Reviewerly/exHarmony huggingface dataset:
Inhibitory and excitatory integration with a quantum dot laser neuron

Neuromorphic computing has garnered a lot of attention in recent years. Excitable photonic systems in particular demonstrate great potential for ultrafast, controllable spike processing. Optically injected quantum dot lasers display several distinct excitable regimes. We demonstrate here that optically injected dual-state quantum dot lasers can display the classic leaky integrate-and-fire mechanism where the integration of several sub-threshold perturbations can yield an effective supra-threshold perturbation. Intriguingly, a contrasting integrate-and-inhibit mechanism is demonstrated in this work where the integration of two supra-threshold perturbations yields an effective sub-threshold perturbation similar to the pre-pulse inhibition mechanism of biological neurons. This is the first such mechanism in neuromorphic photonics to the best of our knowledge.
Backtracking through interventions: An exogenous intervention model for counterfactual semantics

Causal models show promise as a foundation for the semantics of counterfactual sentences. However, current approaches face limitations compared to the alternative similarity theory: they only apply to a limited subset of counterfactuals and the connection to counterfactual logic is not straightforward. This article addresses these difficulties using exogenous interventions, where causal interventions change the values of exogenous variables rather than structural equations. This model accommodates judgments about backtracking counterfactuals, extends to logically complex counterfactuals, and validates familiar principles of counterfactual logic. This combines the interventionist intuitions of the causal approach with the logical advantages of the similarity approach.
Intelligent Image Processing Technology for Badminton Robot under Machine Vision of Internet of Things

The present work aims to promote the development of intelligent image processing technology for badminton robots and optimize the application effect of badminton robots in national fitness. Firstly, the problems and common needs of the badminton robot currently in use are investigated. Secondly, a shuttlecock aerodynamic model is established to simulate the effects of air resistance and gravity on the aerial flight of shuttlecock. Besides, the convolution neural network (CNN) is combined with traditional image processing technology to denoise and recognize the collected shuttlecock images. Finally, the badminton robot vision system is constructed and its performance is tested. The results demonstrate that the image denoising method based on CNN and the traditional image processing method can effectively process and denoise the captured moving image. Under the noise level of [Formula: see text], the peak signal-to-noise ratio index of this method is better than the Gaussian Scale Model, k-Singular Value Decomposition, and Color Names methods, slightly better than that of the Multilayer Perceptron (MLP) network. In terms of the time consumed in processing the same number of pictures, the method reported here takes the least time. But when [Formula: see text], the MLP method has a better denoising effect because the noise is overlarge and the features are not easy to learn. Moreover, the detection accuracy of the optimized Single Shot MultiBox Detector (SSD) method adopted here is 79.0%. This accuracy is 1.7% higher than that of the traditional SSD method, and 2.3% higher than that of Faster Region-Convolutional Neural Network based on Region Proposal Network. The optimized network structure reported here provides a certain idea for the software design of the badminton robot.
Single particle electronic voting scheme based on quantum ring signature

Based on the ring signature approach, this paper proposes a quantum voting protocol using single-particle states where voters can choose their voters from among the candidates without revealing their identities. In this protocol, voters hide their voting information into the envelope prepared by the voting center and the vote-counter through the quantum Fourier transform and the unitary operation, the voting center and the vote-counter will each generate only half of the voter’s identity information, and the voter’s identity information is secured by the mutual supervision of the voting center and the vote-counter. Because the protocol uses single particle states, which effectively reduces the difficulty of implementing this protocol, the security analysis shows that the protocol is secure.
Compositions and tensor products of linear maps between matrix algebras

In this semi-expository paper, we first explain key notions from current quantum information theory and criteria for them in a coherent way. These include separability/entanglement, Schmidt numbers of bi-partite states and block-positivity, together with various kinds of positive maps between matrix algebras like entanglement breaking maps, k-superpositive maps, completely positive maps, k-positive maps. We will begin with concrete examples of elementary positive maps given by x↦s⁎xs, and use Choi matrices and duality to explain all the notions mentioned above. We also show that the Choi matrix can be defined free from coordinates. The above notions of positive maps give rise to mapping cones, whose dual cones are characterized in terms of compositions or tensor products of linear maps. Through the discussion, we exhibit an identity which connects tensor products and compositions of linear maps between matrix algebras through the Choi matrices. Using this identity, we show that the description of the dual cone with tensor products is possible only when the involving cones are mapping cones, and recover various known criteria with ampliation for the notions mentioned above. As another applications of the identity, we construct various mapping cones arising from ampliation and factorization, and provide several equivalent statements to PPT (positive partial transpose) square conjecture in terms of tensor products.
Lithological and structural controls of disseminated-type orogenic gold mineralization in high-grade metamorphic turbidites from the Central Zone of the Damara Belt, Namibia

We report the characteristics and controls of low-grade gold-sulfide disseminations in the Twin Hills prospects, the Central Zone of the Damara Belt in Namibia, that combines to a large (>12 km strike length) zone of mineralization. The host rocks are subvertical, tightly folded and transposed, amphibolite facies metaturbidites (cordierite-biotite metapelites and quartz-biotite metapsammites) that form the subvertical limb of a regional-scale syncline. Sulfide and gold grains are finely dispersed in the host rocks or form networks of quartz-sulfide veinlets. Zones of economic-grade gold mineralization are associated with fine-scale quartz-sulfide (pyrrhotite > arsenopyrite ≫ pyrite) vein stockworks. Morphology and deformational textures of the mineralized vein networks suggest their formation during flexural-slip folding and the progressive transposition of the metaturbidites into the regional, ENE trending steep foliation. The more competent metapsammites promoted fracturing while ductile strain during flexural-slip was localized into the schistose metapelites. As a result, economic-grade mineralization is best developed in well-bedded, compositionally heterogeneous parts of the metaturbidites that experienced pronounced strain partitioning. Metapelite-dominated packages, in contrast, show mainly disseminated sulfide mineralization, fewer and variably transposed vein networks and only sub-economic gold grades. The association of disseminated and vein-type mineralization suggests that pervasive fluid flow was channelized within the lithologically heterogeneous packages that provided fracture permeabilities and hydraulic gradients for more focused fluid flow. Higher gold grades correspond to subtle (5-15°) deflections of bedding and the regional foliation that may relate to dilational jog geometries. On a prospect-scale, occurrence of economic-grade gold mineralization coincides with the inflection and change of the vergence of regional-scale first-order folds. The style of mineralization shares similarities with turbidite-hosted orogenic gold deposits. The differences in gold grades and styles of mineralization can be explained by syn-tectonic (D2) fluid flow, the different metamorphic grade (amphibolite versus mid- to lower-greenschist) and ductility of wall rocks that prevented the formation of larger, brittle, high-permeability structures.
IA-FaceS: A bidirectional method for semantic face editing

Semantic face editing has achieved substantial progress in recent years. However, existing face editing methods, which often encode the entire image into a single code, still have difficulty in enabling flexible editing while keeping high-fidelity reconstruction. The one-code scheme also brings entangled face manipulations and limited flexibility in editing face components. In this paper, we present IA-FaceS, a bidirectional method for disentangled face attribute manipulation as well as flexible, controllable component editing. We propose to embed images onto two branches: one branch computes high-dimensional component-invariant content embedding for capturing face details, and the other provides low-dimensional component-specific embeddings for component manipulations. The two-branch scheme naturally enables high-quality facial component-level editing while keeping faithful reconstruction with details. Moreover, we devise a component adaptive modulation (CAM) module, which integrates component-specific guidance into the decoder and successfully disentangles highly-correlated face components. The single-eye editing is developed for the first time without editing face masks or sketches. According to the experimental results, IA-FaceS establishes a good balance between maintaining image details and performing flexible face manipulation. Both quantitative and qualitative results indicate that the proposed method outperforms the existing methods in reconstruction, face attribute manipulation, and component transfer. We release the code and weights at: https://github.com/CMACH508/IA-FaceS.
A constrained multiobjective evolutionary algorithm based on adaptive constraint regulation

Many constrained multiobjective evolutionary algorithms have been proposed in recent years. However, different regions of a multiobjective optimization problem may have different difficulties in meeting constraints, which leads to an unbalanced search on different regions. Currently, there is a lack of research in this area. Considering that different regions may require different strategies to balance the feasibility and convergence of solutions, this paper proposes an adaptive constraint regulation method to adjust the constraint violation of an infeasible solution by its K-nearest neighbors. This work achieves different evaluations of the constraint violation for these infeasible solutions in different regions. The population is updated by using the constraint dominance principle according to the regulated constraint violation. Some infeasible solutions that have the smallest constraint violation among their K-nearest neighbors are treated as feasible solutions. It is helpful for us to leverage the information of these valuable infeasible solutions. Furthermore, an adaptive mechanism is presented to adjust the size K of the neighbors according to the number of generations and the number of feasible solutions in the current population. Comparative studies on forty-one benchmark test instances clearly show that the proposed algorithm can effectively balance the feasibility, convergence, and diversity of the solutions.
High-Fidelity Face Reenactment Via Identity-Matched Correspondence Learning

Face reenactment aims to generate an animation of a source face using the poses and expressions from a target face. Although recent methods have made remarkable progress by exploiting generative adversarial networks, they are limited in generating high-fidelity and identity-preserving results due to the inappropriate driving information and insufficiently effective animating strategies. In this work, we propose a novel face reenactment framework that achieves both high-fidelity generation and identity preservation. Instead of sparse face representations (e.g., facial landmarks and keypoints), we utilize the Projected Normalized Coordinate Code (PNCC) to better preserve facial details. We propose to reconstruct the PNCC with the source identity parameters and the target pose and expression parameters estimated by 3D face reconstruction to factor out the target identity. By adopting the reconstructed representation as the driving information, we address the problem of identity mismatch. To effectively utilize the driving information, we establish the correspondence between the reconstructed representation and the source representation based on the features extracted by an encoder network. This identity-matched correspondence is then utilized to animate the source face using a novel feature transformation strategy. The generator network is further enhanced by the proposed geometry-aware skip connection. Once trained, our model can be applied to previously unseen faces without further training or fine-tuning. Through extensive experiments, we demonstrate the effectiveness of our method in face reenactment and show that our model outperforms state-of-the-art approaches both qualitatively and quantitatively. Additionally, the proposed PNCC reconstruction module can be easily inserted into other methods and improve their performance in cross-identity face reenactment.
Federated learning based nonlinear two-stage framework for full-reference image quality assessment: An application for biometric

The non-linearity in medical image processing is a critical issue. Because the privacy of the medical image and loss of data is a major concern in recent years. Federated learning is a most advanced form of machine learning in which, rather than transmitting data to local server, a machine learning (ML) algorithm is installed to various devices to train on the information. The parameters from the separate modules will then be transferred to a master ML/ (deep learning) DL model for global training. The research of Image Quality Assessment (IQA) aims to simulate the process of human perception of image quality and construct an objective image quality model as consistent as possible with subjective assessment. The existing IQA methods can be roughly divided into traditional methods and deep learning methods. Traditional methods are knowledge-driven, using prior knowledge or assumptions about the human visual system (HVS) to heuristically design image quality index. Deep learning methods are data-driven, using a large amount of annotated data to learn the mapping from the image to its visual quality end-to-end. To effectively integrate traditional methods into deep networks and investigate the knowledge (model)-driven deep learning methods are the current mainstream trends in IQA research. In this paper, we take the contrary direction and improve traditional methods guided by the cue from deep learning methods. The main works include: 1. the employment of activation function ensure the nonlinear approximation ability of the neural network, here we first extend the two-stage framework widely used in the field of full-reference image quality assessment and propose a nonlinear two-stage framework. 2. Within this framework, we revisit the Edge Strength SIMlarity (ESSIM) algorithm that we previously published in IEEE Signal Processing Letters, and proposed the Nonlinear Edge Strength SIMlarity (NESSIM) algorithm. Experiments on public databases show that NESSIM can obtain good assessment results in traditional methods.
Eyes can draw: A high-fidelity free-eye drawing method with unimodal gaze control

EyeCompass is a novel free-eye drawing system enabling high-fidelity and efficient free-eye drawing through unimodal gaze control, addressing the bottlenecks of gaze-control drawing. EyeCompass helps people to draw using only their eyes, which is of value to people with motor disabilities. Currently, there is no effective gaze-control drawing application due to multiple challenges including involuntary eye movements, conflicts between visuomotor transformation and ocular observation, gaze trajectory control, and inherent eye-tracking errors. EyeCompass addresses this using two initial gaze-control drawing mechanisms: brush damping dynamics and the gaze-oriented method. The user experiments compare the existing gaze-control drawing method and EyeCompass, showing significant improvements in the drawing performance of the mechanisms concerned. The field study conducted with motor-disabled people produced various creative graphics and indicates good usability of the system. Our studies indicate that EyeCompass is a high-fidelity, accurate, feasible free-eye drawing method for creating artistic works via unimodal gaze control.
MAGE: Multi-scale Context-aware Interaction based on Multi-granularity Embedding for Chinese Medical Question Answer Matching

The Chinese medical question answer matching (cMedQAM) task is the essential branch of the medical question answering system. Its goal is to accurately choose the correct response from a pool of candidate answers. The relatively effective methods are deep neural network-based and attention-based to obtain rich question-and-answer representations. However, those methods overlook the crucial characteristics of Chinese characters: glyphs and pinyin. Furthermore, they lose the local semantic information of the phrase by generating attention information using only relevant medical keywords. To address this challenge, we propose the multi-scale context-aware interaction approach based on multi-granularity embedding (MAGE) in this paper.We adapted ChineseBERT, which integrates Chinese characters glyphs and pinyin information into the language model and fine-tunes the medical corpus. It solves the common phenomenon of homonyms in Chinese. Moreover, we proposed a context-aware interactive module to correctly align question and answer sequences and infer semantic relationships. Finally, we utilized the multi-view fusion method to combine local semantic features and attention representation.We conducted validation experiments on the three publicly available datasets, namely cMedQA V1.0, cMedQA V2.0, and cEpilepsyQA. The proposed multi-scale context-aware interaction approach based on the multi-granularity embedding method is validated by top-1 accuracy. On cMedQA V1.0, cMedQA V2.0, and cEpilepsyQA, the top-1 accuracy on the test dataset was improved by 74.1%, 82.7%, and 60.9%, respectively. Experimental results on the three datasets demonstrate that our MAGE achieves superior performance over state-of-the-art methods for the Chinese medical question answer matching tasks.The experiment results indicate that the proposed model can improve the accuracy of the Chinese medical question answer matching task. Therefore, it may be considered a potential intelligent assistant tool for the future Chinese medical answer question system.
Web browsing privacy in the deep learning era: Beyond VPNs and encryption

Web browsing privacy is a matter of paramount importance for the Internet users. While they try to protect themselves from being monitored by getting advantage of encryption or VPNs, users' privacy is still unaccomplished, even taking into account the tangled web, with several domains visited at the same time in a single web page, or IP addresses of a cloud provider shared by several sites. In this work, we provide a novel approach to identify user web browsing that only takes into account the IP addresses that the user has connected to and without performing any DNS reverse resolutions. We use this sequence of addresses as an input of different state-of-the-art deep learning models, such as multi-layer perceptron and transformers, which are able to accurately identify which was the website actually visited among Alexa's World Top 500 most visited domains. Moreover, we have also studied other factors, such as the dependence on the DNS server used to resolve the visited IP addresses, the accuracy for the top domains (e.g., Google, YouTube, Facebook, etc.), data augmentation by packet sampling simulation to improve our results, the impact on packet sampling and the fine-tuning and possible impact of model parameters or the scalability of our approach. We conclude that, using only a 10% of the packets, we can identify the visited website with an accuracy and F1 score between 94% and 95%.
Disguised heterogeneous face recognition using deep neighborhood difference relational network

Heterogeneous face recognition (HFR) aims to match a probe face image to a gallery of images in a different modality, such as visible to thermal. The complexity of the problem is further increased by the presence of facial disguise, one of the emerging problems in application of face biometrics. While several works have been proposed for HFR, disguised HFR remains one of the unexplored challenges of face recognition. Most of the proposed methods apply constraints on the network output for cross-modality image matching, consequently ignoring the image relationships within the network. The deep features within a network contain additional information which can be exploited for improved performance. With this motivation, we propose a Deep Neighborhood Difference Relational (DNDR) network and Joint Discrimination Loss for disguised HFR. The network calculates the difference relationships between cross-modality images in deep feature space. It implicitly learns to ignore the real-to-disguise feature relationships while focusing on real-to-real facial features for HFR. The proposed method learns the relationships between images of the same identity rather than optimizing the network on a hand-crafted metric loss function. For an input image pair, the DNDR network outputs identity representative embeddings of each image and a match probability of the image pair. The embedding distance and the match probability are then fused for more robust classification accuracy. Extensive experiments on publicly available heterogeneous face databases show the effectiveness of the proposed method for visible to infrared, visible to thermal, and visible to sketch face recognition.
Flexible learning tree augmented naïve classifier and its application

Tree augmented naïve Bayes classifier (TAN) has been widely used in machine learning and data mining. To improve the flexibility and classification performance of TAN, this paper proposes a Flexible Tree Augmented Naïve Bayes classifier (FTAN). In the FTAN, the mutual information contribution rate is used to measure the dependencies between attributes in the process of building the maximum weighted spanning tree. Then, a flexible filtering method is adopted to filter out edges with weak dependencies between attributes by dynamically adjusting the threshold. A range of experiments on UCI datasets reveals that the FTAN exhibits considerable advantages over other popular algorithms in terms of the 0-1 loss and class probability root mean square error. The FTAN is used to solve the problem of favourable distribution area prediction for the remaining oil and gas resources of the Jurassic Sangonghe Formation in the Junggar Basin. The application results show the effectiveness and superiority of the FTAN method, favourable areas of oil and gas resources are selected based on the FTAN prediction results. This provides a decision-making basis for optimising drilling strategies and oil and gas exploration targets.