Reviewerly/exHarmony huggingface dataset:
ManQala: Game-Inspired Strategies for Quantum State Engineering

The ability to prepare systems in specific target states through quantum engineering is essential for realizing the new technologies promised by a second quantum revolution. Here, we recast the fundamental problem of state preparation in high-dimensional Hilbert spaces as ManQala, a quantum game inspired by the West African sowing game mancala. Motivated by optimal gameplay in solitaire mancala, where nested nearest-neighbor permutations and actions evolve the state of the game board to its target configuration, ManQala acts as a pre-processing approach for deterministically arranging particles in a quantum control problem. Once pre-processing with ManQala is complete, existing quantum control methods are applied, but now with a reduced search space. We find that ManQala-type strategies match, or outperform, competing approaches in terms of final state variance even in small-scale quantum state engineering problems where we expect the slightest advantage since the relative reduction in search space is the least. These results suggest that ManQala provides a rich platform for designing control protocols relevant to near-term intermediate-scale quantum technologies.
Distributed Randomized Kaczmarz for the Adversarial Workers

Developing large-scale distributed methods that are robust to the presence of adversarial or corrupted workers is an important part of making such methods practical for real-world problems. In this paper, we propose an iterative approach that is adversary-tolerant for convex optimization problems. By leveraging simple statistics, our method ensures convergence and is capable of adapting to adversarial distributions. Additionally, the efficiency of the proposed methods for solving convex problems is shown in simulations with the presence of adversaries. Through simulations, we demonstrate the efficiency of our approach in the presence of adversaries and its ability to identify adversarial workers with high accuracy and tolerate varying levels of adversary rates.
Pushing One Pair of Labels Apart Each Time in Multi-Label Learning: From Single Positive to Full Labels

In Multi-Label Learning (MLL), it is extremely challenging to accurately annotate every appearing object due to expensive costs and limited knowledge. When facing such a challenge, a more practical and cheaper alternative should be Single Positive Multi-Label Learning (SPMLL), where only one positive label needs to be provided per sample. Existing SPMLL methods usually assume unknown labels as negatives, which inevitably introduces false negatives as noisy labels. More seriously, Binary Cross Entropy (BCE) loss is often used for training, which is notoriously not robust to noisy labels. To mitigate this issue, we customize an objective function for SPMLL by pushing only one pair of labels apart each time to prevent the domination of negative labels, which is the main culprit of fitting noisy labels in SPMLL. To further combat such noisy labels, we explore the high-rankness of label matrix, which can also push apart different labels. By directly extending from SPMLL to MLL with full labels, a unified loss applicable to both settings is derived. Experiments on real datasets demonstrate that the proposed loss not only performs more robustly to noisy labels for SPMLL but also works well for full labels. Besides, we empirically discover that high-rankness can mitigate the dramatic performance drop in SPMLL. Most surprisingly, even without any regularization or fine-tuned label correction, only adopting our loss defeats state-of-the-art SPMLL methods on CUB, a dataset that severely lacks labels.
AccelTran: A Sparsity-Aware Accelerator for Dynamic Inference with Transformers

Self-attention-based transformer models have achieved tremendous success in the domain of natural language processing. Despite their efficacy, accelerating the transformer is challenging due to its quadratic computational complexity and large activation sizes. Existing transformer accelerators attempt to prune its tokens to reduce memory access, albeit with high compute overheads. Moreover, previous works directly operate on large matrices involved in the attention operation, which limits hardware utilization. In order to address these challenges, this work proposes a novel dynamic inference scheme, DynaTran, which prunes activations at runtime with low overhead, substantially reducing the number of ineffectual operations. This improves the throughput of transformer inference. We further propose tiling the matrices in transformer operations along with diverse dataflows to improve data reuse, thus enabling higher energy efficiency. To effectively implement these methods, we propose AccelTran, a novel accelerator architecture for transformers. Extensive experiments with different models and benchmarks demonstrate that DynaTran achieves higher accuracy than the state-of-the-art top-k hardware-aware pruning strategy while attaining up to 1.2$\times$ higher sparsity. One of our proposed accelerators, AccelTran-Edge, achieves 330K$\times$ higher throughput with 93K$\times$ lower energy requirement when compared to a Raspberry Pi device. On the other hand, AccelTran-Server achieves 5.73$\times$ higher throughput and 3.69$\times$ lower energy consumption compared to the state-of-the-art transformer co-processor, Energon. The simulation source code is available at https://github.com/jha-lab/acceltran.
Formalising and Learning a Quantum Model of Concepts

In this report we present a new modelling framework for concepts based on quantum theory, and demonstrate how the conceptual representations can be learned automatically from data. A contribution of the work is a thorough category-theoretic formalisation of our framework. We claim that the use of category theory, and in particular the use of string diagrams to describe quantum processes, helps elucidate some of the most important features of our quantum approach to concept modelling. Our approach builds upon Gardenfors' classical framework of conceptual spaces, in which cognition is modelled geometrically through the use of convex spaces, which in turn factorise in terms of simpler spaces called domains. We show how concepts from the domains of shape, colour, size and position can be learned from images of simple shapes, where individual images are represented as quantum states and concepts as quantum effects. Concepts are learned by a hybrid classical-quantum network trained to perform concept classification, where the classical image processing is carried out by a convolutional neural network and the quantum representations are produced by a parameterised quantum circuit. We also use discarding to produce mixed effects, which can then be used to learn concepts which only apply to a subset of the domains, and show how entanglement (together with discarding) can be used to capture interesting correlations across domains. Finally, we consider the question of whether our quantum models of concepts can be considered conceptual spaces in the Gardenfors sense.
High Probability Convergence of Stochastic Gradient Methods

In this work, we describe a generic approach to show convergence with high probability for both stochastic convex and non-convex optimization with sub-Gaussian noise. In previous works for convex optimization, either the convergence is only in expectation or the bound depends on the diameter of the domain. Instead, we show high probability convergence with bounds depending on the initial distance to the optimal solution. The algorithms use step sizes analogous to the standard settings and are universal to Lipschitz functions, smooth functions, and their linear combinations. This method can be applied to the non-convex case. We demonstrate an $O((1+\sigma^{2}\log(1/\delta))/T+\sigma/\sqrt{T})$ convergence rate when the number of iterations $T$ is known and an $O((1+\sigma^{2}\log(T/\delta))/\sqrt{T})$ convergence rate when $T$ is unknown for SGD, where $1-\delta$ is the desired success probability. These bounds improve over existing bounds in the literature. Additionally, we demonstrate that our techniques can be used to obtain high probability bound for AdaGrad-Norm (Ward et al., 2019) that removes the bounded gradients assumption from previous works. Furthermore, our technique for AdaGrad-Norm extends to the standard per-coordinate AdaGrad algorithm (Duchi et al., 2011), providing the first noise-adapted high probability convergence for AdaGrad.
ROG-Map: An Efficient Robocentric Occupancy Grid Map for Large-scene and High-resolution LiDAR-based Motion Planning

Recent advances in LiDAR technology have opened up new possibilities for robotic navigation. Given the widespread use of occupancy grid maps (OGMs) in robotic motion planning, this paper aims to address the challenges of integrating LiDAR with OGMs. To this end, we propose ROG-Map, a uniform grid-based OGM that maintains a local map moving along with the robot to enable efficient map operation and reduce memory costs for large-scene autonomous flight. Moreover, we present a novel incremental obstacle inflation method that significantly reduces the computational cost of inflation. The proposed method outperforms state-of-the-art (SOTA) methods on various public datasets. To demonstrate the effectiveness and efficiency of ROG-Map, we integrate it into a complete quadrotor system and perform autonomous flights against both small obstacles and large-scale scenes. During real-world flight tests with a 0.05 m resolution local map and 30mx30mx12m local map size, ROG-Map takes only 29.8% of frame time on average to update the map at a frame rate of 50 Hz (\ie, 5.96 ms in 20 ms), including 0.33% (i.e., 0.66 ms) to perform obstacle inflation, demonstrating outstanding real-world performance. We release ROG-Map as an open-source ROS package to promote the development of LiDAR-based motion planning.
VQA with Cascade of Self- and Co-Attention Blocks

The use of complex attention modules has improved the performance of the Visual Question Answering (VQA) task. This work aims to learn an improved multi-modal representation through dense interaction of visual and textual modalities. The proposed model has an attention block containing both self-attention and co-attention on image and text. The self-attention modules provide the contextual information of objects (for an image) and words (for a question) that are crucial for inferring an answer. On the other hand, co-attention aids the interaction of image and text. Further, fine-grained information is obtained from two modalities by using a Cascade of Self- and Co-Attention blocks (CSCA). This proposal is benchmarked on the widely used VQA2.0 and TDIUC datasets. The efficacy of key components of the model and cascading of attention modules are demonstrated by experiments involving ablation analysis.
Joint Representations of Text and Knowledge Graphs for Retrieval and Evaluation

A key feature of neural models is that they can produce semantic vector representations of objects (texts, images, speech, etc.) ensuring that similar objects are close to each other in the vector space. While much work has focused on learning representations for other modalities, there are no aligned cross-modal representations for text and knowledge base (KB) elements. One challenge for learning such representations is the lack of parallel data, which we use contrastive training on heuristics-based datasets and data augmentation to overcome, training embedding models on (KB graph, text) pairs. On WebNLG, a cleaner manually crafted dataset, we show that they learn aligned representations suitable for retrieval. We then fine-tune on annotated data to create EREDAT (Ensembled Representations for Evaluation of DAta-to-Text), a similarity metric between English text and KB graphs. EREDAT outperforms or matches state-of-the-art metrics in terms of correlation with human judgments on WebNLG even though, unlike them, it does not require a reference text to compare against.
Penerapan Sistem Informasi E-Raport pada Sekolah di Kota Tangerang

Currently information technology has experienced rapid development so that information can be accessed quickly. One of the applications of technology in education today is the existence of an internet-based information system for report card grades or what we usually know as online report cards. This has changed the pattern of society in various aspects, especially the world of education. The e-Raport system is used as an information system designed by schools to inform parents of student progress, which has been implemented in almost all schools in Tangerang City. For example, SMPN 8 Tangerang City is one of the public schools in Tangerang City which uses the e-Raport System as a substitute for manual student learning outcomes reports. The problems raised were the teacher's lack of understanding in accessing e-reports when inputting grades and the lack of understanding of parents in accessing e-reports to see children's development. This research is a literature study that aims to collect information related to the use of online report card information systems implemented by several schools, the results obtained from this literature study are that the online report card system makes it easy for educators to process student value data that can be accessed both by students and parents.
Basic Data Management in R

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Extended Affine Root Supersystems of Types $C(I, J)$ and $BC(1, 1)$

In this paper, we complete the characterization of tame irreducible extended affine root supersystems.We give a complete description of tame irreducible extended affine root supersystems of type X = C(1, 1), C(1, 2), C(2, 2) and BC(1, 1) and determine isomorphic classes.
Increase of signal to reference ratio for phase compensation in continuous-variable quantum key distribution systems

Continuous variables quantum key distribution (CV-QKD) systems are a promising direction for quantum communications.Coherent detection, which is the basis of CV-QKD, requires taking into consideration and compensating phase distortions.Phase compensation algorithms rely on using reference pulses for phase drift estimation and correcting signal quadratures.The ratio of the number of reference pulses to that of the signal ones, affects the accuracy of the phase compensation algorithm.On the other hand, it influences the secure key rate (SKR).The paper considers the effect of the reference to signal ratio on the SKR, and proposes a modification of the phase compensation algorithm, which allows using a smaller number of references at a pulse repetition frequency close to that of the system phase noise, which results in increasing SKR.We also propose a method for estimating the phase noise in the system for selection of the optimal signal to reference ratio.
INKUBAKTOR PENETAS TELUR OTOMATIS BERBASIS FUZZY LOGIC CONTROL

ABSTRACT&#x0D; &#x0D; The development of technology in today's era has had a profound impact on people's lives. One of them is the creation of an egg incubator. However, the existing egg incubator still uses a simple concept with several weaknesses, including still using the on/off method on the switch, the temperature level required for eggs to hatch is still low, and a manual schedule for egg reversal is still needed. intends to make a research entitled "Automatic Egg Incubator Based on Fuzzy Logic Control". The main controller used in the manufacture of this tool uses Arduino uno which has been programmed with Fuzzy Logic Control then connected to the DHT11 sensor actuator to detect the temperature and humidity values. The DHT11 sensor is used to view the recorded temperature value. And the stepping motor actuator acts as an automatic egg rotation every time. 3 hours. The application of fuzzy logic control in this study produces a stable response according to the system that has been set.&#x0D; &#x0D; Keywords: Egg Incubator, Sensor DHT11, Arduino Uno, Fuzzy Logic&#x0D; &#x0D; ABSTRAK&#x0D; &#x0D; Perkembangan teknologi di zaman sekarang memang sangat berdampak pada kehidupan masyarakat. Salah satu nya adalah terciptanya alat penetas telur. Namun, alat penetas telur yang sudah ada masih menggunakan konsep sederhana dengan beberapa kelemahan, diantaranya masih menggunakan metode mati/nyala pada saklar, tingkat temperatur yang diperlukan telur untuk menetas masih rendah, dan masih diperlukan jadwal manual untuk pembalikan telur.Dari beberapa kelemahan inilah peneliti bermaksud membuat penelitian yang berjudul “Inkubator Penetas Telur Otomatis Berbasis Fuzzy Logic Control”. Kontroller utama yang digunakan dalam pembuatan alat ini menggunakan Arduino uno yang telah terprogram dengan Fuzzy Logic Control kemudian disambungkan dengan aktuator sensor DHT11 untuk mendeteksi nilai temperatur dan kelembapan.Sensor DHT11 digunakan untuk melihat nilai suhu yang terekam.Dan aktuator motor stepping sebagai putaran telur otomatis setiap 3 jam. Penerapan fuzzy logic control pada penelitian ini menghasilkan respon yang stabil sesuai dengan system yang sudah di atur.&#x0D; &#x0D; Kata Kunci: Inkubator Telur, Sensor DHT11, Arduino Uno, Fuzzy Logic&#x0D;
Perceptions on The Implementation of Information Services in Class XI High Schools in Bayah District, Lebak Banten Regency

This research is expected to help Guidance and Counseling Teachers know students' perceptions of BK services provided in schools. This research is a quantitative descriptive study with a population of 612 students of class XI at the senior high school level in the Bayah District. The sample taken in this study was 242 students. The instrument in this study was a questionnaire on students' perceptions of information services which consisted of 52 statement items. The results of the research as a whole show that students' perceptions of the implementation of information services in high school level schools in Bayah District, obtained students who have perceptions in the very low category as much as 7.4%, the low category as many as 16.9%, the medium category as much as 52.5%, the high category as many as 17.4 % and very high category as much as 5.8%. Based on this, it is concluded that most students have a moderate perception of the implementation of information services in schools, as much as 52.5%.&#x0D;
On the Examination of Balinese Cultures upon Animal and Plant Lexicons in Balinese Songs: An Eco-Linguistics Study

This study aims to know the relation of Balinese cultures with the animal and plant lexicons in Balinese songs from the perspective of eco-linguistics. The data from this study were collected from YouTube. Listening and documentation methods along with transcription techniques were applied to collect the data. The animal and plant lexicons found were subsequently analyzed using the method of descriptive-qualitative with the help of Bang and Døør’s theory of dialectical eco-linguistics and the eco-linguistics parameters from Fill and Muhlhausler. The result of this study shows that the animal and plant lexicons used resulted from the co-existence of Balinese people and other species in their environment. The lexicons are inserted into the songs through the concept of metaphor. The co-existence makes Balinese people able to give meanings towards the animal and plant in the song based on their cultures and beliefs.
Café Selection Recommendation System in Semarang City uses Collaborative Filtering Method with Item based Filtering Algorithm

The café selection recommendation system in the city of Semarang aims to provide recommendations for users in finding the desired café according to the type of café expected. This recommendation system serves to predict an item that is of interest to the user. Implementation of recommendation system using Collaborative Filtering and Item Based Filtering algorithms. Collaborative filtering is a recommendation system algorithm where recommendations are given based on consideration of data from other users while the Item Based Filtering algorithm to provide recommendations based on similarities between customer tastes and café characteristics.
A REVIEW ON PREDICTION MODEL FOR BREAST CANCER USING MACHINE LEARNING

One of the commonly encountered in which blood cells begin to proliferate erratically is breast cancer.There are several forms of breast cancer that can be fatal to the patient who's been diagnosed with it.Numerous things, such as genes, changes in the economy, social circumstances, and lifestyle choices, might contribute to it.Several research and articles have been written in this area to address the problem of such a terminal disease and offer answers by figuring out how to assist patients.In order to better comprehend the project's approach and to compare and contrast various machine learning algorithms, this paper is aimed at informing the reader about breast cancer.Machine learning can be employed to identify undetected trends for the identification of breast cancer by emphasizing the creation of a model with a high level of accuracy.The suggested strategy can be integrated with other machine learning approaches depending on the appropriateness and requirements of the project.
Energy Transformation and Flow

Energy is the driving force behind everything we know and experience. To ensure its availability we seek out energy sources of every type real and imaginable. Its presence in every aspect of our lives gives us the illusion that we understand what it is. Yet every branch of science has a different description of energy that is suitable in its own area of expertise, but is not applicable in general. It may come as a surprise then that energy has properties of its own, verifiable by experiment, that are universal and extend throughout all of Nature from the tiniest particles to the heavenly bodies and life itself. When we explore the universal properties of energy we will find that they are embodied most intensely in the phenomenon of life. We conclude in these pages that any scientific theory that claims to be complete must include life.
Use of flash card media to improve english vocabulary memory for class V students at SDN 205/IV Jambi

This study aims to describe the use of flashcard media in learning English vocabulary in 5th-grade elementary school students and improve the memory of 5th-grade elementary school students through the use of flashcard media. This study used the theory of Djunaidi Fauzan (2012). This study uses a descriptive qualitative method. This research explains facts and characteristics factually and carefully, which will later be drawn to conclusions. The participants in this study consisted of 4 people: three students and an English teacher. This study collected data using observation, interview, and test techniques. The results showed that learning using flashcard media improved students' memory in learning English for class V at SDN 2015/IV Jambi. The increase in students' vocabulary can be seen from the observation results, which increased by several points, and the test results, which increased by 15%. Apart from observations and tests, the results of interviews also show that the use of flash card media can attract students' attention so that their memory of the new vocabulary also acquired increases.