Reviewerly/exHarmony huggingface dataset:
On the dynamics of a quantum coherent feedback network of cavity-mediated double quantum dot qubits

The purpose of this paper is to present a comprehensive study of a coherent feedback network where the main component consists of two distant double quantum dot (DQD) qubits which are directly coupled to a cavity. This main component has recently been physically realized (van Woerkom et al., Microwave photon-mediated interactions between semiconductor qubits, Physical Review X, 8(4):041018, 2018). The feedback loop is closed by cascading this main component with a beamsplitter. The dynamics of this coherent feedback network is studied from three perspectives. First, an analytic form of the output single-photon state of the network driven by a single-photon state is derived. In contrast to the experimental observations made in the above paper where a laser is used as input, new interesting physical phenomena are revealed by means of single-photon input. Second, excitation probabilities of DQD qubits are computed when the network is driven by a single-photon input state. Finally, if the input is vacuum but one of the two DQD qubits is initialized in its excited state, the explicit expression of the steady-state joint system-field state is derived, which shows that the output single-photon field and the two DQD qubits can form an entangled state if the transition frequencies of two DQD qubits are equal. This analytical expression can be used to interpret experimental results in the existing literature.
Semantic Segmentation of Underwater Imagery: Dataset and Benchmark

In this paper, we present the first large-scale dataset for semantic Segmentation of Underwater IMagery (SUIM). It contains over 1500 images with pixel annotations for eight object categories: fish (vertebrates), reefs (invertebrates), aquatic plants, wrecks/ruins, human divers, robots, and sea-floor. The images have been rigorously collected during oceanic explorations and human-robot collaborative experiments, and annotated by human participants. We also present a benchmark evaluation of state-of-the-art semantic segmentation approaches based on standard performance metrics. In addition, we present SUIM-Net, a fully-convolutional encoder-decoder model that balances the trade-off between performance and computational efficiency. It offers competitive performance while ensuring fast end-to-end inference, which is essential for its use in the autonomy pipeline of visually-guided underwater robots. In particular, we demonstrate its usability benefits for visual servoing, saliency prediction, and detailed scene understanding. With a variety of use cases, the proposed model and benchmark dataset open up promising opportunities for future research in underwater robot vision.
Building a Shared Resource HPC Center Across University Schools and Institutes: A Case Study

Over the past several years, The George Washington University has recruited a significant number of researchers in a wide variety of domains requiring the availability of advanced computational resources. We discuss the challenges and obstacles encountered planning and establishing a first-time high performance computing center at the university level and present a set of solutions that will be useful for any university developing a fledgling high performance computing center. We focus on justification and cost model, strategies for determining anticipated use cases, planning appropriate resources, staffing, user engagement, and metrics for gauging success.
TResNet: High Performance GPU-Dedicated Architecture

Many deep learning models, developed in recent years, reach higher ImageNet accuracy than ResNet50, with fewer or comparable FLOPS count. While FLOPs are often seen as a proxy for network efficiency, when measuring actual GPU training and inference throughput, vanilla ResNet50 is usually significantly faster than its recent competitors, offering better throughput-accuracy trade-off. In this work, we introduce a series of architecture modifications that aim to boost neural networks' accuracy, while retaining their GPU training and inference efficiency. We first demonstrate and discuss the bottlenecks induced by FLOPs-optimizations. We then suggest alternative designs that better utilize GPU structure and assets. Finally, we introduce a new family of GPU-dedicated models, called TResNet, which achieve better accuracy and efficiency than previous ConvNets. Using a TResNet model, with similar GPU throughput to ResNet50, we reach 80.8 top-1 accuracy on ImageNet. Our TResNet models also transfer well and achieve state-of-the-art accuracy on competitive single-label classification datasets such as Stanford cars (96.0%), CIFAR-10 (99.0%), CIFAR-100 (91.5%) and Oxford-Flowers (99.1%). They also perform well on multi-label classification and object detection tasks. Implementation is available at: https://github.com/mrT23/TResNet.
Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem

This work investigates the task of unsupervised model personalization, adapted to continually evolving, unlabeled local user images. We consider the practical scenario where a high capacity server interacts with a myriad of resource-limited edge devices, imposing strong requirements on scalability and local data privacy. We aim to address this challenge within the continual learning paradigm and provide a novel Dual User-Adaptation framework (DUA) to explore the problem. This framework flexibly disentangles user-adaptation into model personalization on the server and local data regularization on the user device, with desirable properties regarding scalability and privacy constraints. First, on the server, we introduce incremental learning of task-specific expert models, subsequently aggregated using a concealed unsupervised user prior. Aggregation avoids retraining, whereas the user prior conceals sensitive raw user data, and grants unsupervised adaptation. Second, local user-adaptation incorporates a domain adaptation point of view, adapting regularizing batch normalization parameters to the user data. We explore various empirical user configurations with different priors in categories and a tenfold of transforms for MIT Indoor Scene recognition, and classify numbers in a combined MNIST and SVHN setup. Extensive experiments yield promising results for data-driven local adaptation and elicit user priors for server adaptation to depend on the model rather than user data. Hence, although user-adaptation remains a challenging open problem, the DUA framework formalizes a principled foundation for personalizing both on server and user device, while maintaining privacy and scalability.
Grounded Situation Recognition

We introduce Grounded Situation Recognition (GSR), a task that requires producing structured semantic summaries of images describing: the primary activity, entities engaged in the activity with their roles (e.g. agent, tool), and bounding-box groundings of entities. GSR presents important technical challenges: identifying semantic saliency, categorizing and localizing a large and diverse set of entities, overcoming semantic sparsity, and disambiguating roles. Moreover, unlike in captioning, GSR is straightforward to evaluate. To study this new task we create the Situations With Groundings (SWiG) dataset which adds 278,336 bounding-box groundings to the 11,538 entity classes in the imsitu dataset. We propose a Joint Situation Localizer and find that jointly predicting situations and groundings with end-to-end training handily outperforms independent training on the entire grounding metric suite with relative gains between 8% and 32%. Finally, we show initial findings on three exciting future directions enabled by our models: conditional querying, visual chaining, and grounded semantic aware image retrieval. Code and data available at https://prior.allenai.org/projects/gsr.
On the intersection distribution of degree three polynomials and related topics

The intersection distribution of a polynomial $f$ over finite field $\mathbb{F}_q$ was recently proposed in Li and Pott (arXiv:2003.06678v1), which concerns the collective behaviour of a collection of polynomials $\{f(x)+cx \mid c \in \mathbb{F}_q\}$. The intersection distribution has an underlying geometric interpretation, which indicates the intersection pattern between the graph of $f$ and the lines in the affine plane $AG(2,q)$. When $q$ is even, the long-standing open problem of classifying o-polynomials can be rephrased in a simple way, namely, classifying all polynomials which have the same intersection distribution as $x^2$. Inspired by this connection, we proceed to consider the next simplest case and derive the intersection distribution for all degree three polynomials over $\mathbb{F}_q$ with $q$ both odd and even. Moreover, we initiate to classify all monomials having the same intersection distribution as $x^3$, where some characterizations of such monomials are obtained and a conjecture is proposed. In addition, two applications of the intersection distributions of degree three polynomials are presented. The first one is the construction of nonisomorphic Steiner triple systems and the second one produces infinite families of Kakeya sets in affine planes with previously unknown sizes.
Fast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networks

Diffeomorphic deformable image registration is crucial in many medical image studies, as it offers unique, special properties including topology preservation and invertibility of the transformation. Recent deep learning-based deformable image registration methods achieve fast image registration by leveraging a convolutional neural network (CNN) to learn the spatial transformation from the synthetic ground truth or the similarity metric. However, these approaches often ignore the topology preservation of the transformation and the smoothness of the transformation which is enforced by a global smoothing energy function alone. Moreover, deep learning-based approaches often estimate the displacement field directly, which cannot guarantee the existence of the inverse transformation. In this paper, we present a novel, efficient unsupervised symmetric image registration method which maximizes the similarity between images within the space of diffeomorphic maps and estimates both forward and inverse transformations simultaneously. We evaluate our method on 3D image registration with a large scale brain image dataset. Our method achieves state-of-the-art registration accuracy and running time while maintaining desirable diffeomorphic properties.
Utilizing Language Relatedness to improve Machine Translation: A Case Study on Languages of the Indian Subcontinent

In this work, we present an extensive study of statistical machine translation involving languages of the Indian subcontinent. These languages are related by genetic and contact relationships. We describe the similarities between Indic languages arising from these relationships. We explore how lexical and orthographic similarity among these languages can be utilized to improve translation quality between Indic languages when limited parallel corpora is available. We also explore how the structural correspondence between Indic languages can be utilized to re-use linguistic resources for English to Indic language translation. Our observations span 90 language pairs from 9 Indic languages and English. To the best of our knowledge, this is the first large-scale study specifically devoted to utilizing language relatedness to improve translation between related languages.
Efficient Intent Detection with Dual Sentence Encoders

Building conversational systems in new domains and with added functionality requires resource-efficient models that work under low-data regimes (i.e., in few-shot setups). Motivated by these requirements, we introduce intent detection methods backed by pretrained dual sentence encoders such as USE and ConveRT. We demonstrate the usefulness and wide applicability of the proposed intent detectors, showing that: 1) they outperform intent detectors based on fine-tuning the full BERT-Large model or using BERT as a fixed black-box encoder on three diverse intent detection data sets; 2) the gains are especially pronounced in few-shot setups (i.e., with only 10 or 30 annotated examples per intent); 3) our intent detectors can be trained in a matter of minutes on a single CPU; and 4) they are stable across different hyperparameter settings. In hope of facilitating and democratizing research focused on intention detection, we release our code, as well as a new challenging single-domain intent detection dataset comprising 13,083 annotated examples over 77 intents.
Sensitive Data Detection and Classification in Spanish Clinical Text: Experiments with BERT

Massive digital data processing provides a wide range of opportunities and benefits, but at the cost of endangering personal data privacy. Anonymisation consists in removing or replacing sensitive information from data, enabling its exploitation for different purposes while preserving the privacy of individuals. Over the years, a lot of automatic anonymisation systems have been proposed; however, depending on the type of data, the target language or the availability of training documents, the task remains challenging still. The emergence of novel deep-learning models during the last two years has brought large improvements to the state of the art in the field of Natural Language Processing. These advancements have been most noticeably led by BERT, a model proposed by Google in 2018, and the shared language models pre-trained on millions of documents. In this paper, we use a BERT-based sequence labelling model to conduct a series of anonymisation experiments on several clinical datasets in Spanish. We also compare BERT to other algorithms. The experiments show that a simple BERT-based model with general-domain pre-training obtains highly competitive results without any domain specific feature engineering.
Does label smoothing mitigate label noise?

Label smoothing is commonly used in training deep learning models, wherein one-hot training labels are mixed with uniform label vectors. Empirically, smoothing has been shown to improve both predictive performance and model calibration. In this paper, we study whether label smoothing is also effective as a means of coping with label noise. While label smoothing apparently amplifies this problem --- being equivalent to injecting symmetric noise to the labels --- we show how it relates to a general family of loss-correction techniques from the label noise literature. Building on this connection, we show that label smoothing is competitive with loss-correction under label noise. Further, we show that when distilling models from noisy data, label smoothing of the teacher is beneficial; this is in contrast to recent findings for noise-free problems, and sheds further light on settings where label smoothing is beneficial.
SAM: The Sensitivity of Attribution Methods to Hyperparameters

Attribution methods can provide powerful insights into the reasons for a classifier's decision. We argue that a key desideratum of an explanation method is its robustness to input hyperparameters which are often randomly set or empirically tuned. High sensitivity to arbitrary hyperparameter choices does not only impede reproducibility but also questions the correctness of an explanation and impairs the trust of end-users. In this paper, we provide a thorough empirical study on the sensitivity of existing attribution methods. We found an alarming trend that many methods are highly sensitive to changes in their common hyperparameters e.g. even changing a random seed can yield a different explanation! Interestingly, such sensitivity is not reflected in the average explanation accuracy scores over the dataset as commonly reported in the literature. In addition, explanations generated for robust classifiers (i.e. which are trained to be invariant to pixel-wise perturbations) are surprisingly more robust than those generated for regular classifiers.
SPATIAL MODELING OF MULTI-SCENARIO OPTIMAL SOLAR PV POWER PLANT DISTRIBUTION TO SUPPORT INDONESIA'S CLEAN ENERGY ACHIEVEMENT TARGETS

Abstract. The increasing population brings the increasing energy demand. The increasing production of fossil energy makes many gas emissions. This causes some effect like global warming. The production of clean energy concerns the world government. Solar energy has great attention from many countries worldwide, seeing the potential of the energy produced, the ease of installation process, and the small risk of damage. The potential of solar energy in Indonesia itself reaches 4.8 KWh/m2 or equivalent to 112,000 GWp. Currently, the Indonesian government has a target for constructing solar power plants in 2025 of 0.87 GW or around 50 MWp/year. The absence of research on determining the appropriate location based on multiple aspects is one of the obstacles in planning the construction of a solar PV power plant. Good planning is needed to determine the management and installation of an optimum and sustainable solar PV power plant. This research aims to develop an effective and efficient multi-scenario spatial model for the distribution of Solar PV (Photovoltaic) power plant development in Indonesia. The novelty in the study of the distribution of solar energy potential integrates meteorological and Geographic aspects and socio-economic aspects. The integration of dynamic multi-spatial data is used to determine the location of the development of solar power plants. Meteorological data is used to calculate potential energy, socio-economic data is used to determine the location for energy demand, and geographic aspect is used to know the suitable environment to install solar PV. The output of this research is the location of the priorities for the development of communal solar power plants in Indonesia. The distribution of effective Solar PV power plant development in Indonesia using a multi-scenario spatial model is divided into five suitability classes. The percentage of suitability class is 0.2% very low class, 3.5% low, 32.4% medium 56.9% high class, and very high 7%. The result is published in WebGIS that can access in link http://bit.ly/ModelPLTSIndonesia. It is hoped the results of this research can be used as material for consideration and one of the solutions for policymakers in making decisions regarding the development of communal solar power plants in Indonesia.
DEVELOPMENT OF GEOSPATIAL INFORMATION INTEGRATED WITH BIG DATA TO AGRICULTURAL HAZARD MONITORING IN WEST JAVA

Abstract. Food security is highly dependent on three aspects, namely food availability, food access, and food utilization. The availability aspect depends on food supply which is identical to agricultural productivity. West Java Province is the third national rice producer with 16.6%, but West Java Province is the most extensive rice consumer, around 21.1% of the total national rice consumption. Agricultural productivity can decline due to natural hazards such as floods and droughts. Monitoring floods and droughts in paddy fields are necessary to prevent decreased agricultural productivity. This study aims to monitor the rice fields from the dangers of flooding and drought every month. Agricultural hazard monitoring is divided into two parameters, namely static parameters and dynamic parameters. Dynamic parameters are observed every month so that the hazard index is generated on a monthly scale. GIS and Remote sensing data are integrated to perform agricultural hazard modelling. Furthermore, this agricultural hazard modelling results will be strengthened by using big to provide information about an almost real-time event that can be accessed through the Application Program Interface (API) service. This study uses a data mining system from Drone Emprit that performs data mining on Twitter and news portals with machine learning technology (probabilistic classifier) and Natural Learning Process. The results obtained are around 15,000 data from January 1 to November 1, 2021, and 37.9% of them are identified by location based on the city or district level in West Java Province. It is hoped that the policy-maker can consider the area of agricultural land that requires assistance to increase productivity and plan a policy to support agriculture in West Java in the future.
Path planning for fixed-wing UAVs based on expert knowledge and improved VFH in cluttered environments

Obstacle avoidance is a vital problem for flight safety. This paper proposes a fixed wing unmanned aerial vehicle (UAV) path planning algorithm for obstacle avoidance in cluttered environments. Two critical problems have been solved. One is the path planning, i.e., to detour the obstacles at a higher energy cost or to fly through the gaps between obstacles at a lower energy cost by evaluating the collision risk. The other is the optimization of smooth path and pose which is indispensable when the gaps are small. First, the UAV makes the decision of detouring or flying through gaps based on the safety threshold set by the algorithm and the density of the obstacles. Second, if the UAV decides to detour the obstacles, a path planning scheme is developed based on a Dubins-based improved vector field histogram (VFH) for collision avoidance satisfying the kinematic constraints of the fixed-wing UAV. If the UAV decides to fly through the gaps, a pose planner based on machine learning technique is developed, which integrates the human flight experience obtained from trials. The novelty of the proposed strategy is to make the human behavior understandable to machines and let the UAV mimic human behavior in different environments with good adaptability so that the complex nonlinear control and path planning problem can be solved efficiently. Finally, a human-machine interactive simulator is built up to verify the effectiveness of the proposed strategy, and the results show the stable performance in simulations.
THE EFFECT OF USING CLUSTERING TECHNIQUE ON THE STUDENTS’ WRITING SKILLS IN DESCRIPTIVE TEXT AT SENIOR HIGH SCHOOL 10 MAKASSAR

This research focuses on the students’ writing skills in writing descriptive text. The purpose of this research were (1) To find out if the clustering technique is able to improve the students skills in writing descriptive text. (2) To find out to what extent clustering technique affects the development of the students skills in writing descriptive text. In this research, the population was the First Grade of Senior High School 10 Makassar consist of 15 students from X IIS 1. The method of this study was a Mixed Method, the design of this study was The Explanatory-Sequential (QUAN-qual) design where quantitative data were collected first. Pre-experimental method was applied in this research with a group pre-test and post-test design. The instrument used to collect data were writing test and questionnaire. The result of the research presented that the pre-test of the class got the mean score 40.33. After giving treatment the students got the the mean score 71.00. The outcome of the data analysis revealed that there was a significant improvement in the students’ skill in writing descriptive text after being taught using clustering technique. It can also be supported the strength of clustering technique, that is the students can generate their ideas in writing descriptive text. Subsequently, the result of one sample t-test where Sig. (2-tailed) = 0,000 &lt; 0.05. Therefore, the null (Ho) is rejected and the alternative hypothesis (H1) is accepted. In addition, the qualitative result from the questionnaire showed there were 4 factors affecting the development of students’ writing skill in descriptive text, those are: teaching materials’ content, method in the teaching process, activities in the teaching materials, task given in teaching materials. Based on the result analysis, the researcher may conclude that using Clustering Technique was effective to improve the students’ skill in writing descriptive text because the learning objective was achieved, that was the students were able to write English descriptive text appropriately in English by following the generic structure and language features.