Reviewerly/exHarmony huggingface dataset:
Probing anyonic statistics via Mach-Zehnder interferometry in quantum  computers

We introduce a synthetic Mach-Zehnder interferometer for digitized quantum computing devices to probe fractional exchange statistics of anyonic excitations that appear in quantum spin liquids. Employing an IonQ quantum computer, we apply this scheme to the toric ladder, a quasi-one-dimensional reduction of the toric code. We observe interference patterns resulting from the movement of `electric' excitations in the presence and absence of `magnetic' ones. We model the noise in IonQ via depolarizing Lindbladian dynamics, and find quantitative agreement with the measurements obtained from the quantum device. The synthetic Mach-Zehnder interferometer can thus also serve as an effective means to probe the coherence length and time scales of multi-qubit noisy quantum devices.
On the probability of a Pareto record

Given a sequence of independent random vectors taking values in ${\mathbb R}^d$ and having common continuous distribution $F$, say that the $n^{\rm \scriptsize th}$ observation sets a (Pareto) record if it is not dominated (in every coordinate) by any preceding observation. Let $p_n(F) \equiv p_{n, d}(F)$ denote the probability that the $n^{\rm \scriptsize th}$ observation sets a record. There are many interesting questions to address concerning $p_n$ and multivariate records more generally, but this short paper focuses on how $p_n$ varies with $F$, particularly if, under $F$, the coordinates exhibit negative dependence or positive dependence (rather than independence, a more-studied case). We introduce new notions of negative and positive dependence ideally suited for such a study, called negative record-setting probability dependence (NRSPD) and positive record-setting probability dependence (PRSPD), relate these notions to existing notions of dependence, and for fixed $d \geq 2$ and $n \geq 1$ prove that the image of the mapping $p_n$ on the domain of NRSPD (respectively, PRSPD) distributions is $[p^*_n, 1]$ (resp., $[n^{-1}, p^*_n]$), where $p^*_n$ is the record-setting probability for any $F$ governing independent coordinates.
A Cardiac Audio Classification Method Based on Multidimensional Feature Expression

Abstract Heart sound auscultation plays a crucial role in the early diagnosis of cardiovascular diseases. In recent years, great achievements have been made in automatic heart sound classification; however, most methods are based on segmentation features and traditional classifiers, and do not fully use existing deep networks. This paper proposes a cardiac audio classification method based on image expression of multidimensional features (CACIEMDF). First, a 102-dimensional feature vector is designed by combining the characteristics of heart sound data in the time, frequency and statistical domains. Based on the feature vector, a two-dimensional feature projection space is constructed by PCA dimensionality reduction and the convex hull algorithm, and 102 pairs of coordinate representations of the feature vector in the two-dimensional plane are calculated. Each one-dimensional component of the feature vector corresponds to a pair of 2-d coordinate representations. Finally, the one-dimensional feature component value and its divergence from the two categories are used to fill the three channels of a color image, and a Gaussian model is used to dye the image to enrich its content. The color image is subsequently sent to a deep network such as ResNet50 for classification tasks. In this paper, three public heart sound datasets are fused, and experiments are conducted using the above methods. The results show that the proposed method can achieve a classification accuracy of 95.72% for the two-classification task of heart sounds when combined with the current deep network.
"Whisper and Write Game” as Pre-Writing Activity to Enhance Descriptive Writing Proficiency of Indonesian Seventh Graders

Pre-writing is a preparation process before writing where students gather ideas, organize information, and plan their writing so that pre-writing has a significant influence on student writing outcomes. This study aims to analyze the effect of using whisper and write games as students' pre-writing activities on the descriptive writing abilities of the seventh-grade students at MTs Mamba'us Sholihin Gresik. This research uses class action research (CAR). The results showed that the improvement was seen from the cycle I and cycle II learning that had been implemented, comparing between cycle I and cycle II. The increase in learning outcomes was 76.19%, the increase obtained from the evaluation results in the first cycle was 14.28% and the second cycle was 90.47%. the learning outcomes of the last cycle show that the mastery of learning exceeds the KKM of 75%. Based on the results of the study it was explained that the use of whispering and writing as pre-writing activity games can help to understand and improve students' skills in writing descriptive text. and this can be seen from the increase in the percentage of student scores in each cycle. and the application of CAR can help students achieve the KKM target. The author admits that the writing of this research has several limitations. The limitation of this study is the inappropriate timing of the research because many agendas clashed with the school, so it is hoped that further research can adjust the time to be appropriate.
Emerging Trends and Innovations in Blockchain-Digital Twin Integration for Green Investments

This chapter explores the dynamic interaction between blockchain technology and digital twins in the context of green investing. It examines the revolutionary influence of the synergy of these cutting-edge technologies on the efficiency, transparency, and dependability of sustainable investing methods. The chapter begins with an informative introduction to the fundamentals of blockchain and digital twins, illuminating their separate functions before revealing their joint potential for strengthening green investment initiatives. A detailed assessment of the most recent improvements in blockchain and digital twin technologies follows, focusing on smart contracts, enhanced data analytics, and real-time monitoring. The actual implementation and effects of this integration are presented via illustrative case studies of renewable energy, sustainable infrastructure, and environmental conservation. This chapter delves deeper into the problems of integrating these technologies, providing practical insights and solutions to technical, regulatory, and operational roadblocks.
Automation of complex text СAPTCHA recognition using conditional generative adversarial networks

With the rapid development of Internet technologies, the problems of network security continue to worsen. So, one of the most common methods of maintaining security and preventing malicious attacks is CAPTCHA (fully automated public Turing test). CAPTCHA most often consists of some kind of security code, to bypass which it is necessary to perform a simple task, such as entering a word displayed in an image, solving a basic arithmetic equation, etc. However, the most widely used type of CAPTCHA is still the text type. In the recent years, the development of computer vision and, in particular, neural networks has contributed to a decrease in the resistance to hacking of text CAPTCHA. However, the security and resistance to recognition of complex CAPTCHA containing a lot of noise and distortion is still insufficiently studied. This study examines CAPTCHA, the distinctive feature of which is the use of a large number of different distortions, and each individual image uses its own different set of distortions, that is why even the human eye cannot always recognize what is depicted in the photo. The purpose of this work is to assess the security of sites using the CAPTCHA text type by testing their resistance to an automated solution. This testing will be used for the subsequent development of recommendations for improving the effectiveness of protection mechanisms. The result of the work is an implemented synthetic generator and discriminator of the CGAN architecture, as well as a decoder program, which is a trained convolutional neural network that solves this type of CAPTCHA. The recognition accuracy of the model constructed in the article was 63 % on an initially very limited data set, which shows the information security risks that sites using a similar type of CAPTCHA can carry.
Time-Restricted Double-Spending Attack on PoW-based Blockchains

Numerous blockchain applications are designed with tasks that naturally have finite durations, and hence, a double-spending attack (DSA) on such blockchain applications leans towards being conducted within a finite timeframe, specifically before the completion of their tasks. Furthermore, existing research suggests that practical attackers typically favor executing a DSA within a finite timeframe due to their limited computational resources. These observations serve as the impetus for this paper to investigate a time-restricted DSA (TR-DSA) model on Proof-of-Work based blockchains. In this TR-DSA model, an attacker only mines its branch within a finite timeframe, and the TR-DSA is considered unsuccessful if the attacker's branch fails to surpass the honest miners' branch when the honest miners' branch has grown by a specific number of blocks. First, we developed a general closed-form expression for the success probability of a TR-DSA. This developed probability not only can assist in evaluating the risk of a DSA on blockchain applications with timely tasks, but also can enable practical attackers with limited computational resources to assess the feasibility and expected reward of launching a TR-DSA. In addition, we provide rigorous proof that the success probability of a TR-DSA is no greater than that of a time-unrestricted DSA where the attacker indefinitely mines its branch. This result implies that blockchain applications with timely tasks are less vulnerable to DSAs than blockchain applications that provide attackers with an unlimited timeframe for their attacks. Furthermore, we show that the success probability of a TR-DSA is always smaller than one even though the attacker controls more than half of the hash rate in the network. This result alerts attackers that there is still a risk of failure in launching a TR-DSA even if they amass a majority of the hash rate in the network.
Photon statistics of resonantly driven spectrally diffusive quantum  emitters

In the solid state, a large variety of single-photon emitters present high quality photophysical properties together with a potential for integration. However, in many cases, the host matrix induces fluctuations of the emission wavelength in time, limiting the potential applications based on indistinguishable photons. A deep understanding of the underlying spectral diffusion processes is therefore of high importance for improving the stability of the light emission. Here, we theoretically investigate the photon statistics of an emitter driven by a resonant laser, and subject to either of two qualitatively different stationary spectral diffusion processes - a continuous diffusion process and a process based on discrete spectral jumps, both of which being known to model the spectral diffusion of various solid-state emitters. We show that the statistics of light emission carries several experimentally accessible signatures that allow to discriminate between the two classes of models, both at short times in the intensity correlation function, and at long times in the fluctuations of the integrated intensity. These results establish that resonant excitation combined with photon statistics offers a rich access to the spectral diffusion processes, yielding information that goes beyond the bare characterization of the inhomogeneous shape and noise correlation time. Incidentally, our findings shed a new light on recent experimental results of spectral diffusion of B centers in hexagonal boron nitride, providing more insight in their spectral diffusion mechanisms.
OccTransformer: Improving BEVFormer for 3D camera-only occupancy  prediction

This technical report presents our solution, "occTransformer" for the 3D occupancy prediction track in the autonomous driving challenge at CVPR 2023. Our method builds upon the strong baseline BEVFormer and improves its performance through several simple yet effective techniques. Firstly, we employed data augmentation to increase the diversity of the training data and improve the model's generalization ability. Secondly, we used a strong image backbone to extract more informative features from the input data. Thirdly, we incorporated a 3D unet head to better capture the spatial information of the scene. Fourthly, we added more loss functions to better optimize the model. Additionally, we used an ensemble approach with the occ model BevDet and SurroundOcc to further improve the performance. Most importantly, we integrated 3D detection model StreamPETR to enhance the model's ability to detect objects in the scene. Using these methods, our solution achieved 49.23 miou on the 3D occupancy prediction track in the autonomous driving challenge.
Evaluating Quantized Large Language Models

Post-training quantization (PTQ) has emerged as a promising technique to reduce the cost of large language models (LLMs). Specifically, PTQ can effectively mitigate memory consumption and reduce computational overhead in LLMs. To meet the requirements of both high efficiency and performance across diverse scenarios, a comprehensive evaluation of quantized LLMs is essential to guide the selection of quantization methods. This paper presents a thorough evaluation of these factors by evaluating the effect of PTQ on Weight, Activation, and KV Cache on 11 model families, including OPT, LLaMA2, Falcon, Bloomz, Mistral, ChatGLM, Vicuna, LongChat, StableLM, Gemma, and Mamba, with parameters ranging from 125M to 180B. The evaluation encompasses five types of tasks: basic NLP, emergent ability, trustworthiness, dialogue, and long-context tasks. Moreover, we also evaluate the state-of-the-art (SOTA) quantization methods to demonstrate their applicability. Based on the extensive experiments, we systematically summarize the effect of quantization, provide recommendations to apply quantization techniques, and point out future directions.
Efficiently Computable Safety Bounds for Gaussian Processes in Active  Learning

Active learning of physical systems must commonly respect practical safety constraints, which restricts the exploration of the design space. Gaussian Processes (GPs) and their calibrated uncertainty estimations are widely used for this purpose. In many technical applications the design space is explored via continuous trajectories, along which the safety needs to be assessed. This is particularly challenging for strict safety requirements in GP methods, as it employs computationally expensive Monte-Carlo sampling of high quantiles. We address these challenges by providing provable safety bounds based on the adaptively sampled median of the supremum of the posterior GP. Our method significantly reduces the number of samples required for estimating high safety probabilities, resulting in faster evaluation without sacrificing accuracy and exploration speed. The effectiveness of our safe active learning approach is demonstrated through extensive simulations and validated using a real-world engine example.
Unsupervised Cross-Domain Image Retrieval via Prototypical Optimal  Transport

Unsupervised cross-domain image retrieval (UCIR) aims to retrieve images sharing the same category across diverse domains without relying on labeled data. Prior approaches have typically decomposed the UCIR problem into two distinct tasks: intra-domain representation learning and cross-domain feature alignment. However, these segregated strategies overlook the potential synergies between these tasks. This paper introduces ProtoOT, a novel Optimal Transport formulation explicitly tailored for UCIR, which integrates intra-domain feature representation learning and cross-domain alignment into a unified framework. ProtoOT leverages the strengths of the K-means clustering method to effectively manage distribution imbalances inherent in UCIR. By utilizing K-means for generating initial prototypes and approximating class marginal distributions, we modify the constraints in Optimal Transport accordingly, significantly enhancing its performance in UCIR scenarios. Furthermore, we incorporate contrastive learning into the ProtoOT framework to further improve representation learning. This encourages local semantic consistency among features with similar semantics, while also explicitly enforcing separation between features and unmatched prototypes, thereby enhancing global discriminativeness. ProtoOT surpasses existing state-of-the-art methods by a notable margin across benchmark datasets. Notably, on DomainNet, ProtoOT achieves an average P@200 enhancement of 24.44%, and on Office-Home, it demonstrates a P@15 improvement of 12.12%. Code is available at https://github.com/HCVLAB/ProtoOT.
Sunshine to Rainstorm: Cross-Weather Knowledge Distillation for Robust  3D Object Detection

LiDAR-based 3D object detection models have traditionally struggled under rainy conditions due to the degraded and noisy scanning signals. Previous research has attempted to address this by simulating the noise from rain to improve the robustness of detection models. However, significant disparities exist between simulated and actual rain-impacted data points. In this work, we propose a novel rain simulation method, termed DRET, that unifies Dynamics and Rainy Environment Theory to provide a cost-effective means of expanding the available realistic rain data for 3D detection training. Furthermore, we present a Sunny-to-Rainy Knowledge Distillation (SRKD) approach to enhance 3D detection under rainy conditions. Extensive experiments on the WaymoOpenDataset large-scale dataset show that, when combined with the state-of-the-art DSVT model and other classical 3D detectors, our proposed framework demonstrates significant detection accuracy improvements, without losing efficiency. Remarkably, our framework also improves detection capabilities under sunny conditions, therefore offering a robust solution for 3D detection regardless of whether the weather is rainy or sunny
TAMM: TriAdapter Multi-Modal Learning for 3D Shape Understanding

The limited scale of current 3D shape datasets hinders the advancements in 3D shape understanding, and motivates multi-modal learning approaches which transfer learned knowledge from data-abundant 2D image and language modalities to 3D shapes. However, even though the image and language representations have been aligned by cross-modal models like CLIP, we find that the image modality fails to contribute as much as the language in existing multi-modal 3D representation learning methods. This is attributed to the domain shift in the 2D images and the distinct focus of each modality. To more effectively leverage both modalities in the pre-training, we introduce TriAdapter Multi-Modal Learning (TAMM) -- a novel two-stage learning approach based on three synergetic adapters. First, our CLIP Image Adapter mitigates the domain gap between 3D-rendered images and natural images, by adapting the visual representations of CLIP for synthetic image-text pairs. Subsequently, our Dual Adapters decouple the 3D shape representation space into two complementary sub-spaces: one focusing on visual attributes and the other for semantic understanding, which ensure a more comprehensive and effective multi-modal pre-training. Extensive experiments demonstrate that TAMM consistently enhances 3D representations for a wide range of 3D encoder architectures, pre-training datasets, and downstream tasks. Notably, we boost the zero-shot classification accuracy on Objaverse-LVIS from 46.8 to 50.7, and improve the 5-way 10-shot linear probing classification accuracy on ModelNet40 from 96.1 to 99.0. Project page: \url{https://alanzhangcs.github.io/tamm-page}.
Enhancing 2D Path Search and PID Control for Autonomous Mobile Robots: A* Algorithm Optimization and Hardware

Intelligent autonomous mobile robots find diverse applications across various fields. This article delves into the optimization of path search planning and implementation for these robots in specific scenarios. The study enhances the traditional right-hand principle's search strategy, integrating a forward-looking approach grounded in the goal-oriented law with A* algorithm path planning. Emphasizing the algorithm's lightweight and high efficiency, the study also considers its interaction with hardware systems. The real-world application scenario is abstracted into a two-dimensional planar environment. Collaborative efforts between hardware and software systems result in a comprehensive robot system, validated through tests ensuring swift navigation from the starting point to the endpoint along the planned path.
Analisis Deiksis dalam Channel YouTube Satu Persen pada Daftar Putar “Manajemen Waktu”

The studies we took in this research are from the field of pragmatics. The pragmatics study in this research focuses on deictic analysis. Deixis is a part of pragmatics that discusses the expressions or context in a sentence. This research aims to analyze various types of deixis contained in the 'Time Management' video on the Satu Persen YouTube channel. This research was carried out using a qualitative approach, namely a method as a research procedure that produces descriptive data. The research techniques used to produce data are observation techniques, note-taking techniques, writing techniques, and listening techniques. In the analysis of deixis in a series of videos contained in the 'Time Management' playlist via the One Percent YouTube channel that we studied, five types of deixis can be found, namely persona deixis (first person singular, first person plural, second person singular, second person plural, third person singular), place deixis, time deixis (past, present, future), discourse deixis (anaphora, cataphora), and social deixis. Each type of deixis has its own function depending on its context. This research will be interesting for readers who have difficulty managing time.
DAWN: Dashboard for Agricultural Water Use and Nutrient Management—A Predictive Decision Support System to Improve Crop Production in a Changing Climate

Abstract Climate change presents huge challenges to the already-complex decisions faced by U.S. agricultural producers, as seasonal weather patterns increasingly deviate from historical tendencies. Under USDA funding, a transdisciplinary team of researchers, extension experts, educators, and stakeholders is developing a climate decision support Dashboard for Agricultural Water use and Nutrient management (DAWN) to provide Corn Belt farmers with better predictive information. DAWN’s goal is to provide credible, usable information to support decisions by creating infrastructure to make subseasonal-to-seasonal forecasts accessible. DAWN uses an integrated approach to 1) engage stakeholders to coproduce a decision support and information delivery system; 2) build a coupled modeling system to represent and transfer holistic systems knowledge into effective tools; 3) produce reliable forecasts to help stakeholders optimize crop productivity and environmental quality; and 4) integrate research and extension into experiential, transdisciplinary education. This article presents DAWN’s framework for integrating climate–agriculture research, extension, and education to bridge science and service. We also present key challenges to the creation and delivery of decision support, specifically in infrastructure development, coproduction and trust building with stakeholders, product design, effective communication, and moving tools toward use.