Reviewerly/exHarmony huggingface dataset:
The Analysis of Global RMB Exchange Rate Forecasting and Risk Early Warning Using ARIMA and CNN Model

The purposes are to predict exchange rate fluctuations more accurately and enhance Chinese enterprises’ ability to avoid exchange rate risks. Renminbi (RMB) exchange rate fluctuation’s prediction methods are studied based on data mining technology. The Auto-Regressive Integrated Moving Average (ARIMA) model is introduced first using a modeling method that combines linear and nonlinear models. The linear prediction is obtained by the ARIMA model’s application in the RMB exchange rate’s dynamic fluctuation analysis. The nonlinear residual prediction is obtained by integrating the ARIMA model with the convolutional neural network (CNN) algorithm. The RMB exchange rate fluctuations’ influence mechanism on China’s economic growth is explored by theoretical analysis and empirical research. The US dollar’s daily central parity rate (USD) data against the RMB from September 2015 to March 2019 are selected for model verification, obtaining the exchange rate’s logarithmic return sequence (RUSD).
Ultralow-loss waveguide crossing for photonic integrated circuits by using inverted tapers

An ultralow-loss silicon planar waveguide crossing operating in the O-band was experimentally demonstrated based on the Gaussian beam synthesis method. Elliptical parabolic inverted tapers were introduced in our design to reduce the crossing loss. According to the measurement results, the proposed device exhibits an insertion loss of 0.008 dB, which is the lowest reported loss for planar silicon waveguide crossings operating in the O-band. The device exhibits a low crosstalk below -40 dB over a 40 nm wavelength range with a compact footprint of 18 × 18 µm2 and can be fabricated in a complementary metal-oxide-semiconductor-compatible process.
Aligning Source Visual and Target Language Domains for Unpaired Video Captioning

Training supervised video captioning model requires coupled video-caption pairs. However, for many targeted languages, sufficient paired data are not available. To this end, we introduce the unpaired video captioning task aiming to train models without coupled video-caption pairs in target language. To solve the task, a natural choice is to employ a two-step pipeline system: first utilizing video-to-pivot captioning model to generate captions in pivot language and then utilizing pivot-to-target translation model to translate the pivot captions to the target language. However, in such a pipeline system, 1) visual information cannot reach the translation model, generating visual irrelevant target captions; 2) the errors in the generated pivot captions will be propagated to the translation model, resulting in disfluent target captions. To address these problems, we propose the Unpaired Video Captioning with Visual Injection system (UVC-VI). UVC-VI first introduces the Visual Injection Module (VIM), which aligns source visual and target language domains to inject the source visual information into the target language domain. Meanwhile, VIM directly connects the encoder of the video-to-pivot model and the decoder of the pivot-to-target model, allowing end-to-end inference by completely skipping the generation of pivot captions. To enhance the cross-modality injection of the VIM, UVC-VI further introduces a pluggable video encoder, i.e., Multimodal Collaborative Encoder (MCE). The experiments show that UVC-VI outperforms pipeline systems and exceeds several supervised systems. Furthermore, equipping existing supervised systems with our MCE can achieve 4% and 7% relative margins on the CIDEr scores to current state-of-the-art models on the benchmark MSVD and MSR-VTT datasets, respectively.
Multiplication sampling moire method for full-field deformation measurement of composite materials

A multiplication sampling moire (MSM) method was proposed for robust deformation distribution measurement by performing phase analysis of the second harmonic (second-order frequency) of a single grating pattern. The MSM method has a very strong noise immunity because the second harmonic spectrum is far from the low-frequency region of the background noise in the frequency domain. Phase analysis of an experimental grid image on a carbon fiber-reinforced plastic (CFRP) specimen indicated that the MSM method effectively solved the problem of non-negligible phase measurement errors of conventional methods that extract the fundamental frequency of the grating, in the case of severe local noise. The displacement and strain distributions of CFRP in a tensile test were successfully measured. This method is suitable for deformation measurement of various composite materials.
Ditch the Gold Standard: Re-evaluating Conversational Question Answering

Conversational question answering aims to provide natural-language answers to users in information-seeking conversations.Existing conversational QA benchmarks compare models with pre-collected human-human conversations, using ground-truth answers provided in conversational history.It remains unclear whether we can rely on this static evaluation for model development and whether current systems can well generalize to real-world human-machine conversations.In this work, we conduct the first large-scale human evaluation of state-of-the-art conversational QA systems, where human evaluators converse with models and judge the correctness of their answers.We find that the distribution of humanmachine conversations differs drastically from that of human-human conversations, and there is a disagreement between human and goldhistory evaluation in terms of model ranking.We further investigate how to improve automatic evaluations, and propose a question rewriting mechanism based on predicted history, which better correlates with human judgments.Finally, we analyze the impact of various modeling strategies and discuss future directions towards building better conversational question answering systems.
TA-SBERT: Token Attention Sentence-BERT for Improving Sentence Representation

A sentence embedding vector can be obtained by connecting a global average pooling (GAP) to a pre-trained language model. The problem of such a sentence embedding vector using a GAP is that it is generated with the same weight for all words appearing in the sentence. We propose a novel sentence embedding-method-based model Token Attention-SentenceBERT (TA-SBERT) to address this problem. The rationale of TA-SBERT is to enhance the performance of sentence embedding by introducing three strategies. First, we convert the base form while preprocessing the input sentence to reduce misunderstanding. Second, we propose a novel Token Attention (TA) technique that distinguishes important words to produce more informative sentence vectors. Third, we increase stability of fine-tuning to avoid catastrophic forgetting by adding a reconstruction loss to the word embedding vector. Extensive ablation studies demonstrate that our TA-SBERT outperforms the original SentenceBERT (SBERT) in the sentence vector evaluation using semantic textual similarity (STS) tasks and the SentEval toolkit.
Fooling the Eyes of Autonomous Vehicles: Robust Physical Adversarial Examples Against Traffic Sign Recognition Systems

Adversarial Examples (AEs) can deceive Deep Neural Networks (DNNs) and have received a lot of attention recently. However, majority of the research on AEs is in the digital domain and the adversarial patches are static, which is very different from many real-world DNN applications such as Traffic Sign Recognition (TSR) systems in autonomous vehicles. In TSR systems, object detectors use DNNs to process streaming video in real time. From the view of object detectors, the traffic sign`s position and quality of the video are continuously changing, rendering the digital AEs ineffective in the physical world. In this paper, we propose a systematic pipeline to generate robust physical AEs against real-world object detectors. Robustness is achieved in three ways. First, we simulate the in-vehicle cameras by extending the distribution of image transformations with the blur transformation and the resolution transformation. Second, we design the single and multiple bounding boxes filters to improve the efficiency of the perturbation training. Third, we consider four representative attack vectors, namely Hiding Attack, Appearance Attack, Non-Target Attack and Target Attack. We perform a comprehensive set of experiments under a variety of environmental conditions, and considering illuminations in sunny and cloudy weather as well as at night. The experimental results show that the physical AEs generated from our pipeline are effective and robust when attacking the YOLO v5 based TSR system. The attacks have good transferability and can deceive other state-of-the-art object detectors. We launched HA and NTA on a brand-new 2021 model vehicle. Both attacks are successful in fooling the TSR system, which could be a life-threatening case for autonomous vehicles. Finally, we discuss three defense mechanisms based on image preprocessing, AEs detection, and model enhancing.
Genetic Programming for Image Classification: A New Program Representation With Flexible Feature Reuse

Extracting effective features from images is crucial for image classification, but it is challenging due to high variations across images. Genetic programming (GP) has become a promising machine-learning approach to feature learning in image classification. The representation of existing GP-based image classification methods is usually the tree-based structure. These methods typically learn useful image features according to the output of the GP program’s root node. However, they are not flexible enough in feature learning since the features produced by internal nodes of the GP program have seldom been directly used. In this article, we propose a new image classification approach using GP with a new program structure, which can flexibly reuse features generated from different nodes, including internal nodes of the GP program. The new method can automatically learn various informative image features based on the new function set and terminal set for effective and efficient image classification. Furthermore, instead of relying on a predefined classification algorithm, the proposed approach can automatically select a suitable classification algorithm based on the learned features and conduct classification simultaneously in a single evolved GP program for an image classification task. The experimental results on 12 benchmark datasets of varying difficulty suggest that the new approach achieves better performance than many state-of-the-art methods. Further analysis demonstrates the effectiveness and efficiency of the flexible feature reuse in the proposed approach. The analysis of evolved GP programs/solutions shows their potentially high interpretability.
NC-Max: Breaking the Security-Performance Tradeoff in Nakamoto Consensus

First implemented in Bitcoin, Nakamoto Consensus (NC) is the most influential consensus protocol in cryptocurrencies despite all the alternative protocols designed afterward.Nevertheless, NC is trapped by a security-performance tradeoff.While existing efforts mostly attempt to break this tradeoff via abandoning or adjusting NC's backbone protocol, we alternatively forward the relevance of the network layer.We identify and experimentally prove that the crux resides with the prolonged block propagation latency caused by not-yet-propagated transactions.We thus present a two-step mechanism to confirm only fully-propagated transactions, and therefore remove the limits upon NC's performance imposed by its security demands, realizing NC's untapped potential.Implementing this two-step mechanism, we propose NC-Max, whose (1) security is analyzed, proving that it provides stronger resistance than NC against transaction withholding attacks, and (2) performance is evaluated, showing that it exhausts the full throughput supported by the network, and shortens the transaction confirmation latency by 3.0 to 6.6 times compared to NC without compromising security.NC-Max is implemented in Nervos CKB, a public permissionless blockchain.
A Fast and Effective Transformer for Human Pose Estimation

Most of the existing human pose estimation methods improve accuracy by constantly increasing computational resources. However, balancing the efficiency and efficacy of the model is the key to enhancing the real application value. In this work, we present a Fast and Effective Transformer model to ensure the efficiency and efficacy of the model, called FET. Specifically, the FET consists of three parts: Feature Extraction Module (FEM), Feature Interaction Module (FIM) and Feature Decode Module (FDM). The FEM is used to efficiently extract low-level features from input images. Unlike CNN-based strategies, the FIM enables our model to capture global dependencies by self-attention, thus improving the accuracy for human pose estimation. The FDM is a multistage way that gradually recovers the size of the features to obtain a higher-quality target heatmap. In addition, Feature Squeeze Attention is introduced in the FET to further improve the overall performance of our model. Extensive experiments show that our method is 1.7× and 7× faster than SimpleBaseline and HRNet-32, respectively, while achieving comparable or even better results with the most state-of-the-art methods on the COCO dataset and the MPII dataset.
Emergence of solitons from many-body photon bound states in quantum nonlinear media

Solitons are known to occur in the context of atom-light interaction via the well-known semiclassical phenomenon of self-induced transparency (SIT). Separately, in the regime where both light and atoms are fully treated quantum mechanically, quantum few-photon bound states are known to be a ubiquitous phenomenon that arises in different systems such as atoms coupled to chiral or bidirectional waveguides, and in Rydberg atomic media. In the specific case of two-level atoms coupled to a chiral waveguide, a recent analysis based on Bethe ansatz has established that SIT emerges from the quantum realm as a superposition of quantum many-photon bound states. Beyond this case, however, the nature of any connection between the full quantum many-body regime and semiclassical behavior has not been established. Here, we employ a general spin-model formulation of quantum atom-light interfaces to numerically investigate this problem, taking advantage of the fact that this approach readily allows for powerful many-body simulations based on matrix product states (MPS). We first analytically derive the two-photon bound state dispersion relation for a variety of atom-light interfaces and then proceed to numerically investigate the multiexcitation bound states dynamics. Interestingly, for all the specific systems studied, we find that the large-photon number limit always coincides with the soliton phenomenon of self-induced transparency or immediate generalizations thereof.
Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems

Users interacting with voice assistants today need to phrase their requests in a very specific manner to elicit an appropriate response.This limits the user experience, and is partly due to the lack of reasoning capabilities of dialogue platforms and the hand-crafted rules that require extensive labor.One possible way to improve user experience and relieve the manual efforts of designers is to build an end-toend dialogue system that can do reasoning itself while perceiving user's utterances.In this work, we propose a novel method to incorporate the knowledge reasoning capability into dialogue systems in a more scalable and generalizable manner.Our proposed method allows a single transformer model to directly walk on a large-scale knowledge graph to generate responses.To the best of our knowledge, this is the first work to have transformer models generate responses by reasoning over differentiable knowledge graphs.We investigate the reasoning abilities of the proposed method on both task-oriented and domain-specific chitchat dialogues.Empirical results show that this method can effectively and efficiently incorporate a knowledge graph into a dialogue system with fully-interpretable reasoning paths.
SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Maps

We present SeamlessGAN, a method capable of automatically generating tileable texture maps from a single input exemplar. In contrast to most existing methods, focused solely on solving the synthesis problem, our work tackles both problems, synthesis and tileability, simultaneously. Our key idea is to realize that tiling a latent space within a generative network trained using adversarial expansion techniques produces outputs with continuity at the seam intersection that can be then be turned into tileable images by cropping the central area. Since not every value of the latent space is valid to produce high-quality outputs, we leverage the discriminator as a perceptual error metric capable of identifying artifact-free textures during a sampling process. Further, in contrast to previous work on deep texture synthesis, our model is designed and optimized to work with multi-layered texture representations, enabling textures composed of multiple maps such as albedo, normals, etc. We extensively test our design choices for the network architecture, loss function and sampling parameters. We show qualitatively and quantitatively that our approach outperforms previous methods and works for textures of different types.
Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks

Before entering the neural network, a token is generally converted to the corresponding onehot representation, which is a discrete distribution of the vocabulary.Smoothed representation is the probability of candidate tokens obtained from a pre-trained masked language model, which can be seen as a more informative substitution to the one-hot representation.We propose an efficient data augmentation method, termed text smoothing, by converting a sentence from its one-hot representation to a controllable smoothed representation.We evaluate text smoothing on different benchmarks in a low-resource regime.Experimental results show that text smoothing outperforms various mainstream data augmentation methods by a substantial margin.Moreover, text smoothing can be combined with those data augmentation methods to achieve better performance.Our code are available at https://github.com/caskcsg/TextSmoothing.
Quantum violation of local causality in an urban network using hybrid photonic technologies

Quantum networks play a crucial role in distributed quantum information processing, enabling the establishment of entanglement and quantum communication among distant nodes. Fundamentally, networks with independent sources allow for new forms of nonlocality, beyond the paradigmatic Bell’s theorem. Here we implement the simplest of such networks—the bilocality scenario—in an urban network connecting different buildings with a fully scalable and hybrid approach. Two independent sources using different technologies—a quantum dot and a nonlinear crystal—are used to share a photonic entangled state among three nodes connected through a 270 m free-space channel and fiber links. By violating a suitable nonlinear Bell inequality, we demonstrate the nonlocal behavior of the correlations among the nodes of the network. Our results pave the way towards the realization of more complex networks and the implementation of quantum communication protocols in an urban environment, leveraging the capabilities of hybrid photonic technologies.
Multimodal neural networks better explain multivoxel patterns in the hippocampus

The human hippocampus possesses "concept cells", neurons that fire when presented with stimuli belonging to a specific concept, regardless of the modality. Recently, similar concept cells were discovered in a multimodal network called CLIP (Radford et al., 2021). Here, we ask whether CLIP can explain the fMRI activity of the human hippocampus better than a purely visual (or linguistic) model. We extend our analysis to a range of publicly available uni- and multi-modal models. We demonstrate that "multimodality" stands out as a key component when assessing the ability of a network to explain the multivoxel activity in the hippocampus.
aiWave: Volumetric Image Compression With 3-D Trained Affine Wavelet-Like Transform

Volumetric image compression has become an urgent task to effectively transmit and store images produced in biological research and clinical practice. At present, the most commonly used volumetric image compression methods are based on wavelet transform, such as JP3D. However, JP3D employs an ideal, separable, global, and fixed wavelet basis to convert input images from pixel domain to frequency domain, which seriously limits its performance. In this paper, we first design a 3-D trained wavelet-like transform to enable signal-dependent and non-separable transform. Then, an affine wavelet basis is introduced to capture the various local correlations in different regions of volumetric images. Furthermore, we embed the proposed wavelet-like transform to an end-to-end compression framework called aiWave to enable an adaptive compression scheme for various datasets. Last but not least, we introduce the weight sharing strategies of the affine wavelet-like transform according to the volumetric data characteristics in the axial direction to reduce the number of parameters. The experimental results show that: 1) when cooperating our trained 3-D affine wavelet-like transform with a simple factorized entropy coding module, aiWave performs better than JP3D and is comparable in terms of encoding and decoding complexities; 2) when adding a context module to remove signal redundancy further, aiWave can achieve a much better performance than HEVC.
Few-Shot Learning with Collateral Location Coding and Single-Key Global Spatial Attention for Medical Image Classification

Humans are born with the ability to learn quickly by discerning objects from a few samples, to acquire new skills in a short period of time, and to make decisions based on limited prior experience and knowledge. The existing deep learning models for medical image classification often rely on a large number of labeled training samples, whereas the fast learning ability of deep neural networks has failed to develop. In addition, it requires a large amount of time and computing resource to retrain the model when the deep model encounters classes it has never seen before. However, for healthcare applications, enabling a model to generalize new clinical scenarios is of great importance. The existing image classification methods cannot explicitly use the location information of the pixel, making them insensitive to cues related only to the location. Besides, they also rely on local convolution and cannot properly utilize global information, which is essential for image classification. To alleviate these problems, we propose a collateral location coding to help the network explicitly exploit the location information of each pixel to make it easier for the network to recognize cues related to location only, and a single-key global spatial attention is designed to make the pixels at each location perceive the global spatial information in a low-cost way. Experimental results on three medical image benchmark datasets demonstrate that our proposed algorithm outperforms the state-of-the-art approaches in both effectiveness and generalization ability.