Reviewerly/exHarmony huggingface dataset:
Preserving large-scale features in simulations of elastic turbulence

Simulations of elastic turbulence, the chaotic flow of highly elastic and inertialess polymer solutions, are plagued by numerical difficulties: The chaotically advected polymer conformation tensor develops extremely large gradients and can loose its positive definiteness, which triggers numerical instabilities. While efforts to tackle these issues have produced a plethora of specialized techniques -- tensor decompositions, artificial diffusion, and shock-capturing advection schemes -- we still lack an unambiguous route to accurate and efficient simulations. In this work, we show that even when a simulation is numerically stable, maintaining positive-definiteness and displaying the expected chaotic fluctuations, it can still suffer from errors significant enough to distort the large-scale dynamics and flow-structures. Focusing on two-dimensional simulations of the Oldroyd-B and FENE-P equations, we first compare two decompositions of the conformation tensor: symmetric square root (SSR) and Cholesky with a logarithmic transformation (Cholesky-log). While both simulations yield chaotic flows, only the Cholesky-log preserves the pattern of the forcing, i.e., its vortical cells remain ordered in a lattice as opposed to the vortices of the SSR simulations which shrink, expand and reorient constantly. To identify the accurate simulation, we appeal to a hitherto overlooked mathematical bound on the determinant of the conformation tensor, which unequivocally rejects the SSR simulation. Importantly, the accuracy of the Cholesky-log simulation is shown to arise from the logarithmic transformation. We then consider local artificial diffusion, a potential low-cost alternative to high-order advection schemes, and find unfortunately that it significantly modifies the dynamics. We end with an example, showing how the spurious large-scale motions identified here contaminate predictions of scalar mixing.
Speed limits of two-qubit gates with qudits

The speed of elementary quantum gates ultimately sets the limit on the speed at which quantum circuits can operate. For a fixed physical interaction strength between two qubits, the speed of any two-qubit gate is limited even with arbitrarily fast single-qubit gates. In this work, we explore the possibilities of speeding up two-qubit gates beyond such a limit by expanding our computational space outside the qubit subspace, which is experimentally relevant for qubits encoded in multi-level atoms or anharmonic oscillators. We identify an optimal theoretical bound for the speed limit of a two-qubit gate achieved using two qudits with a bounded interaction strength and arbitrarily fast single-qudit gates. In addition, we find an experimentally feasible protocol using two parametrically coupled superconducting transmons that achieves this theoretical speed limit in a non-trivial way. We also consider practical scenarios with limited single-qudit drive strengths and off-resonant transitions. For such scenarios, we develop an open-source, machine learning assisted, quantum optimal control algorithm that can achieve a speedup close to the theoretical limit with near-perfect gate fidelity. This work opens up a new avenue to speed up two-qubit gates when the physical interaction strength between qubits cannot be easily increased while extra states outside the qubit subspace can be well controlled.
Fast Sampling via De-randomization for Discrete Diffusion Models

Diffusion models have emerged as powerful tools for high-quality data generation, such as image generation. Despite its success in continuous spaces, discrete diffusion models, which apply to domains such as texts and natural languages, remain under-studied and often suffer from slow generation speed. In this paper, we propose a novel de-randomized diffusion process, which leads to an accelerated algorithm for discrete diffusion models. Our technique significantly reduces the number of function evaluations (i.e., calls to the neural network), making the sampling process much faster. Furthermore, we introduce a continuous-time (i.e., infinite-step) sampling algorithm that can provide even better sample qualities than its discrete-time (finite-step) counterpart. Extensive experiments on natural language generation and machine translation tasks demonstrate the superior performance of our method in terms of both generation speed and sample quality over existing methods for discrete diffusion models.
DIRECT: Deep Active Learning under Imbalance and Label Noise

Class imbalance is a prevalent issue in real world machine learning applications, often leading to poor performance in rare and minority classes. With an abundance of wild unlabeled data, active learning is perhaps the most effective technique in solving the problem at its root -- collecting a more balanced and informative set of labeled examples during annotation. Label noise is another common issue in data annotation jobs, which is especially challenging for active learning methods. In this work, we conduct the first study of active learning under both class imbalance and label noise. We propose a novel algorithm that robustly identifies the class separation threshold and annotates the most uncertain examples that are closest from it. Through a novel reduction to one-dimensional active learning, our algorithm DIRECT is able to leverage the classic active learning literature to address issues such as batch labeling and tolerance towards label noise. We present extensive experiments on imbalanced datasets with and without label noise. Our results demonstrate that DIRECT can save more than 60% of the annotation budget compared to state-of-art active learning algorithms and more than 80% of annotation budget compared to random sampling.
Vision-Language Models as a Source of Rewards

Building generalist agents that can accomplish many goals in rich open-ended environments is one of the research frontiers for reinforcement learning. A key limiting factor for building generalist agents with RL has been the need for a large number of reward functions for achieving different goals. We investigate the feasibility of using off-the-shelf vision-language models, or VLMs, as sources of rewards for reinforcement learning agents. We show how rewards for visual achievement of a variety of language goals can be derived from the CLIP family of models, and used to train RL agents that can achieve a variety of language goals. We showcase this approach in two distinct visual domains and present a scaling trend showing how larger VLMs lead to more accurate rewards for visual goal achievement, which in turn produces more capable RL agents.
FineControlNet: Fine-level Text Control for Image Generation with Spatially Aligned Text Control Injection

Recently introduced ControlNet has the ability to steer the text-driven image generation process with geometric input such as human 2D pose, or edge features. While ControlNet provides control over the geometric form of the instances in the generated image, it lacks the capability to dictate the visual appearance of each instance. We present FineControlNet to provide fine control over each instance's appearance while maintaining the precise pose control capability. Specifically, we develop and demonstrate FineControlNet with geometric control via human pose images and appearance control via instance-level text prompts. The spatial alignment of instance-specific text prompts and 2D poses in latent space enables the fine control capabilities of FineControlNet. We evaluate the performance of FineControlNet with rigorous comparison against state-of-the-art pose-conditioned text-to-image diffusion models. FineControlNet achieves superior performance in generating images that follow the user-provided instance-specific text prompts and poses compared with existing methods. Project webpage: https://samsunglabs.github.io/FineControlNet-project-page
LIME: Localized Image Editing via Attention Regularization in Diffusion Models

Diffusion models (DMs) have gained prominence due to their ability to generate high-quality, varied images, with recent advancements in text-to-image generation. The research focus is now shifting towards the controllability of DMs. A significant challenge within this domain is localized editing, where specific areas of an image are modified without affecting the rest of the content. This paper introduces LIME for localized image editing in diffusion models that do not require user-specified regions of interest (RoI) or additional text input. Our method employs features from pre-trained methods and a simple clustering technique to obtain precise semantic segmentation maps. Then, by leveraging cross-attention maps, it refines these segments for localized edits. Finally, we propose a novel cross-attention regularization technique that penalizes unrelated cross-attention scores in the RoI during the denoising steps, ensuring localized edits. Our approach, without re-training and fine-tuning, consistently improves the performance of existing methods in various editing benchmarks.
Beyond the parametric approximation: pump depletion, entanglement and squeezing in macroscopic down-conversion

We study the dynamics of the pump mode in the down-conversion Hamiltonian using the cumulant expansion method, perturbation theory, and the full numerical simulation of systems with a pump mean photon number of up to one hundred thousand. We particularly focus on the properties of the pump-mode such as depletion, entanglement, and squeezing for an experimentally relevant initial state in which the pump mode is initialized in a coherent state. Through this analysis, we obtain the short-time behaviour of various quantities and derive timescales at which the above-mentioned features, which cannot be understood through the parametric approximation, originate in the system. We also provide an entanglement witness involving moments of bosonic operators that can capture the entanglement of the pump mode. Finally, we study the photon-number statistics of the pump and the signal/idler modes to understand the general behaviour of these modes for experimentally relevant time scales.
Effect of Learning Media on Science Learning Achievement In Elementary School Students Class IV

Because by using learning media effectively, it is hoped that students will be able to understand well the theory lessons being taught. The purpose of this research is (a) to determine the level of science learning performance of class IV students who were taught using and not using learning media (on the science learning performance of class IV A students in the previous control group and B) to find out whether there is a positive influence on the use of learning media on the science learning performance of class IV students. This research uses an experimental approach with a comparative design. The population and sample were 80 class IV students. Data collection uses documentation and test techniques, while the data analysis techniques used are descriptive statistical analysis and inferential statistical analysis with the t-test formula. The results of research conducted in class IV of SD Negeri Mangkura III Makassar which consisted of the top 2 groups, namely the control group (class IV.A) and the experimental group (class IV.B) showed that there were differences in learning performance, especially after learning was carried out, where the group The controls were taught science learning in 4 meetings without using the same learning media once, while the experimental group was taught using learning media even though the characteristics were still very simple. There is also a positive impact of using learning media on student performance, where groups of students who are taught using media are more likely to show increased learning performance compared to groups of students who are taught not using learning media.
Aplikasi Mobile Periklanan Digiprom Berbasis Android

Digiprom is a startup that provides ad serving services. These advertisements will later be shown in public places, such as supermarkets, cafes, malls, and others. The problem faced by Digiprom is that there is no information technology to help interact between Digiprom and its clients (businessman). The interactions in question are such as registration as a client, the process of making a contract between the client and Digiprom, the process of monitoring the contract that has been made and the delivery of other information about Digiprom. To solve this problem, an Android-based mobile application will be developed to help Digiprom interact with its clients. This research focuses on the development side of the Android application, while the server side is not included in this study. The development of this application will use the Waterfall method and tested using black box testing and user acceptance testing. The system design is described using three UML diagrams, namely use case diagrams, activity diagrams and class diagrams. Program code written using Kotlin language with MVVM architecture (Model, View, ViewModel). There are three main objects in this system, namely users (businessman/clients), ads (advertisements) and location (advertising locations). Each object will have its own repository. The repository has three data sources, namely network (server), Room Database, and Data Store. The use of MVVM architecture facilitates the development and maintenance process, because if there is a change in the data structure of an object, it will not affect the data structure of other objects. The results of this research (Android application) will be uploaded to the Google Play Store under the name Digiprom.
Teacher's Strategies in the Classroom Setting: Integrating Local Culture into English Language Teaching

Some teachers have a lot of strategies in the learning and teaching process in English class, but only a few teachers link local culture into the learning process in their class, meanwhile the teacher's strategies is an important role in improving students' ability to learn English containing local culture. The design used in this research is a type of narrative inquiry research with a qualitative approach. which is held in the intensive course class of the faculty of teacher training and education in January 2022. The subject of this research is an English teacher who teach in the intensive class at the Faculty of Education and Vocational Studies, Universitas Lancang Kuning. Then, the data from the observations and interviews were analyzed. Based on the results of the interview, the teacher applies information about local culture into eight techniques or strategies; brainstorming, contextualizing topics, conducting class discussions, creating video projects, looking for references/literature, designing lesson syllabus, selecting textbooks, drilling topics. The researcher found that these techniques had an impact on increasing students' ability to learn English with local culture. The findings of this study revealed that bringing local culture in English language teaching gave a significant effect on the students' ability in learning English with local culture at intensive course class. The idea of ​​bringing local culture in the classroom was in line with the implementation of Contextual Teaching and Learning approach. During the teaching and learning process, the students not only learned a foreign language but also became aware of preserving their local culture.
Rancang Bangun Aplikasi Teknik Dasar Taekwondo Menggunakan Animasi 3D Berbasis Android

Taekwondo, essentially a form of self-discipline and a martial art utilizing techniques with bare feet and hands, originated as a ceremonial practice in Korean culture. Over time, it transformed into a crucial self-defense system for Korean warriors. In the contemporary context, Taekwondo has become widely popular, attracting individuals eager to learn but hindered by a lack of foundational knowledge. The application developed in this study serves as a valuable resource for users to grasp the fundamental movements of Taekwondo, encompassing kicks, punches, stances, and techniques. This initiative aims to make the learning process more accessible, allowing individuals to engage with Taekwondo lessons conveniently at any time and from any location. The research involves a collaborative effort with a Taekwondo instructor affiliated with the State Polytechnic Lampung, ensuring the incorporation of authentic martial arts knowledge into the application.
Hybrid AI-based Voice Authentication

Biometric authentication systems reveal individuals' physical or behavioral uniqueness and identify them by comparing them with existing records. Today, many biometric recognition systems, such as fingerprint reading, palm reading, and face reading, are being studied and used. The human voice is also among the techniques used for this purpose. Due to this feature, the human voice performs secure transactions and authentication in various fields. Based on these voice features, we used a dataset of 66,569 voice recordings. The voice recordings were revised to include six sentences of at least six words each from 24 different people to get the maximum benefit from the dataset. The voices in the reduced dataset were labeled as sentences belonging to the same person and sentences belonging to different people and converted into matrix form. A biometric recognition study resulted in a correlation score of 0.88. As a result of these processes, the feasibility of a voice biometric recognition system with artificial intelligence has been demonstrated.
Analysis of Rob Flood Risk on The Coast of East Luwu District Using GIS

Rob floods caused by rising sea levels are a natural disaster that can potentially threaten coastal areas, especially in Indonesia. Tidal floods seriously threaten coastal areas, especially East Luwu Regency. Environmental factors and rapid growth on the coast of East Luwu Regency influence the vulnerability and complexity of the environment. This research aims to identify the spatial distribution of tidal flood risk levels and predict tidal flood inundation in 2050 at the highest tide on the coast of Luwu Timur District. This effort is part of a disaster mitigation strategy due to rising sea levels. The modeling approach involves Geographic Information Systems (GIS) overlaying data and integrating DEM, HHWL, and SLR data for 28 years (1992-2020). The research results show that the coastal areas studied have a high risk related to tidal flooding, with locations closest to the coastline being at the highest risk. In contrast, the risk decreases as you move away from the coastline. Apart from that, the modeling results also estimate that in 2050, inundation will reach a height of 1,570 meters. The area affected by tidal flood inundation has increased in each sub-district. The inundation will spread evenly along the coastline and extend inland due to seawater intrusion. Coastal areas dominated by production land, such as ponds and agricultural areas, are predicted to experience the most extensive impact of inundation compared to other land uses. Emphasizes the need for mitigation efforts to minimize the impacts that may be caused by tidal floods in the future.
Zero-shot Visual Sentiment Prediction via Cross-domain Knowledge Distillation

There are various sentiment theories for categorizing human sentiments into several discrete sentiment categories, which means that the theory used for training sentiment prediction methods does not always match that used in the test phase. As a solution to this problem, zero-shot visual sentiment prediction methods have been proposed to predict unseen sentiments for which no images are available in the training phase. However, the training of these previous zero-shot methods relies on a single sentiment theory, which limits their ability to handle sentiments from other theories. Thus, this paper proposes a more robust zero-shot visual sentiment prediction method that can handle cross-domain sentiments defined in different sentiment theories. Specifically, by focusing on the fact that sentiments are abstract concepts common to humans regardless of whether their theories are different, we incorporate knowledge distillation into our method to construct a teacher–student model that can train the implicit relationships between sentiments defined in different sentiment theories. Furthermore, to enhance sentiment discrimination capability and strengthen the implicit relationships between sentiments, we introduce a novel sentiment loss between the teacher and student models. In this way, our model becomes robust to unseen sentiments by exploiting the implicit relationships between sentiments. The contributions of this paper are the introduction of knowledge distillation and a novel sentiment loss between the teacher and student models for zero-shot visual sentiment prediction, and improved performance of zero-shot visual sentiment prediction. Experiments on several open datasets demonstrate the effectiveness of the proposed method.
Quantum Algorithm for Signal Denoising

This letter presents a novel quantum algorithm for signal denoising, which performs a thresholding in the frequency domain through amplitude amplification and using an adaptive threshold determined by local mean values. The proposed algorithm is able to process both classical and quantum signals. It is parametrically faster than previous classical and quantum denoising algorithms. Numerical results show that it is efficient at removing noise of both classical and quantum origin, significantly outperforming existing quantum algorithms in this respect, especially in the presence of quantum noise.
Unsupervised Domain Adaptation for Instance Segmentation: Extracting Dwellings in Temporary Settlements Across Various Geographical Settings

Dwelling information is essential for humanitarian emergency response during or in the aftermath of disasters, especially in temporary settlement areas hosting forcibly displaced people. To map dwellings, the integration of very highresolution remotely sensed imagery in computer vision models plays a key role. However, state-of-the-art deep learning models have two known downsides: (1) lack of generalization across space and time under changing scenes and object characteristics, and (2) extensive demand for annotated samples for training and validation. Both could pose a critical challenge during an emergency. To bypass this problem, this study deals with unsupervised domain adaptation for instance segmentation using a single-stage instance segmentation model, namely segmenting objects by location (SOLO). The goal is to adapt a SOLO model trained on a labelled source domain to detect dwellings in an unlabelled target domain. In this context, we study three domain adaptation techniques based on adversarial learning, domain discrepancy, and domain alignment mapping. We also propose domain similarity at different levels to understand its implication on domain adaptation. Experiments are conducted on very high-resolution satellite images obtained from four temporary settlement areas located in different countries and exhibiting various spatial characteristics. Analysis results show that in most source-target combinations unsupervised domain adaptation improves the performance by a large margin even surpassing a model trained with supervised learning. There is also an observed performance deviation among implemented strategies and different source-target dataset combinations. From the in-depth analysis of domain similarity at the image, object, and deep feature space levels, the former is more correlated with unsupervised domain adaptation performance.