Reviewerly/exHarmony huggingface dataset:
Fast and Energy‐Efficient Non‐Volatile III‐V‐on‐Silicon Photonic Phase Shifter Based on Memristors

Abstract Silicon photonics has evolved from lab research to commercial products in the past decade as it plays an increasingly crucial role in data communication for next‐generation data centers and high‐performance computing. Recently, programmable silicon photonics has also found new applications in quantum and classical information processing. A key component of programmable silicon photonic integrated circuits (PICs) is the phase shifter, traditionally realized via thermo‐optic or free‐carrier effects that are weak, volatile, and power hungry. A non‐volatile phase shifter can circumvent these limitations by requiring zero power to maintain the switched phases. Previously non‐volatile phase modulation is achieved via phase‐change or ferroelectric materials, but the switching energy remains high (pico to nano joules) and the speed is slow (micro to milliseconds). Here, a non‐volatile III‐V‐on‐silicon photonic phase shifter based on a HfO 2 memristor with sub‐pJ switching energy (≈400 fJ), representing over an order of magnitude improvement in energy efficiency compared to the state of the art, is reported. The non‐volatile phase shifter can be switched reversibly using a single 100 ns pulse and exhibits excellent endurance over 800 cycles. This technology can enable future energy‐efficient programmable PICs for data centers, optical neural networks, and quantum information processing.
Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy

The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanent magnet synchronous motors (PMSMs). To infer the unknown part of the system, machine learning techniques are widely employed, especially Gaussian process regression (GPR) due to its flexibility of continuous system modeling and its guaranteed performance. For practical implementation, distributed GPR is adopted to alleviate the high computational complexity. However, the study of distributed GPR from a control perspective remains an open problem. In this paper, a control-aware optimal aggregation strategy of distributed GPR for PMSMs is proposed based on the Lyapunov stability theory. This strategy exclusively leverages the posterior mean, thereby obviating the need for computationally intensive calculations associated with posterior variance in alternative approaches. Moreover, the straightforward calculation process of our proposed strategy lends itself to seamless implementation in high-frequency PMSM control. The effectiveness of the proposed strategy is demonstrated in the simulations.
An efficient adaptive-mutated Coati optimization algorithm for feature selection and global optimization

The feature selection (FS) problem has occupied a great interest of scientists lately since the highly dimensional datasets might have many redundant and irrelevant features. FS aims to eliminate such features and select the most important ones that affect classification performance. Metaheuristic algorithms are the best choice to solve this combinatorial problem. Recent researchers invented and adapted new algorithms, hybridized many algorithms, or enhanced existing ones by adding some operators to solve the FS problem. In our paper, we added some operators to the Coati optimization algorithm (CoatiOA). The first operator is the adaptive s-best mutation operator to enhance the balance between exploration and exploitation. The second operator is the directional mutation rule that opens the way to discover the search space thoroughly. The final enhancement is controlling the search direction toward the global best. We tested the proposed mCoatiOA algorithm in solving) in solving challenging problems from the CEC'20 test suite. mCoatiOA performance was compared with Dandelion Optimizer (DO), African vultures optimization algorithm (AVOA), Artificial gorilla troops optimizer (GTO), whale optimization algorithm (WOA), Fick's Law Algorithm (FLA), Particle swarm optimization (PSO), Harris hawks optimization (HHO), and Tunicate swarm algorithm (TSA). According to the average fitness, it can be observed that the proposed method, mCoatiOA, performs better than the other optimization algorithms on 8 test functions. It has lower average standard deviation values compared to the competitive algorithms. Wilcoxon test showed that the results obtained by mCoatiOA are significantly different from those of the other rival algorithms. mCoatiOA has been tested as a feature selection algorithm. Fifteen benchmark datasets of various types were collected from the UCI machine-learning repository. Different evaluation criteria are used to determine the effectiveness of the proposed method. The proposed mCoatiOA achieved better results in comparison with other published methods. It achieved the mean best results on 75% of the datasets.
Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting

Large language models (LLMs) demonstrate impressive multilingual capability, but their performance varies substantially across different languages. In this work, we introduce a simple yet effective method, called cross-lingual-thought prompting (XLT), to systematically improve the multilingual capability of LLMs. Specifically, XLT is a generic template prompt that stimulates cross-lingual and logical reasoning skills to enhance task performance across languages. We conduct comprehensive evaluations on 7 typical benchmarks related to reasoning, understanding, and generation tasks, covering both high-resource and low-resource languages. Experimental results show that XLT not only remarkably enhances the performance of various multilingual tasks but also significantly reduces the gap between the average performance and the best performance of each task in different languages. Notably, XLT brings over 10 points of average improvement in arithmetic reasoning and open-domain question-answering tasks.
CRUD Operation on WordPress Posts From C# over REST API

Purpose: WordPress is a sound content management system on the web. We can build a website with little effort. Around 40% of internet websites are powered by WordPress. Using the WordPress website, our researcher can store research data inside the website database without spending money on the cloud. We demonstrate how to run CRUD operations on a WordPress-powered website database using WordPress Posts. We observe the essential CRUD operation so that our researcher can integrate it easily into their project with complex procedures. We use REST API for communication between the WordPress and the client application. We created a GUI in Visual Studio using C# language for easy operation. An HTTP client is used for communication. The complete project code is available to download and continue research work. Design/Methodology/Approach: First, we install a “local” application in our working system. Then, we create a brand-new WordPress website. We activated the latest theme, which is now Twenty twenty-four. We make a custom route and add a posts operation handling module inside the theme folder. This is the server-side action. On the other hand, inside the Visual Studio 2022 community edition, using C# language, we created a GUI-based client application to interact with users. We added an HTTP client module to communicate with the server. Findings/Result: We experimented with practical, real-world examples. The overall performance is excellent and helpful in real-world scenarios without any issues. Network latency is sometimes present, creating problems in real-time data processing. The researcher creating a custom route in their online website can experiment. It will not have any adverse effect on keeping the website live. Originality/Value: WordPress uses the database to store post-type data and some WordPress-related configurations. Out of the traditional way, we can also reserve our research data as post-type content according to our project requirements. Keeping our data as the post-type of content from the C# application is the least available documentation on the web. We demonstrate here through Practical examples. Some valuable reference information can be found here. Paper Type: Experimental-based Research.
Path Planning of a Mobile Robot Based on the Improved RRT Algorithm

RRT (rapidly exploring random tree) is a sampling-based planning algorithm that has been widely used due to its simple structure and fast speed. However, the RRT algorithm has several issues such as low planning efficiency, high randomness, and poor path quality. To address these issues, this paper proposes a novel method, the adjustable probability and sampling area and the Dijkstra optimization-based RRT algorithm (APSD-RRT), which consists of the following two modules: an APS-RRT planner and an optimizer. The APS-RRT planner can reach a feasible path quickly using the proposed adjustable probability and sampling area strategies, while the optimizer applies the Dijkstra algorithm to prune and improve the initial path generated by the APS-RRT planner and smooths-out sharp nodes based on the interpolation method. A series of experiments are conducted to demonstrate that our method can perform much better in terms of the balance between the computing cost and performance.
Scale-Aware Modulation Meet Transformer

This paper presents a new vision Transformer, Scale-Aware Modulation Transformer (SMT), that can handle various downstream tasks efficiently by combining the convolutional network and vision Transformer. The proposed Scale-Aware Modulation (SAM) in the SMT includes two primary novel designs. Firstly, we introduce the Multi-Head Mixed Convolution (MHMC) module, which can capture multi-scale features and expand the receptive field. Secondly, we propose the Scale-Aware Aggregation (SAA) module, which is lightweight but effective, enabling information fusion across different heads. By leveraging these two modules, convolutional modulation is further enhanced. Furthermore, in contrast to prior works that utilized modulations throughout all stages to build an attention-free network, we propose an Evolutionary Hybrid Network (EHN), which can effectively simulate the shift from capturing local to global dependencies as the network becomes deeper, resulting in superior performance. Extensive experiments demonstrate that SMT significantly outperforms existing state-of-the-art models across a wide range of visual tasks. Specifically, SMT with 11.5M / 2.4GFLOPs and 32M / 7.7GFLOPs can achieve 82.2% and 84.3% top-1 accuracy on ImageNet-1K, respectively. After pretrained on ImageNet-22K in 224 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> resolution, it attains 87.1% and 88.1% top-1 accuracy when finetuned with resolution 224 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 384 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. For object detection with Mask R-CNN, the SMT base trained with 1× and 3× schedule outperforms the Swin Transformer counterpart by 4.2 and 1.3 mAP on COCO, respectively. For semantic segmentation with UPerNet, the SMT base test at single- and multi-scale surpasses Swin by 2.0 and 1.1 mIoU respectively on the ADE20K. Our code is available at https://github.com/AFeng-x/SMT.
Zero-1-to-3: Zero-shot One Image to 3D Object

We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image. To perform novel view synthesis in this under-constrained setting, we capitalize on the geometric priors that large-scale diffusion models learn about natural images. Our conditional diffusion model uses a synthetic dataset to learn controls of the relative camera viewpoint, which allow new images to be generated of the same object under a specified camera transformation. Even though it is trained on a synthetic dataset, our model retains a strong zero-shot generalization ability to out-of-distribution datasets as well as in-the-wild images, including impressionist paintings. Our viewpoint-conditioned diffusion approach can further be used for the task of 3D reconstruction from a single image. Qualitative and quantitative experiments show that our method significantly outperforms state-of-the-art single-view 3D reconstruction and novel view synthesis models by leveraging Internet-scale pre-training.
I-ViT: Integer-only Quantization for Efficient Vision Transformer Inference

Vision Transformers (ViTs) have achieved state-of-the-art performance on various computer vision applications. However, these models have considerable storage and computational overheads, making their deployment and efficient inference on edge devices challenging. Quantization is a promising approach to reducing model complexity, and the dyadic arithmetic pipeline can allow the quantized models to perform efficient integer-only inference. Unfortunately, dyadic arithmetic is based on the homogeneity condition in convolutional neural networks, which is not applicable to the non-linear components in ViTs, making integer-only inference of ViTs an open issue. In this paper, we propose I-ViT, an integer-only quantization scheme for ViTs, to enable ViTs to perform the entire computational graph of inference with integer arithmetic and bit-shifting, and without any floating-point arithmetic. In I-ViT, linear operations (e.g., MatMul and Dense) follow the integer-only pipeline with dyadic arithmetic, and non-linear operations (e.g., Softmax, GELU, and LayerNorm) are approximated by the proposed light-weight integer-only arithmetic methods. More specifically, I-ViT applies the proposed Shiftmax and ShiftGELU, which are designed to use integer bit-shifting to approximate the corresponding floating-point operations. We evaluate I-ViT on various benchmark models and the results show that integer-only INT8 quantization achieves comparable (or even slightly higher) accuracy to the full-precision (FP) baseline. Furthermore, we utilize TVM for practical hardware deployment on the GPU’s integer arithmetic units, achieving 3.72 ~ 4.11 inference speedup compared to the FP model. Code of both Pytorch and TVM is released at https://github.com/zkkli/I-ViT.
Core: Cooperative Reconstruction for Multi-Agent Perception

This paper presents Core, a conceptually simple, effective and communication-efficient model for multi-agent cooperative perception. It addresses the task from a novel perspective of cooperative reconstruction, based on two key insights: 1) cooperating agents together provide a more holistic observation of the environment, and 2) the holistic observation can serve as valuable supervision to explicitly guide the model learning how to reconstruct the ideal observation based on collaboration. Core instantiates the idea with three major components: a compressor for each agent to create more compact feature representation for efficient broadcasting, a lightweight attentive collaboration component for cross-agent message aggregation, and a reconstruction module to reconstruct the observation based on aggregated feature representations. This learning-to-reconstruct idea is task-agnostic, and offers clear and reasonable supervision to inspire more effective collaboration, eventually promoting perception tasks. We validate Core on two large-scale multi-agent percetion dataset, OPV2V and V2X-Sim, in two tasks, i.e., 3D object detection and semantic segmentation. Results demonstrate that Core achieves state-of-the-art performance, and is more communication-efficient.
Multimodal Large Language Models: A Survey

The exploration of multimodal language models integrates multiple data types, such as images, text, language, audio, and other heterogeneity. While the latest large language models excel in text-based tasks, they often struggle to understand and process other data types. Multimodal models address this limitation by combining various modalities, enabling a more comprehensive understanding of diverse data. This paper begins by defining the concept of multimodal and examining the historical development of multimodal algorithms. Furthermore, we introduce a range of multimodal products, focusing on the efforts of major technology companies. A practical guide is provided, offering insights into the technical aspects of multimodal models. Moreover, we present a compilation of the latest algorithms and commonly used datasets, providing researchers with valuable resources for experimentation and evaluation. Lastly, we explore the applications of multimodal models and discuss the challenges associated with their development. By addressing these aspects, this paper aims to facilitate a deeper understanding of multimodal models and their potentiality in various domains.
The Students’ Perception on Teaching Narrative Writing Skills through Contextual Teaching and Learning (CTL) Approach

This study aims to investigate the students’ perception on teaching narrative writing skills through contextual teaching and learning (CTL) approach. This study employed a quasi- experimental design with fifty-two eleventh-grade students as participants. The participants were divided into two groups; An experimental group received the CTL approach, and a control group received the conventional way. This study utilized a pre-posttest and perceptional questionnaire. Nonparametric tests (i.e., Mann-Whitney U test and Wilcoxon test) were run to analyze the data in this study. Implementation of the CTL approach through its components positively contributes to teaching narrative writing skills. Meanwhile, the perceptional questionnaires showed that the students' experiment group perceived the implementation of the CTL Approach positively. This study results render teachers’ insight into a practical approach to teaching writing to be able to teach narrative writing. It can be concluded that the students’ perception on teaching narrative writing skills through contextual teaching and learning (CTL) approach have highly positive perception. As recommendation can work at all levels of students’ capability. The implementation of Contextual Teaching and Learning approach can be successful if it is implemented with the appropriate material based on students’ level.
Cavity QED photons for quantum information processing

Abstract Based on a cavity QED framework, we theoretically describe a universal set of logic gates which are implemented by passing a multi-level atom initially in its ground state through a multi-mode cavity. The qubits are encoded on the cavity modes and the atom plays the role of an ancilla which will not be entangled with the final result of a gate operation. We apply the multiphoton resonance theory of Shore to develop effective two- and three-level Hamiltonians, so that the proper values for detunings, coupling coefficients, and interaction times for gate operations can be determined. This enables us to examine a faster iSWAP gate than our previous study and to examine numerically the effects of decoherence on both the iSWAP gate and our previously presented Fredkin gate which used the same multi-mode approach. We also present results that show how conditional measurements of the ancilla atom can improve gate fidelities in these cases.
Variable-Sized, Circular Bokeh Depth of Field Effects

We propose the Flexible Linear-time Area Gather (FLAG) blur algorithm with a variable-sized, circular bokeh for producing depth of field effects on rasterized images. The algorithm is separable (and thus linear). Bokehs can be of any convex shape including circles. The goal is to create a high quality bokeh blur effect by post processing images rendered in real-time by a 3D graphics system. Given only depth and colour information as input, the method performs three passes. The circle of confusion pass calculates the radius of the blur at each pixel and packs the input buffer for the next pass. The horizontal pass samples pixels across each row and outputs a 3D texture packed with blur information. The vertical pass performs a vertical gather on this 3D texture to produce the final blurred image. The time complexity of the algorithm is linear with respect to the maximum radius of the circle of confusion, which compares favorably with the naive algorithm, which is quadratic. The space complexity is linear with respect to the maximum radius of the circle of confusion. The results of our experiments show that the algorithm generates high quality blurred images with variable-sized circular bokehs. The implemented version of the proposed algorithm is consistently faster in practice than the implemented naive algorithm. Although some previous algorithms have provided linear performance scaling and variable sized bokehs, the proposed algorithm provides these while also permitting more flexibility in the allowed blur shapes, including any convex shape.
Credit Card Fraud Detection System: A Survey

The credit card has become the most popular mode of payment for both online as well as regular purchase, in cases of fraud associated with it are also rising. Credit card frauds are increasing day by day regardless of the various techniques developed for its detection. Fraudsters are so expert that they generate new ways for committing fraudulent transactions each day which demands constant innovation for its detection techniques. Most of the techniques based on Artificial Intelligence, Fuzzy logic, neural network, logistic regression, naive Bayesian, Machine learning, Sequence Alignment, decision tree, Bayesian network, meta learning, Genetic Programming etc., these are evolved in detecting various credit card fraudulent transactions. This paper presents a survey of various techniques used in credit card fraud detection mechanisms.
Handwriting recognition using sinusoidal model parameters

Handwriting is produced by the oscillatory motion of the hand in both horizontal and vertical directions, with a constant drift velocity along its writing direction. The velocity profiles of handwriting in these orthogonal directions have an invariant bell-shaped nature with zero velocities at the change in trajectory directions. According to recent studies, online handwriting can be suitably characterized by modeling the velocity profiles with sinusoidal oscillations. In this paper, we propose a set of features derived from sinusoidal modeling of handwriting velocities for online handwriting recognition (HR) task. Although the predominant information of handwriting is modeled in the velocity profiles, the first derivatives of the velocity profiles (i.e. acceleration) and the x- and y-coordinates are also important in characterizing the handwriting. Accordingly, these signals are also modeled by sinusoidal oscillations, and the parameters are utilized as features to develop the HR system. As these parameters are extracted directly using the hand movement generation theory, therefore it may also contain additional information describing the generation of the pattern along with its spatial shape information. The efficacy of the proposed features is shown for character and word recognition task employing hidden Markov model (HMM) and support vector machine (SVM) classifiers. The experiments are conducted on three online handwritten databases: Assamese digit database, UNIPEN English character database and UNIPEN ICROW-03 English word database. The results obtained are promising over the prior works for these databases.