bluuebunny/crossref_metadata_2025 huggingface dataset:
Duplex Hecke algebras of type B
Authors: Xie Yu , Zhang An , Shu Bin
Published: December 2023

As a sequel to [C. Xue and A. Zhang, Doubled Hecke algebras and related quantum Schur duality, preprint (2021), arXiv:2108.07587[math.RT], accepted for publication in Algebra Colloq.], in this article we first introduce a so-called duplex Hecke algebras of type [Formula: see text] which is a [Formula: see text]-algebra associated with the Weyl group [Formula: see text] of type [Formula: see text], and symmetric groups [Formula: see text] for [Formula: see text], satisfying some Hecke relations (see Definition 3.1). This notion originates from the degenerate duplex Hecke algebra arising from the course of study of a kind of Schur–Weyl duality of Levi-type (see [B. Shu and Y. Yao, On enhanced reductive groups (I): Enhanced Schur algebras and the dualities related to degenerate duplex Hecke algebras, with an appendix by B. Liu, submitted (2023)]), extending the duplex Hecke algebra of type [Formula: see text] arising from the related [Formula: see text]-Schur–Weyl duality of Levi-type (see [C. Xue and A. Zhang, Doubled Hecke algebras and related quantum Schur duality, preprint (2021), arXiv:2108.07587[math.RT], accepted for publication in Algebra Colloq.]). A duplex Hecke algebra of type [Formula: see text] admits natural representations on certain tensor spaces. We then establish a Levi-type [Formula: see text]-Schur–Weyl duality of type [Formula: see text], which reveals the double centralizer property between such duplex Hecke algebras and [Formula: see text]quantum groups studied by Bao and Wang in [H. Bao and W. Wang, A new approach to Kazhdan–Lusztig theory of type B via quantum symmetric pairs, Astérisque 402 (2018)].
Tropical matrix groups and Boolean matrix groups
Authors: Yang Lin , Ren Miao-Miao , Zeng Ling-Li
Published: December 2023

In this paper, we give a complete description of the maximal subgroups of the semigroup of all [Formula: see text] tropical matrices under multiplication up to isomorphism. Using the description, we prove that every subgroup of the semigroup of all [Formula: see text] Boolean matrices under multiplication is embedded in the symmetric group [Formula: see text].
Variations of primeness of ideals in rings of continuous functions
Authors: Aliabad A. R. , Ghoulipour M. , Paimann M.
Published: December 2023

In this paper, we describe some different variations of prime ideals in the context of rings of continuous functions such as strongly prime ideals, almost prime ideals, [Formula: see text]-absorbing ideals, and 2-prime ideals. We characterize the strongly prime ideals of [Formula: see text]. We prove that an ideal [Formula: see text] of [Formula: see text] is almost prime if and only if it is semiprime or equivalently if and only if it is an [Formula: see text]-closed ideal, where [Formula: see text] and [Formula: see text] are positive integers. In [On [Formula: see text]-absorbing ideals of commutative rings, Comm. Algebra 39 (2011) 1646–1672], Anderson and Badawi conjectured that every [Formula: see text]-absorbing ideal of a commutative ring is strongly [Formula: see text]-absorbing. We determine the [Formula: see text]-absorbing ideals of [Formula: see text] and show that the conjecture holds in the case of rings of continuous functions. We also characterize the rings of continuous functions [Formula: see text] in which every pseudoprime ideal is 2-prime.
ROUTING STRATEGIES FOR SUPPRESSING TRAFFIC-DRIVEN EPIDEMIC SPREADING IN MULTIPLEX NETWORKS
Authors: MA JINLONG , XIANG TINGTING , CAI MINGWEI
Published: December 2023

Multiplex networks have proven to be valuable tools for modeling and analyzing real complex system. Extensive work has been done on the traffic dynamics on multiplex networks, but there remains a lack of sufficient attention towards studying routing strategies for the purpose of suppressing epidemic spreading. In this paper, the impact of global awareness routing (GAR), improved global awareness routing (IGAR), and improved active routing (IAR) strategies on traffic-driven epidemic spreading are investigated. Our findings indicate that in the case of infinite node-delivery capacity and no traffic congestion in the network, adjusting routing parameters can effectively suppress epidemic spreading. In this context, these three strategies show better abilities on the multiplex network built by WS or ER model to minimize the density of infected nodes, thus contributing to the overall inhibition of the epidemic spread. However, in the multiplex network constructed by BA model, GAR strategy has a promoting effect on epidemic spreading compared with the shortest routing strategy. In addition, by controlling traffic flow, limiting node delivery capabilities can contain outbreaks. Our results suggest that adopting appropriate routing strategies in multiplex networks can play a proactive role in controlling epidemic spreading. This is crucial for formulating effective prevention and control measures and improving public health security.
On the strong solution for a diffuse interface model of non-Newtonian two-phase flows
Authors: Zhao Xiaopeng , Zhou Yong
Published: December 2023

The main purpose of this paper is to study the well-posedness of a diffuse interface model of non-Newtonian two-phase flows in [Formula: see text]. First, we prove the local existence of a unique strong solution provided that the initial velocity and initial concentration of two phases are sufficiently regular. Then, by using the energy estimates and standard continuity argument, we show that there exists a unique global strong solution provided that the initial velocity and initial concentration are sufficiently small.
Some Chloroquine Derivatives for Promising New Antifungal Drugs and Absorption Behavior
Authors: Al-Refai’a Rana A. K. , Abdali Karar , Al-Bermany Ehssan
Published: December 2023

Chloroquine (CQ) has been a good treatment for antimalarial mainstay for several decades; additionally discovered, it has a significant therapeutic impact on some instances of fungal inhibition. This study focused on the effect of novel CQ compounds on the activity of two different species of fungi, Aspergillus niger and Aspergillus falves. The activity of each CQ derivative was monitored using Nuclear magnetic resonance spectroscopy (NMR), minimal inhibitory concentration and physical parameters such as optical microscopy, UV/visible absorbance and optical band gap. NMR indicated the conjugation between the substrate and amino acid. The optical microscopic images indicated homogeneously distributed and uniform density distribution of CQ-derivative particles on the glass substrates. The samples’ presented absorption peaks at 203, 207 and 220 nm wavelengths, suggesting the important electronic transition with reducing the indirect bandgap from 4.1 eV to 3.95 eV. These compounds have the best antifungal growth inhibitory properties and excellent features, indicating cosmetics use.
Hydrothermal Synthesis of Mn2 P2 O7 Nanostructures and Their Electrochemical Behavior in Organic Electrolyte
Authors: Ramesh K. , Gunavarthini S. , Soundhirarajan P. , Silambarasan M. , Mehala B.
Published: December 2023

In this work, Mn2P2O7 nanoparticles were synthesized via hydrothermal route without any templates or surfactants followed by heat treatment at 700[Formula: see text]C. The as-prepared samples were characterized and described using thermogravimetric and differential thermal analysis (TG-DTA), X-ray diffraction (XRD), Fourier transform infrared spectrometer (FT-IR), Scanning electron microscopy (SEM) analysis. The resultant product was evaluated for electrochemical properties in organic electrolyte between −1.4 and 1.6 V using cyclic voltammetry in ambient condition. It revealed the specific capacitance of 565 F/g at scan rate of 5 mV/s. The outstanding pseudocapacitive performance was absorbed due to the faradaic oxidation and reduction reactions related to the intercalation/de-intercalation of the tetrabutylammonium cation (TBA[Formula: see text] electrolyte and inorganic pyrophosphate lattice. It was believed that the cost effective Mn2P2O7 nanoparticles may be promising electrode materials for electrochemical capacitors.
Federated learning on non-IID and globally long-tailed data via meta re-weighting networks
Authors: Lu Yang , Qian Pinxin , Yan Shanshan , Huang Gang , Tang Yuan Yan
Published: December 2023

Under a federated learning environment, the training samples are generally collected and stored locally on each client’s device, which makes the machine learning procedure not meet the requirement of independent and identical distribution (IID). Existing federated learning methods to deal with non-IID data generally assume that the data is globally balanced. However, real-world multi-class data tend to exhibit long-tail distribution, where the majority of samples are in a few head classes and a large number of tail classes only have a small amount of data. This paper, therefore, focuses on addressing the problem of handling non-IID and globally long-tailed data in a federated learning scenario. Accordingly, we propose a new federated learning method called Federated meta re-weighting networks (FedReN), which assigns weights during the local training process from the class-level and instance-level perspectives, respectively. To deal with data non-IIDness and global long-tail, both of the two re-weighting functions are globally trained by the meta-learning approach to acquire the knowledge of global long-tail distribution. Experiments on several long-tailed image classification benchmarks show that FedReN outperforms the state-of-the-art federated learning methods. The code is available at https://github.com/pxqian/FedReN .
Learning long- and short-term dependencies for improving drug-target binding affinity prediction using transformer and edge contraction pooling
Authors: Gao Min , Jiang Shaohua , Ding Weibin , Xu Ting , Lyu Zhijian
Published: December 2023

The accurate identification of drug-target affinity (DTA) is crucial for advancements in drug discovery and development. Many deep learning-based approaches have been devised to predict drug-target binding affinity accurately, exhibiting notable improvements in performance. However, the existing prediction methods often fall short of capturing the global features of proteins. In this study, we proposed a novel model called ETransDTA, specifically designed for predicting drug-target binding affinity. ETransDTA combines convolutional layers and transformer, allowing for the simultaneous extraction of both global and local features of target proteins. Additionally, we have integrated a new graph pooling mechanism into the topology adaptive graph convolutional network (TAGCN) to enhance its capacity for learning feature representations of chemical compounds. The proposed ETransDTA model has been evaluated using the Davis and Kinase Inhibitor BioActivity (KIBA) datasets, consistently outperforming other baseline methods. The evaluation results on the KIBA dataset reveal that our model achieves the lowest mean square error (MSE) of 0.125, representing a 0.6% reduction compared to the lowest-performing baseline method. Furthermore, the incorporation of queries, keys and values produced by the stacked convolutional neural network (CNN) enables our model to better integrate the local and global context of protein representation, leading to further improvements in the accuracy of DTA prediction.
Verifiable multi-party quantum secret sharing based on GHZ states
Authors: Su Weijie , Situ Haozhen , Huang Qiong , Zhang Cai
Published: December 2023

In this paper, we propose a verifiable multi-party secret sharing protocol based on an entanglement swapping theorem of GHZ states. In our protocol, all receivers only need to perform simple GHZ measurements to recover the secret. The quantum-safe system proposed in this paper is feasible with current quantum technologies, and we show that it is secure against both external and internal attacks. In addition, the secret sender can verify whether the secret is correctly recovered.
Conscious Learning without Post-Selection Misconduct
Authors: Weng Juyang
Published: December 2023

The most fundamental oversight in Artificial Intelligence (AI) is probably the avoidance of conscious learning. The most widespread misconduct in AI seems to be Post-Selection. Avoiding conscious learning, we program humanoid robots that do not have the intent to learn new skills, such as standing up, walking, jumping, speaking, and thinking. Because programmed-in skills are all brittle, we must imitate natural intelligence, from insects to humans, that all learn from consciousness. Worldwide AI researchers and the public have been misled by media hypes about false AI performances, from Deep Learning to ChatGPT, rooted in Post-Selection. However, the Post-Selection protocol behind such hypes is fatally flawed by alleged misconduct — Misconduct 1, Cheating in the absence of a test; Misconduct 2, hiding bad-looking data. In other words, the reported errors are only data-fitting errors, instead of testing errors. This paper discusses how Conscious Learning is enabled by Developmental Network 3 (DN-3) to learn from its own intents without Post-Selection misconduct. Among many new concepts presented here, this paper establishes a new theorem that the expected error of the luckiest system in a future test is the same as any other less lucky system, namely, average. In contrast, DN-3 develops a sole network that is optimal in the sense of maximum likelihood (ML), better than the luckiest system on a validation set. The ML optimality transfers the performance on a validation set in the prior lifetime to a test set in the future lifetime. Many other AI techniques, e.g., symbolic, connectionist, and evolutional, also use Post-Selection which lacks such a transfer.
C-SQG: Cosine-Law-Based Spatially Quantized Gait Generation for Knee-stretched Biped Walking
Authors: Zou Chaobin , Huang Rui , Song Guangkui , Shi Kecheng , Peng Zhinan , Cheng Hong
Published: December 2023

Knee-stretched walking is desirable for biped robot because it is a characteristic of human walking as well as lower energy consumption. In this paper, we proposed a new knee-stretched bipedal walking pattern generation approach named Cosine-law-based Spatially Quantized Gait (C-SQG). C-SQG includes two parts, the first one is the Cosine law-based kinematic joint angles generation for knee-stretched biped walking with the hip horizontal displacement; the second one is combining with the concept of Spatially Quantized Dynamics (SQD) and the optimization-based methods. Based on these two approaches, the balanced human-like walking patterns with fully extended knee joints for humanoid robots can be generated. The proposed approach was tested on the CoppeliaSim simulation platform, and the experimental results indicate that the knee-stretched walking patterns for different walking speeds and foot locations can be generated. Compared to the existing knee-stretched gait generation approach, the C-SQG is more robust and adaptive to different desired foot locations appropriately.
Wormholes and energy conditions in f(R,T) gravity
Authors: Tangphati Takol , Banerjee Ayan , Pradhan Anirudh
Published: December 2023

In this paper, we explore the existence of wormholes in the context of [Formula: see text] gravity. The [Formula: see text] theory is a curvature-matter coupled modified gravity that depends on an arbitrary function of the Ricci scalar [Formula: see text] and the trace of the stress-energy tensor [Formula: see text]. In this work, we adopt two different choices for the matter Lagrangian density ([Formula: see text] and [Formula: see text]) and investigate the impact of each one on wormhole structure. By adequately specifying the redshift function and the shape function, we found a variety of exact wormhole solutions in the theory. Our finding indicates that for both classes of wormholes, the energy density is always positive throughout the spacetime, while the radial pressure is negative. This means exotic matter is necessary for the existence of wormholes in [Formula: see text] gravity.
A Harnack inequality for a class of 1D nonlinear reaction–diffusion equations and applications to wave solutions
Authors: Abolarinwa Abimbola , Osilagun Johnson A , Azami Shahroud
Published: December 2023

In this paper, a differential-geometric method is applied to build some Li–Yau–Hamilton-type Harnack inequalities for the positive solutions to a one spatial dimensional nonlinear reaction–diffusion equation in a plane geometry. The class of reaction–diffusion equation that is considered here contains several important equations some of which are Newel–Whitehead–Segel, Allen–Cahn and Fisher–KPP equations. The Harnack inequalities so derived are used to discuss some other important properties of positive solutions and in the characterization of positive wave solutions.
Novel approach of Hubble parameter in f(R,Tϕ) gravity
Authors: Shukla Bhupendra Kumar , Sofuoğlu Değer , Mishra Preeti
Published: December 2023

The [Formula: see text] theory of gravity investigated by Harko et al. [T. Harko, F. S. N. Lobo, S. Nojiri and S. D. Odintsov, Phys. Rev. D 84 (2011) 024020; T. Harko, Phys. Rev. D 90(4) (2014) 044067] serves as the inspiration for the homogeneous and isotropic cosmological model presented in generalized [Formula: see text] theories connected with a scalar field. Assuming that [Formula: see text] is the trace of the energy-momentum tensor, [Formula: see text] gravity can be understood as [Formula: see text] gravity with a self-interacting scalar field [Formula: see text]. To address this, we provide a novel model-independent method for the parametrization of the Hubble parameter, and we apply it to the Friedmann equations in the FLRW Universe. The Markov Chain Monte Carlo (MCMC) approach is then used to estimate the best-fit values of the model parameters using a combination of the updated [Formula: see text] dataset, which comprises 57 points, and the Pantheon dataset, which contains 1048 points. The evolution of the deceleration parameter shows a transition from the universe’s deceleration phase to its acceleration phase. We also investigate the behavior of the statefinder and the Om diagnostic parameter. Finally, a thorough discussion of the model’s physical attributes has been carried out.
Generalized Duffin–Kemmer–Petiau oscillator under Aharonov–Bohm flux in topological defects backgrounds
Authors: Ahmed Faizuddin , Candemir Nuray
Published: December 2023

In this paper, we study the generalized Duffin–Kemmer–Petiau (DKP) oscillator under the influence of quantum flux field in the topological defects produced by a cosmic string space-time and point-like global monopole. The generalized DKP oscillator will be investigated through a non-minimal substitution of the momentum operator [Formula: see text] in the relativistic DKP equation. We solve this generalized DKP oscillator in a cosmic string space-time background and obtain the energy levels and wave function of the oscillator field using the parametric Nikiforov–Uvarov (NU) method. Afterward, we solve the generalized DKP-oscillator in a point-like global monopole space-time and obtain the energy levels and wave functions following the same method. In fact, it is shown there that the energy eigenvalues are influenced by the topological defect of cosmic string and point-like global monopole and get modified compared to flat space results, and breaks the degeneracy of the energy levels. Furthermore, we observe that the eigenvalue solutions depend on the quantum flux field that shows the gravitational analog of the Aharonov–Bohm effect and also gives us a persistent current.
Energy–Momentum distributions of cylindrical black hole in rainbow gravity
Authors: Korunur Murat , Korunur Sibel
Published: December 2023

In this study, the energy–momentum localization problem is addressed within the framework of rainbow gravity. Considering the black string black hole, one of the cylindrical black hole models, energy–momentum distributions are obtained for Einstein, Bergmann–Thomson, and Landau–Liftshitz prescriptions in rainbow gravity. All prescriptions’ energy densities are non-zero and depend on the rainbow functions, while the momentum distributions are zero. It has been observed that space-time energy does not depend on the energy of the test particle for some particular choices of rainbow functions. The results obtained for all special cases are given in a table.
Geometric properties of lightlike magnetic curves and magnetic flux surfaces in Minkowski 3-space
Authors: Jiang Xiaoyan , Pei Donghe
Published: December 2023

We define the lightlike [Formula: see text]-magnetic curves, lightlike [Formula: see text]-magnetic curves and lightlike [Formula: see text]-magnetic curves in Minkowski 3-space. Then we discuss the characteristics of the corresponding magnetic fields of these lightlike magnetic curves. Furthermore, we also investigate the geometric properties of the magnetic flux surfaces formed by the magnetic fields along lightlike magnetic curves.
Lagrangian formulation of the Raychaudhuri equation in non-Riemannian geometry
Authors: Agashe Anish
Published: December 2023

The Raychaudhuri equation (RE) for a congruence of curves in a general non-Riemannian geometry is derived. A formal connection is established between the expansion scalar and the cross-sectional volume of the congruence. It is found that the expansion scalar is equal to the fractional rate of change of volume, weighted by a scalar factor that depends on the non-Riemannian features of the geometry. Treating the congruence of curves as a dynamical system, an appropriate Lagrangian is derived such that the corresponding Euler–Lagrange equation is the RE. A Hamiltonian formulation and Poisson brackets are also presented.
A Data-Driven Model for Range Sensors
Authors: Spiess Florian , Strobel Norbert , Kaupp Tobias , Kounev Samuel
Published: December 2023

In this paper, an analysis of the precision of range sensors is presented. Light Detection and Ranging (LIDAR) data from three different sensors (HLS-LFCD-LDS, SICK TIM561, and Kinect V2), stereo data from the Realsense D435, and structured light data from both Kinect V1 for Xbox and the Xtion PRO Live were analyzed regarding the influence of range, incident angle to the surface, and surface material. In addition, a comparison with standard deviation models based on vendor-provided specifications was performed. We found that the vendor specifications are in general not specific enough to facilitate accurate sensor simulations. Therefore, we developed a data-driven model for range precision. Our model can be used to create realistic sensor simulations and to develop robot navigation algorithms weighing sensor range readings based on the precision.
Semantic Knowledge Graph Generator and Ontology Recommender: Industry Use Cases
Authors: Ferguson Holly , Yu Dan , Kritzler Mareike
Published: December 2023

Incorporating semantics in modern projects can be time-consuming, rely on limited re-sources, and require expert knowledge to implement in a future-proof manner. Across many working groups and industrial fields, semantics continues to help solve data consolidation challenges; however, there are still emerging situations such as where modeling and ontology engineering efforts overlap, ongoing siloed data, or even continuing to create tech-debt for the future. Tools have been developed and researched that offer users assistance with the automation of select expert tasks to help streamline semantic artifact creation, understanding of design decisions, and domain data alignments. The Semantic Knowledge Graph Generator and Recommendation Framework encourage semantic projects that are automated from the start to follow industry standards, enhanced with recommendations, and use components which make semantic artifacts more accessible for non-semantic experts. Based on that tool, this extension article presents several industry use cases and related discussion where semantic recommendations and deployment automation have assisted with project development, teaching about ontology structures, and gaining accessibility of semantic artifacts.
A Flexible MATLAB/Simulink Simulator for Robotic Floating-base Systems in Contact with the Ground: Theoretical Background and Implementation Details
Authors: Guedelha Nuno , Pasandi Venus , L’Erario Giuseppe , Traversaro Silvio , Pucci Daniele
Published: December 2023

This paper presents an open-source MATLAB/Simulink physics simulator for rigid-body articulated systems, including manipulators and floating-base robots. Thanks to MATLAB/Simulink features like MATLAB system classes and Simulink Function blocks, the presented simulator combines a programmatic and block-based approach, resulting in a flexible design in the sense that different parts, including its physics engine, robot-ground interaction model, and state evolution algorithm are simply accessible and editable. Moreover, through the use of Simulink dynamic mask blocks, the proposed simulator supports robot models integrating open-chain and closed-chain kinematics with any desired number of links interacting with the ground. This simulator can also integrate second-order actuator dynamics. Furthermore, the simulator benefits from a one-line installation and an easy-to-use Simulink interface.
A 15-Category Audio Dataset for Drones and an Audio-Based UAV Classification Using Machine Learning
Authors: Wang Mia Yaqin , Chu Zhiwei , Ku Ilmun , Smith E. Cho , Matson Eric T.
Published: December 2023

The popularity of Unmanned Aerial Vehicles (UAVs), aka drones, has increased rapidly in recent years. UAVs are becoming easily accessible to more users. Malicious intentions can erode public safety when least expected. Current methods used for UAV detection systems include computer vision, radar, radio frequency and audio approaches. We choose the audio method for its high accuracy, low computational requirement and low cost. However, the lack of publicly available datasets is one of the main bottlenecks for developing an audio-based UAV detection and classification system. To fill this gap, we select 15 different UAVs, ranging from toy hand drones to Class I drones and record a total of 8120 s length of audio data generated from the flying UAVs. To the best of our knowledge, the proposed dataset is the largest audio dataset for UAVs so far. We further implement a Convolutional Neural Network (CNN) model for 15-class UAV classification and trained the model with the collected data. The average test accuracy of the trained model is 98.7% and the test loss is 0.076.
Basic Evaluation and Scoring of Energy Use in Range Image Curvature Determination
Authors: Hauenstein Jacob D. , Newman Timothy S.
Published: December 2023

Energy efficiency of surface curvature determination in range images, a core component of a variety of robotics/multimedia tasks, is studied on a popular computing architecture (Intel x86-64). Eight methods for that type of curvature determination are part of the study reported here. Their base energy usage and their reduced energy usage when energy consumption-conscious strategies are applied to them are considered within the study. Comparative evaluation of energy consumption is included in the study. The focus here on core computing component power reduction aims at enabling robotics/multimedia task computation to exhibit reduced energy usage. Some general notes on methods for potentially improving energy efficiency are also provided.
Autonomous Drone Landing: Marked Landing Pads and Solidified Lava Flows
Authors: Springer Joshua , Kyas Marcel
Published: December 2023

Landing is the most challenging and risky aspect of basic multirotor drone flight, and only simple landing methods exist for autonomous drones. We explore methods for autonomous drone landing in two scenarios. In the first scenario, we examine methods for landing on known landing pads using fiducial markers and a gimbal-mounted monocular camera. This method has potential in drone applications where a drone must land more accurately than global positioning system (GPS) can provide (e.g. package delivery in an urban canyon). We expand on previous methods by actuating the drone’s camera to track the marker over time, and we address the complexities of pose estimation caused by fiducial marker orientation ambiguity. In the second scenario, and in collaboration with the Rover-Aerial Vehicle Exploration Network (RAVEN) project, we explore methods for landing on solidified lava flows in Iceland, which serves as an analog environment for Mars and provides insight into the effectiveness of drone-rover exploration teams. Our drone uses a depth camera to visualize the terrain, and we are developing methods to analyze the terrain data for viable landing sites in real time with minimal sensors and external infrastructure requirements, so that the solution does not heavily influence the drone’s behavior, mission structure, or operational environments.
Model-Agnostic Zero-Shot Intent Detection via Contrastive Transfer Learning
Authors: Maqbool M. H. , Fereidouni Moghis , Siddique A. B. , Foroosh Hassan
Published: December 2023

An intent detector is a central component of any task-oriented conversational system. The goal of the intent detector is to identify the user’s goal by classifying natural language utterances. In recent years, research has focused on supervised intent detection models. Supervised learning approaches cannot accommodate unseen intents, which may emerge after the system has been deployed– the more practically relevant setting, known as zero-shot intent detection. The existing zero-shot learning approaches split a dataset into seen and unseen intents for training and evaluations without taking the sensitivity of the data collection process into account. That is, humans tend to use repeated vocabulary and compose sentences with similar compositional structures. We argue that the source-to-target relationship learning objective of zero-shot approaches under typical data split procedure renders the zero-shot models prone to misclassifications when target intents are divergent from source intents. To this end, we propose INTEND , a zero-shot INTENt Detection methodology that leverages contrastive transfer learning and employs a zero-shot learning paradigm in its true sense. First, in contrast to partitioning the training and testing sets from the same dataset, we demonstrate that selecting training and testing sets from two different datasets allows for rigorous zero-shot intent detection evaluations. Second, our employed contrastive learning goal is model-agnostic and encourages the system to focus on learning a generic similarity function, rather than on commonly encountered patterns in the training set. We conduct extensive experimental evaluations using a range of transformer models on four public intent detection datasets for up to 150 unseen classes. Our experimental results show that INTEND consistently outperforms state-of-the-art zero-shot techniques by a substantial margin. Furthermore, our approach achieves significantly better performance than few-shot intent detection models.
Unsupervised Estimation of Subjective Content Descriptions in an Information System
Authors: Bender Magnus , Braun Tanya , Möller Ralf , Gehrke Marcel
Published: December 2023

Let us consider the following scenario: A human is working with a corpus of text documents. In this corpus, the human needs to know documents with similar content and highlight relevant locations in the retrieved documents. An information system displaying the contents of the corpus and providing an information retrieval agent will help the human. To perform information retrieval on the corpus, the agent used internally in the information system may need additional data associated with the documents. In order to support this, the so-called Subjective Content Descriptions (SCDs) provide additional location-specific data for text documents. SCDs are subjective in the sense that the agent associates data with sentences to reflect the beliefs of users. In our scenario, the agent needs SCDs referencing sentences of similar content across various documents in the corpus and most text documents are not associated with SCDs. Therefore, this paper presents UESM, the Unsupervised Estimator for SCD Matrices, an approach to associate any corpus with SCDs. In an evaluation, we show that the performance of UESM in estimating topics of similar content in the corpus is on par with Latent Dirichlet Allocation, while UESM provides the SCDs referencing sentences of similar content.
Does Noise Really Matter? Investigation into the Influence of Noisy Labels on BERT-Based Question Answering System
Authors: Alexandrov Dmitriy , Zakharova Anastasiia , Butakov Nikolay
Published: December 2023

Recent works with the BERT-based models demonstrate their generalization ability and high performance on the new domain tasks. However, this kind of model requires a large amount of data. Collecting this data can be error-prone, and it is important to know: how the errors in data affect the quality of the model. In this work, we study the impact of data with different errors – noisy data on the training of the question answering-over-text BERT-model. We use the concept of random, structural and irrelevant question noises. We study the robustness of QAT models during the training process with different settings, datasets and noise types and discuss possible reasons. We also propose a real-world domain dataset to probe our findings in a real-world scenario. The results of an experimental study showed that following developed recommendations allowed performance improvement up to 3.6% in a real-world setting.
Automatic Domain-Adaptive Sentiment Analysis with SentiMap
Authors: Veltmeijer Emmeke , Gerritsen Charlotte
Published: December 2023

Location-based sentiment analysis is a promising field of study with various applications, but faces issues due to location uncertainty and lack of domain specificity. Our proposed solution automatically builds domain-adaptive lexicons for region-specific sentiment analysis. An initial lexicon for location estimation is created using topic modeling on news articles related to the target domain. For sentiment estimation, we start with a preexisting lexicon. Both initial lexicons are expanded recursively with a word embedding trained on social media messages from the target area. The final lexicons are used for location estimation and for automatically assigning sentiment labels to data, which is then used for fine-tuning a BERT transformer network. Our approach is validated in a case study of Amsterdam, demonstrating that both the automatically expanded lexicons and the fine-tuned network outperform their respective baselines. This illustrates how our system can enhance its performance by adapting to the domain, with minimal manual input. Finally, a temporal analysis is performed at different scales, showcasing the model’s ability to automatically detect sentimentally charged events.
Accuracy Enhancement of Industrial Robots Based on Visual Servoing Using Optimal Adaptive RBFNN Integral Terminal Fractional-Order Super-Twisting Algorithm
Authors: Zakeri Ehsan , Xie Wen-Fang
Published: December 2023

This paper proposes a novel adaptive robust control scheme for accuracy enhancement of eye-to-hand photogrammetry-based industrial robots subject to uncertainties. The proposed method uses two control loops: internal and external loops. The former is the dynamic controller designed for controlling the robot’s joints. The external loop is the kinematic controller to minimize the error of the end-effector detected by the photogrammetry sensor. An adaptive integral terminal fractional-order super-twisting algorithm (AITFOSTA) is developed and employed for both control loops. AITFOSTA is an integral sliding-mode controller (ISMC) whose nominal control law is terminal. Its switching part is replaced with a fractional-order super-twisting algorithm (FOSTA), reducing the chattering to a great extent while rejecting the uncertainties. Additionally, an adaptive uncertainty and disturbance estimator based on radial basis function neural networks (RBFNNs) is designed and employed to reduce the uncertainty bounds, contributing to further chattering reduction. The stability analysis of the proposed controller is also presented. Simulation and experimental results show the superiority of the proposed method over other well-known approaches by reaching an unprecedented tracking accuracy, i.e. 0.06 mm and 0.18[Formula: see text] for position and orientation, respectively.
Human-Inspired Video Imitation Learning on Humanoid Model
Authors: Lee Chun Hei , Yueh Nicole Chee Lin , Leung Chun Yin , Woo Kam Tim
Published: December 2023

Generating good and human-like locomotion or other legged motions for bipedal robots has always been challenging. One of the emerging solutions to this challenge is to use imitation learning. The sources for imitation are mostly state-only demonstrations, so using state-of-the-art Generative Adversarial Imitation Learning (GAIL) with Imitation from Observation (IfO) ability will be an ideal framework to use in solving this problem. However, it is often difficult to allow new or complicated movements as the common sources for these frameworks are either expensive to set up or hard to produce satisfactory results without computationally expensive preprocessing, due to accuracy problems. Inspired by how people learn advanced knowledge after acquiring a basic understanding of specific subjects, this paper proposes a Motion capture-aided Video Imitation (MoVI) learning framework based on Adversarial Motion Priors (AMP) by combining motion capture data of primary actions like walking with video clips of target motion like running, aiming to create smooth and natural imitation results of the target motion. This framework is able to produce various human-like locomotion by taking the most common and abundant motion capture data with any video clips of motion without the need for expensive datasets or sophisticated preprocessing.
Efficient VVC Encoding Using Hierarchical Parallelization: A Comprehensive Analysis
Authors: George Valeri , Brandenburg Jens , Hege Gabriel , Hinz Tobias , Wieckowski Adam , Bross Benjamin , Marpe Detlev
Published: December 2023

This paper presents and analyzes different parallelization strategies in VVenC, an open and optimized software encoder implementation of the Versatile Video Coding (VVC) standard. VVC has been developed to address the increasing demand for higher compression of digital video data, and it reduces the bitrate by around 50% for the same perceived quality compared to its predecessor, the High-Efficiency Video Coding (HEVC) standard. However, this increase in compression efficiency comes with an increase in computational complexity, particularly on the encoder side. VVenC integrates algorithmic optimizations for each coding tool in VVC and defines a set of five presets from faster to slower that provide Pareto-optimal tradeoffs between runtime and efficiency. Multithreading is employed to further reduce runtime while preserving most of the compression efficiency of each preset. With a hierarchical combination of pre-processing, picture-level and in-picture parallelization, VVenC achieves a 4× speedup for four threads. Further speedup using a higher number of threads depends on the video resolution and used encoder preset. For 16 threads, it ranges from 6 to 9 for high definition to 10-12 for ultra-high-definition video. Compared to previous work, the usage of temporal prediction for the adaptive loop filter reduces the associated coding efficiency loss from 0.4% to almost zero. A better scaling for higher numbers of threads can be achieved at the cost of a higher coding efficiency loss. In the presented framework, increased speedup by smaller block Coding Tree Unit (CTU) sizes, a combination of wavefront parallel processing and various tiles picture partitioning configurations is examined. Furthermore, results on a 20-core ARM-based Apple M1 computer indicate a better scaling for multithreading compared to × 86-based architectures. The analysis is complemented by profiling, which exhibits the overhead by idle threads and identifies the mode estimation as the main bottleneck of the presented framework.
Common zero-divisor graph over a commutative ring
Authors: Abbasnia Fazlahmad , Khashyarmanesh Kazem , Erfanian Ahmad
Published: December 2023

Let [Formula: see text] be a finite commutative ring with non-zero identity and [Formula: see text] be the set of all non-zero zero-divisors of [Formula: see text]. In this paper, for a non-empty subset [Formula: see text] of [Formula: see text], we introduce a new graph, denoted by [Formula: see text], with vertex set [Formula: see text] and two distinct vertices [Formula: see text] and [Formula: see text] are adjacent if and only if there is [Formula: see text] such that [Formula: see text]. Then, we investigate some properties of the graph [Formula: see text].
Fabrication and multiphysics numerical investigations of carbon fiber structural batteries
Authors: Liang Jianhai , Du Chunzhi
Published: December 2023

The structural battery (SB) that uses carbon fibers as anodes is a lightweight composite material battery integrating load bearing and energy storage. However, during usage, diffusion-induced stress may result in structural failure and a reduction in the battery capacity. In this paper, a laminated structural battery (LSB) was fabricated using carbon fiber cloth as an anode and collector and successfully powered LEDs. Then, based on this laminated structure, a multiphysics field model based on electrochemical-mechanical coupling is established for the analysis of complicated diffusion-induced stresses in carbon fibers. Following this model, researchers investigate the electro-chemo-mechanical behaviors and influencing factors of the SB and compare them with those of the lithium-ion battery (LIB). The results show that diffusion polarization in carbon fibers is aggravated by their lower diffusion coefficient, larger radius, and higher discharge rate; in both types of batteries, there is a higher tensile diffusion stress and a higher probability of failure at the surface of the anode active material than in the interior; a more uniform distribution of diffusion-induced stress on the surface of fibers at different locations; when discharging at a high rate, the SB capacity hardly reduces, and the high strength and relatively low diffusion-induced stress in the carbon fiber make it much safer, showing excellent electrochemical performance and resistance to failure due to diffusion-induced stress. This study demonstrates the feasibility of the fabrication and safety of carbon fiber structural batteries, which may potentially offer recommendations for the fabrication and use of novel SBs.
Luminescent metal organic frameworks and their fluorescence sensing application
Authors: Yuan Mengnan , Chen Fengzhen , Zhang Liang , Wang Dandan , Ji Zhiqiang
Published: December 2023

Metal-organic framework materials (MOFs) provide remarkable advantages in the field of identification and detection due to their structural flexibility, aperture adaptation, and distinctive fluorescence features. Fluorescent MOF-based chemical sensors have been developed more regularly in recent years to detect a wide range of chemicals. By introducing the research status of MOFs, the research advancement of fluorescence sensors, the overview of MOF materials, construction methods, and luminescence mechanism, as well as the suggestion of further research on the application of MOFs in the low-temperature environment, the use of fluorescent metal-organic skeleton materials as fluorescence sensors is briefly analyzed.
Degradation of sodium butylxanthate in flotation wastewater by natural pyrite via visible light-assisted advanced oxidation processes: A comprehensive study
Authors: Fan Jiawei , Wang Jinpeng , Jiang Lisha , Li Yubiao , Wu Xiaoyong
Published: December 2023

The advanced oxidation process (AOPs) based on natural pyrite (NP) as a catalyst is a promising strategy for flotation wastewater purification. However, the slow Fe(III)/Fe(II) cycle limits the number of active sites during oxidant activation. Therefore, this work attempts to accelerate Fe(III)/Fe(II) reduction on NP surface by introducing visible light irradiation. By comparing the degradation performance of sodium butylxanthate (SBX) via various oxidants (peroxydisulfate, periodate, hydrogen peroxide, and sulfite) activation under visible light, periodate was selected as the most suitable oxidant for SBX removal. Although peroxydisulfate exhibits optimal SBX degradation in acidic environments, its failure in alkaline environments and poor reusability caused by high surface oxidation limit its application. The investigation of active species suggests that O2[Formula: see text] are the dominant active species in hydrogen peroxide and sulfite activation systems, while the electron transfer process is the dominant active species in peroxydisulfate and periodate activation systems. Electrochemistry elucidates the interaction of different oxidants on the NP surface. XPS analysis revealed that the photogenerated electrons induced by visible light irradiation accelerate the Fe(III)/Fe(II) cycle on NP surface, thereby improving the continuous activation of NP towards the oxidants. In addition, the ecotoxicity of wastewater treated by the periodate activation system is greatly reduced. This work provides a new idea towards flotation wastewater purification.
A new application to cryptology using generalized Jacobsthal matrices, inner product and self-adjoint operator
Authors: İrge Vedat , Soykan Yüksel
Published: December 2023

In this paper, we first examine the properties of the matrices of the generalized [Formula: see text]-step Jacobsthal sequence, and we develop a public key cryptosystem using key exchange based on the relationship between inner product and orthogonality. Then we create encryption and decryption algorithms using this key exchange, self-adjoint operator, and generalized [Formula: see text]-step Jacobsthal sequence, and we provide a detailed example of how encryption and decryption algorithms work. Finally, a security strength and complexity analysis of these algorithms will be made.
On the Hasse diagram of binary linear codes
Authors: Delgado-Ordoñez Lisbeth D. , Castillo John H. , Holguín-Villa Alexander
Published: December 2023

A binary [Formula: see text]-linear code [Formula: see text] is a [Formula: see text]-dimensional subspace of [Formula: see text]. For [Formula: see text], the set [Formula: see text] is a coset of [Formula: see text]. In this work, we study a partial ordering on the set of cosets of a binary linear code [Formula: see text] of length [Formula: see text]: for [Formula: see text], [Formula: see text] provided that [Formula: see text], and we construct its associated Hasse diagram. We give general and particular examples that help to understand the concept and we obtain general properties of this graph.
Equitable coloring of planar graphs without 5-cycles and chordal 4-cycles
Authors: Wu Xianxi , Huang Danjun
Published: December 2023

An equitable [Formula: see text]-coloring of a graph [Formula: see text] is a proper vertex coloring such that [Formula: see text] for any [Formula: see text], where [Formula: see text] ([Formula: see text]) is the set of vertices colored with [Formula: see text]. If there is an equitable [Formula: see text]-coloring of [Formula: see text], then the graph [Formula: see text] is said to be equitably [Formula: see text]-colorable. In this paper, we prove that every planar graph without 5-cycles and chordal 4-cycles has an equitable [Formula: see text]-coloring for [Formula: see text] where [Formula: see text] is the maximum degree of [Formula: see text].