Reviewerly/exHarmony huggingface dataset:
Automated Statement Extraction from Press Briefings

Scientific press briefings are a valuable information source. They consist of alternating expert speeches, questions from the audience and their answers. Therefore, they can contribute to scientific and fact-based media coverage. Even though press briefings are highly informative, extracting statements relevant to individual journalistic tasks is challenging and time-consuming. To support this task, an automated statement extraction system is proposed. Claims are used as the main feature to identify statements in press briefing transcripts. The statement extraction task is formulated as a four-step procedure. First, the press briefings are split into sentences and passages, then claim sentences are identified through sequence classification. Subsequently, topics are detected, and the sentences are filtered to improve the coherence and assess the length of the statements. The results indicate that claim detection can be used to identify statements in press briefings. While many statements can be extracted automatically with this system, they are not always as coherent as needed to be understood without context and may need further review by knowledgeable persons.
Scaling Up Computer Vision Neural Networks Using Fast Fourier Transform

Deep Learning-based Computer Vision field has recently been trying to explore larger kernels for convolution to effectively scale up Convolutional Neural Networks. Simultaneously, new paradigm of models such as Vision Transformers find it difficult to scale up to larger higher resolution images due to their quadratic complexity in terms of input sequence. In this report, Fast Fourier Transform is utilised in various ways to provide some solutions to these issues.
Improving Adaptive Conformal Prediction Using Self-Supervised Learning

Conformal prediction is a powerful distribution-free tool for uncertainty quantification, establishing valid prediction intervals with finite-sample guarantees. To produce valid intervals which are also adaptive to the difficulty of each instance, a common approach is to compute normalized nonconformity scores on a separate calibration set. Self-supervised learning has been effectively utilized in many domains to learn general representations for downstream predictors. However, the use of self-supervision beyond model pretraining and representation learning has been largely unexplored. In this work, we investigate how self-supervised pretext tasks can improve the quality of the conformal regressors, specifically by improving the adaptability of conformal intervals. We train an auxiliary model with a self-supervised pretext task on top of an existing predictive model and use the self-supervised error as an additional feature to estimate nonconformity scores. We empirically demonstrate the benefit of the additional information using both synthetic and real data on the efficiency (width), deficit, and excess of conformal prediction intervals.
Simple and Scalable Nearest Neighbor Machine Translation

$k$NN-MT is a straightforward yet powerful approach for fast domain adaptation, which directly plugs pre-trained neural machine translation (NMT) models with domain-specific token-level $k$-nearest-neighbor ($k$NN) retrieval to achieve domain adaptation without retraining. Despite being conceptually attractive, $k$NN-MT is burdened with massive storage requirements and high computational complexity since it conducts nearest neighbor searches over the entire reference corpus. In this paper, we propose a simple and scalable nearest neighbor machine translation framework to drastically promote the decoding and storage efficiency of $k$NN-based models while maintaining the translation performance. To this end, we dynamically construct an extremely small datastore for each input via sentence-level retrieval to avoid searching the entire datastore in vanilla $k$NN-MT, based on which we further introduce a distance-aware adapter to adaptively incorporate the $k$NN retrieval results into the pre-trained NMT models. Experiments on machine translation in two general settings, static domain adaptation and online learning, demonstrate that our proposed approach not only achieves almost 90% speed as the NMT model without performance degradation, but also significantly reduces the storage requirements of $k$NN-MT.
HL Dataset: Visually-grounded Description of Scenes, Actions and Rationales

Current captioning datasets focus on object-centric captions, describing the visible objects in the image, e.g. "people eating food in a park". Although these datasets are useful to evaluate the ability of Vision & Language models to recognize and describe visual content, they do not support controlled experiments involving model testing or fine-tuning, with more high-level captions, which humans find easy and natural to produce. For example, people often describe images based on the type of scene they depict ('people at a holiday resort') and the actions they perform ('people having a picnic'). Such descriptions draw on personal experience and commonsense assumptions. We present the High-Level Dataset a dataset extending 14997 images from the COCO dataset, aligned with a new set of 134,973 human-annotated (high-level) captions collected along three axes: scenes, actions, and rationales. We further extend this dataset with confidence scores collected from an independent set of readers, as well as a set of narrative captions generated synthetically, by combining each of the three axes. We describe this dataset and analyse it extensively. We also present baseline results for the High-Level Captioning task.
Phase diagram of early training dynamics in deep neural networks: effect of the learning rate, depth, and width

We systematically analyze optimization dynamics in deep neural networks (DNNs) trained with stochastic gradient descent (SGD) and study the effect of learning rate $\eta$, depth $d$, and width $w$ of the neural network. By analyzing the maximum eigenvalue $\lambda^H_t$ of the Hessian of the loss, which is a measure of sharpness of the loss landscape, we find that the dynamics can show four distinct regimes: (i) an early time transient regime, (ii) an intermediate saturation regime, (iii) a progressive sharpening regime, and (iv) a late time ``edge of stability" regime. The early and intermediate regimes (i) and (ii) exhibit a rich phase diagram depending on $\eta \equiv c / \lambda_0^H $, $d$, and $w$. We identify several critical values of $c$, which separate qualitatively distinct phenomena in the early time dynamics of training loss and sharpness. Notably, we discover the opening up of a ``sharpness reduction" phase, where sharpness decreases at early times, as $d$ and $1/w$ are increased.
Learning Neural Volumetric Representations of Dynamic Humans in Minutes

This paper addresses the challenge of quickly reconstructing free-viewpoint videos of dynamic humans from sparse multi-view videos. Some recent works represent the dynamic human as a canonical neural radiance field (NeRF) and a motion field, which are learned from videos through differentiable rendering. But the per-scene optimization generally requires hours. Other generalizable NeRF models leverage learned prior from datasets and reduce the optimization time by only finetuning on new scenes at the cost of visual fidelity. In this paper, we propose a novel method for learning neural volumetric videos of dynamic humans from sparse view videos in minutes with competitive visual quality. Specifically, we define a novel part-based voxelized human representation to better distribute the representational power of the network to different human parts. Furthermore, we propose a novel 2D motion parameterization scheme to increase the convergence rate of deformation field learning. Experiments demonstrate that our model can be learned 100 times faster than prior per-scene optimization methods while being competitive in the rendering quality. Training our model on a $512 \times 512$ video with 100 frames typically takes about 5 minutes on a single RTX 3090 GPU. The code will be released on our project page: https://zju3dv.github.io/instant_nvr
Do Machine Learning Models Produce TypeScript Types That Type Check?

Type migration is the process of adding types to untyped code to gain assurance at compile time. TypeScript and other gradual type systems facilitate type migration by allowing programmers to start with imprecise types and gradually strengthen them. However, adding types is a manual effort and several migrations on large, industry codebases have been reported to have taken several years. In the research community, there has been significant interest in using machine learning to automate TypeScript type migration. Existing machine learning models report a high degree of accuracy in predicting individual TypeScript type annotations. However, in this paper we argue that accuracy can be misleading, and we should address a different question: can an automatic type migration tool produce code that passes the TypeScript type checker? We present TypeWeaver, a TypeScript type migration tool that can be used with an arbitrary type prediction model. We evaluate TypeWeaver with three models from the literature: DeepTyper, a recurrent neural network; LambdaNet, a graph neural network; and InCoder, a general-purpose, multi-language transformer that supports fill-in-the-middle tasks. Our tool automates several steps that are necessary for using a type prediction model, (1) importing types for a project's dependencies; (2) migrating JavaScript modules to TypeScript notation; (3) inserting predicted type annotations into the program to produce TypeScript when needed; and (4) rejecting non-type predictions when needed. We evaluate TypeWeaver on a dataset of 513 JavaScript packages, including packages that have never been typed before. With the best type prediction model, we find that only 21% of packages type check, but more encouragingly, 69% of files type check successfully.
Set Features for Fine-grained Anomaly Detection

Fine-grained anomaly detection has recently been dominated by segmentation based approaches. These approaches first classify each element of the sample (e.g., image patch) as normal or anomalous and then classify the entire sample as anomalous if it contains anomalous elements. However, such approaches do not extend to scenarios where the anomalies are expressed by an unusual combination of normal elements. In this paper, we overcome this limitation by proposing set features that model each sample by the distribution its elements. We compute the anomaly score of each sample using a simple density estimation method. Our simple-to-implement approach outperforms the state-of-the-art in image-level logical anomaly detection (+3.4%) and sequence-level time-series anomaly detection (+2.4%).
An Explicit Expansion of the Kullback-Leibler Divergence along its Fisher-Rao Gradient Flow

Let $V_* : \mathbb{R}^d \to \mathbb{R}$ be some (possibly non-convex) potential function, and consider the probability measure $\pi \propto e^{-V_*}$. When $\pi$ exhibits multiple modes, it is known that sampling techniques based on Wasserstein gradient flows of the Kullback-Leibler (KL) divergence (e.g. Langevin Monte Carlo) suffer poorly in the rate of convergence, where the dynamics are unable to easily traverse between modes. In stark contrast, the work of Lu et al. (2019; 2022) has shown that the gradient flow of the KL with respect to the Fisher-Rao (FR) geometry exhibits a convergence rate to $\pi$ is that \textit{independent} of the potential function. In this short note, we complement these existing results in the literature by providing an explicit expansion of $\text{KL}(\rho_t^{\text{FR}}\|\pi)$ in terms of $e^{-t}$, where $(\rho_t^{\text{FR}})_{t\geq 0}$ is the FR gradient flow of the KL divergence. In turn, we are able to provide a clean asymptotic convergence rate, where the burn-in time is guaranteed to be finite. Our proof is based on observing a similarity between FR gradient flows and simulated annealing with linear scaling, and facts about cumulant generating functions. We conclude with simple synthetic experiments that demonstrate our theoretical findings are indeed tight. Based on our numerics, we conjecture that the asymptotic rates of convergence for Wasserstein-Fisher-Rao gradient flows are possibly related to this expansion in some cases.
Application of Bayesian Network in Analysis and Management of Agricultural Water - Taking Kinmen for Example

By calculating the water demand and programming a fine irrigation project, the management and cultivating efficiency of traditional agriculture can be greatly improved. Taking rotational irrigation for example, the efficiency of irrigation can be maximized by adjusting water distribution routes, irrigation area allocation, and irrigation schedule planning. However, in actual operation, some problems are often encountered, such as how to persuade farmers and promote the designed irrigation project, and the negotiation of various stakeholders. Generally, due to the complexity of the irrigation design model, it is impossible to have an effective and immediate communication or presentation. Therefore, this study introduces the Bayesian network to presents the key points of the irrigation project after simplifying the relationship. In addition to being simpler for stakeholders to understand, it is also possible to adjust various parameters in time to obtain rough estimation results.The research area of this study is a 100-hectare farmland, which is located in Kinmen County, Taiwan. For many years, local farmers have only relied on precipitation to cultivate sorghum, wheat and other crops. However, the precipitation in Kinmen is semiarid and unstable. In the past five years, the annual rainfall has been lower than the average in previous years, which directly led to a very bleak crop harvest. Therefore, we hope to establish an irrigation project in Kinmen, using recycled water as the water source to provide local farmers with a reliable water source.The Bayesian network used in this study is a directed acyclic graphical (DAG) model based on conditional probability and Bayesian theorem to express the possible relationship between variables. In terms of operation, the different influencing factors in the research topic are converted into nodes, and the relationship between nodes is given by different conditional probabilities. This study uses GeNIe to establish a Bayesian network that can be used to estimate water profit and loss and other results. This Bayesian network can be divided into four sub-blocks, which are the relevant data of the irrigation area, the water demand, the water supply, and the final result calculation. Therefore, when the stakeholders are negotiating the irrigation project, they can discuss the different estimation results by adjusting each node of the first three sub-blocks.
Cross disciplinary collaboration for societal benefit across Australian Research Infrastructure Networks

Earth systems including the geosphere, biosphere, cryosphere, hydrosphere and atmosphere complexly interact to create a planet on which life and humanity has thrived. Throughout history we have studied and observed all these systems, but too often there has been too little strategic integration of activities supporting observing and monitoring, storage and access to data, computing and modelling, and predicting future states.&amp;#160;This has reduced our ability to quickly advance our understanding of the interdependence of these complex systems, particularly as data has often been collected in isolation, or to address a very specific research problem. The challenges to achieving such an integrated perspective are scientific, technical, social, political and organisational.In order to address this issue the Earth and environment related research infrastructure facilities in Australia have self-organised to create the National Earth and Environmental Sciences Facilities Forum (NEESFF).&amp;#160; NEESFF is intended to harness the capacity of its environmentally-focused capabilities to collectively create solutions and deliver the information needed for sustainable development and use of environmental resources.&amp;#160; NEESFF&amp;#8217;s vision is an effective and coordinated response to global environmental conditions in a uniquely Australian context.&amp;#160;Many NEESFF organisations are funded through the National Research Infrastructure Infrastructure Strategy (NCRIS) which supports research activity across many STEM and HASS disciplines.&amp;#160; The Australian Government recently developed a Roadmap outlining the Challenges, opportunities for system enhancement and potential step-changes that the next phase of research infrastructure investment will drive.&amp;#160; These too are guided by national and global science and societal challenges and rely on underpinning robust and integrated FAIR open data systems across the RI network.Here we will outline the national approach to these challenges, the current thinking on the data challenges that we face in an Australian context and the first steps we are taking as a community to develop a framework for the delivery of integrated Earth data.Members are:&amp;#160; Atlas of Living Australia (ALA); AuScope; Australian Urban Research Infrastructure Network (AURIN); Australian Research Data Commons; Australian Plant Phenomics Facility (APPF); BioPlatforms Australia; Geoscience Australia (GA); Integrated Marine Observing System (IMOS); Australian Terrestrial Ecosystem Research Network (TERN); Marine National Facility; Bureau of Meteorology; E2SIP (CSIRO); National Computational Infrastructure; and AARNet.
PENERAPAN MODEL PEMBELAJARAN BASED LEARNING (PBL) UNTUK MENINGKATKAN PEMAHAMAN SISWA KELAS IV PADA MATERI PENJUMLAHAN PECAHAN DI SEKOLAH DASAR NEGERI 3 CILEULEUY

This research was motivated by the low mathematical understanding of the fourth grade students of SDN 3 Cileuleuy on the matter of adding fractions. Based on the results of observations, only 3 students out of 11 students obtained test scores above/equal to the KKM 70. So this study aims to improve students' mathematical understanding of fraction addition material in class IV SDN 3 Cileuleuy through the use of Model Pembelajaran Based Learning (PBL) models. The research method used is Classroom Action Research (CAR). The research design consisted of 2 cycles where the research subjects were 11 students of class IV SDN 3 Cileuleuy consisting of 8 male students and 3 female students. Data collection techniques were carried out through tests (evaluation tests) and non-tests (observations and documentation). The data analysis technique used is descriptive quantitative to analyze the results of evaluation tests and qualitative descriptive analysis to analyze the results of student evaluations. The results showed that there was an increase in students' mathematical understanding from before giving the action until the second cycle. Individual completeness before giving the action is 3 students with classical completeness reaching 27% and the average value obtained is 63.18. Then in the first cycle, 7 students obtained individual mastery with an average value of 70.27 and classical learning completeness of 63%. In the second cycle, individual mastery increased to 10 students with an average value of 81.89 and classical learning completeness increased to 85%. Based on the results obtained, it can be concluded that the use of the Problem Based Learning (PBL) model can improve students' mathematical understanding of ordinary fractions in the fifth grade of SDN 3 Cileuleuy in the 2022/2023 academic year.&#x0D;
A Google Earth Engine (GEE) system/tool for the monitoring of active volcanoes at a global scale

The NHI (Normalized Hotspot Indices) system performs the automated monitoring of volcanic thermal anomalies at global scale under the Google Earth Engine (GEE) platform, by integrating information from Sentinel-2 MSI and Landsat 8/9 OLI/OLI-2 data. Thermal anomalies flagged by the NHI system may be then investigated through the tool. The latter enables the analysis of volcanic thermal features in terms of hot spot pixels, total SWIR (short wave infrared) radiance and total hotspot area, with low processing times. In this study, we present some recent results of the active volcanoes investigation performed using the tool, starting from the automated NHI detections. Results show that the NHI system/tool may provide a relevant contribution to the monitoring of thermal volcanic activity in both remote and well-monitored areas, thanks to the capacity in detecting and mapping hot targets with a low false positive rate.
Peeking Inside Hydrologists' Minds: Comparing Human Judgment and Quantitative Metrics of Hydrographs

Everyone wants their hydrologic models to be as good as possible. But how do we know if a model is accurate or not? In the spirit of rigorous and reproducible science, the answer should be: we calculate metrics. Yet, as humans, we sometimes follow a scheme of "I know a good model when I see it" and manually inspect hydrographs to assess their quality. This is certainly a valid method for sanity checks, but it is unclear whether these subjective visual ratings agree with metric-based rankings. Moreover, the consistency of such inspections is unclear, as different observers might come to different conclusions about the same hydrographs.In this presentation, we report a large-scale study where we collected responses from 622 experts, who compared and judged more than 14,000 pairs of hydrographs from 13 different models. Our results show that overall, human ratings broadly agree with quantitative metrics in a clear preference for a Machine Learning model. At the level of individuals, however, there is a large amount of inconsistency between ratings from different participants. Still, in cases where experts agree, we can predict their most likely rating purely from qualitative metrics. This indicates that we can encode intersubjective human preferences with a small set of objective, quantitative metrics. To us, these results make a compelling case for the community to put more trust into existing metrics&amp;#8212;for example, by conducting more rigorous benchmarking efforts.
ANALISIS DAMPAK KELUARGA TERHADAP KESULITAN BELAJAR SISWA PADA MATA PEMBELAJARAN KEWARGANEGARAAN DI MAN 1 MEDAN

The purpose of this study was to see the effect of the family environment on students' learning Citizenship difficulties in each subject of MAN 1 Medan. The subjects of this study were several students, totaling 100 students, while the subjects of this study were the influence of the family environment on students' learning difficulties in the subjects of MAN 1 Medan. Data collection techniques used by the author are questionnaires and documentation. Data analysis was performed using a quantitative approach with linear regression analysis. Based on the research results, it is known that the family environment influences the learning difficulties of students in financial subjects at Madrasah Aliyah Medan City. Based on the calculation results, it is obtained that the coefficient of R squared = 0.533 = 53.3%, meaning that the family environment variable affects the student's learning difficulty variable by 53.3%, the remaining 46.7% is accounted for by other variables. which have . not included. in this research.&#x0D;