most effective approaches defending against adversarial examples, can largely harm the standard performance, thus has limited usefulness on industrial-scale production and applications. Surprisingly, this phenomenon is totally opposite in Natural Language Processing (NLP) task, where AT can even benefit for generalization. We notice the merit of AT in NLP tasks could derive from the discrete and symbolic input space. For borrowing the advantage from NLP-style AT, we propose Discrete Adversarial Training (DAT). DAT leverages VQGAN to reform the image data to discrete text-like inputs, i.e. visual words. Then it minimizes the maximal risk on such discrete images with symbolic adversarial perturbations. We further give an explanation from the perspective of distribution to demonstrate the effectiveness of DAT. As a plug-and-play technique for enhancing the visual representation, DAT achieves significant improvement on multiple tasks including image classification, object detection and self-supervised learning. Especially, the model pre-trained with Masked Auto-Encoding (MAE) and fine-tuned by our DAT without extra data can get 31.40 mCE on ImageNet-C and 32.77% top-1 accuracy on Stylized-ImageNet, building the new state-of-the-art. The code will be available at https://github.com/alibaba/easyrobust.Comment: Accepted to NeurIPS 2022, https://github.com/alibaba/easyrobus

Preferential flow in the understory soil of Hippophae rhamnoides at different stump heights

This study aimed to clarify the effects of stumping on preferential flow in the understory soils of Hippophae rhamnoides and to assess appropriate stumping height for optimization of preferential flow. Root properties, soil properties, and preferential flow for different H. rhamnoides stump heights (0, 10, 15, 20 cm, and no-stumping, labeled conditions S1, S2, S3, S4, and CK, respectively) were studied using in situ dye-tracing and laboratory analysis. The results showed that stumping significantly increased preferential flow development. This effect was maximized in condition S3, with dye-tracing coverage (DC) of 36.77%, maximum dye depth (MaxD) of 40.02 cm, uniform infiltration depth (Unifr) of 14.28 cm, preferential flow ratio (PFfr) of 23.85%, and length index (LI) of 96.72%. In terms of root length density (RLD), root mass density (RMD), root surface area density (RSAD), soil water content (SWC), soil total porosity (TP), mean weight diameter (MWD), and soil organic matter (SOM), the conditions were ranked S3&gt;S2&gt;S1&gt;S3&gt;CK; for root average diameter (RAD), they were ranked S3&lt;S2&lt;S1&lt;S4&lt;CK. Structural equation modeling showed that DC was affected directly by TP, MWD, and SWC and indirectly by RAD, RLD, RMD, RSAD, and SOM, explaining up to 89.1% of the variance. Thus, stumping of H. rhamnoides affected soil properties through the mechanism of root development, thereby improving preferential flow development in the soil and soil infiltration. The optimal stump height was 15 cm. These findings are critical for vegetation recovery and for prevention and control of soil erosion in feldspathic sandstone areas

Model-enhanced Vector Index

Embedding-based retrieval methods construct vector indices to search for document representations that are most similar to the query representations. They are widely used in document retrieval due to low latency and decent recall performance. Recent research indicates that deep retrieval solutions offer better model quality, but are hindered by unacceptable serving latency and the inability to support document updates. In this paper, we aim to enhance the vector index with end-to-end deep generative models, leveraging the differentiable advantages of deep retrieval models while maintaining desirable serving efficiency. We propose Model-enhanced Vector Index (MEVI), a differentiable model-enhanced index empowered by a twin-tower representation model. MEVI leverages a Residual Quantization (RQ) codebook to bridge the sequence-to-sequence deep retrieval and embedding-based models. To substantially reduce the inference time, instead of decoding the unique document ids in long sequential steps, we first generate some semantic virtual cluster ids of candidate documents in a small number of steps, and then leverage the well-adapted embedding vectors to further perform a fine-grained search for the relevant documents in the candidate virtual clusters. We empirically show that our model achieves better performance on the commonly used academic benchmarks MSMARCO Passage and Natural Questions, with comparable serving latency to dense retrieval solutions

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead