Miracle-dz/UniFAR-FTUs huggingface dataset:
Event based sentence level interpretation of sentiment variation in twitter data

Twitter is one of the most popular micro blogging sites used by people to express their opinions. Text mining is the area where automatically data is mined for extracting features etc for different purposes. Interpretation of public opinion in micro blogging site is a challenging problem since it has noise data and other unnecessary tweets. The current systems focus on removing these challenges along with the sentiment extraction and modeling. Also the existing system focus on topic related extraction. We move ahead to the sentence level extraction with the help of existing methods. In this paper we propose a combination of enhanced RCB-LDA method, NLP, event based analysis and text summarization. RCB-LDA is used to automatically extract the sentiments within a variation period. NLP is used for finding the meaning of sentiments in the tweets. Event based analysis analyzes the sentiment related to each other by using text summarization. Event based analysis group the sentiment together to relate each other by summarizing tweets. Finally a candidate is assigned to which related ones are combined together so that it will be the most important reason behind the sentiment variation.
Precise glucose measurement in sodium ﬂ uoride-citrate plasma a ﬀ ects estimates of prevalence in diabetes and prediabetes

Objectives: Estimates of glucose concentrations vary among types of blood samples, which impact on the assessment of diabetes prevalence. Guidelines recommend a conversion factor to calculate plasma glucose from measurements of glucose in whole blood. The American Diabetes Association recommends the use of blood drawing tubes containing sodium ﬂ uoride (NaF) and citrate, which have not yet been evaluated regarding possible di ﬀ erences in glucose concentration and conversion factors. Thus, we compared glucose measurements in NaF-citrate plasma and venous whole blood and estimated the impact of differences on diabetes and prediabetes prevalence. Methods: Glucose di ﬀ erences were calculated by Bland-Altman analysis with pairwise comparison of glucose measurements from whole blood and NaF-citrate plasma (n=578) in clinical studies of the German Diabetes Center. Subse-quently, we computed the impact of the glucose di ﬀ erence on diabetes and prediabetes prevalence in the population-based National Health and Nutrition Examination Survey (NHANES). Results: Even upon conversion of whole blood to plasma glucose concentrations using the recommended conversion factor, mean glucose concentration di ﬀ erence remained 4.72 % higher in NaF-citrate plasma. Applying the higher glucose estimates, increases the population-based diabetes and prediabetes prevalence by 13.67 and 33.97 % or more than 7.2 and 13 million people in NHANES, respectively. Additional economic burden could be about 20 $ billion per year due to undiagnosed diabetes. Conclusions: The recommended conversion factor is not valid for NaF-citrate plasma. Systematic bias of glucose measurements due to sampling type leads to clinically relevant higher estimates of diabetes and prediabetes prevalence.
Mobile-Assisted Localization by Stitching in Wireless Sensor Networks

In most mobile-assisted localization schemes, mobile anchors follow predefined or random trajectories to send beacon messages around. Although working well in regular and uniform networks, predefined or random trajectories lead to wasted efforts in irregular and non-uniform deployment. We propose a novel scheme of trajectory design by utilizing the topology information of the network. Based on rigidity theory, the original network is first partitioned into localization units as localizable subnets or orphan nodes. The shortest path traversing all localization units is then chosen as the trajectory of the mobile anchor. Simulations show that the new scheme avoids unnecessary movements in blank areas and travels at much shorter path than the classic SCAN scheme.
Accelerating MCMC with Parallel Predictive Prefetching

Parallel predictive prefetching is a new framework for accelerating a large class of widely-used Markov chain Monte Carlo (MCMC) algorithms. It speculatively evaluates many potential steps of an MCMC chain in parallel while exploiting fast, iterative approximations to the target density. This can accelerate sampling from target distributions in Bayesian inference problems. Our approach takes advantage of whatever parallel resources are available, but produces results exactly equivalent to standard serial execution. In the initial burn-in phase of chain evaluation, we achieve speedup close to linear in the number of available cores.
The Open Journal of Occupational Therapy The Open Journal of Occupational Therapy

Abstract This paper presents an historical analysis of occupational therapy’s role in hospice care with relation to past and current hospice practices, as well as cultural forces that impact that role. Since the beginning of the movement, hospice has developed into a strong component of end-of-life care, and occupational therapy practice models and interventions are unique in addressing the occupational needs of clients during this stage of life. Despite compelling evidence of the positive impact of employing occupational therapists, there continue to be significant barriers to implementation of services. The author proposes that the concept of occupation, as experienced at the end-of-life stage, needs to be more clearly defined and occupational therapy’s role broadened in order to strengthen the profession’s presence in the hospice setting.
Designing Technologies for Community Policing

Designing Technologies for Community Policing Conference or Workshop Item How to cite: Zhang, Min; Bandara, Arosha; Price, Blaine; Pike, Graham; Walkington, Zoe; Elphick, Camilla; Frumkin, Lara; Philpot, Richard; Levine, Mark; Stuart, Avelie and Nuseibeh, Bashar (2020). Designing Technologies for Community Policing. In: CHI EA ’20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems pp. 1–9.
Shaping carbon nanostructures by controlling the synthesis process

The ability to control the nanoscale shape of nanostructures in a large-scale synthesis process is an essential and elusive goal of nanotechnology research. Here, we report significant progress toward that goal. We have developed a technique that enables controlled synthesis of nanoscale carbon structures with conical and cylinder-on-cone shapes and provides the capability to dynamically change the nanostructure shape during the synthesis process. In addition, we present a phenomenological model that explains the formation of these nanostructures and provides insight into methods for precisely engineering their shape. Since the growth process we report is highly deterministic in allowing large-scale synthesis of precisely engineered nanoscale components at defined locations, our approach provides an important tool for a practical nanotechnology.
Using Large Language Models for OntoClean-based Ontology Refinement

This paper explores the integration of Large Language Models (LLMs) such as GPT-3.5 and GPT-4 into the ontology refinement process, specifically focusing on the OntoClean methodology. OntoClean, critical for assessing the metaphysical quality of ontologies, involves a two-step process of assigning meta-properties to classes and verifying a set of constraints. Manually conducting the first step proves difficult in practice, due to the need for philosophical expertise and lack of consensus among ontologists. By employing LLMs with two prompting strategies, the study demonstrates that high accuracy in the labelling process can be achieved. The findings suggest the potential for LLMs to enhance ontology refinement, proposing the development of plugin software for ontology tools to facilitate this integration.
Many-hypercube codes: High-rate quantum error-correcting codes for high-performance fault-tolerant quantum computation

Conventional approaches to quantum error correction for fault-tolerant quantum computation are based on encoding a single logical qubit into many physical qubits, resulting in asymptotically zero encoding rates and therefore huge resource overheads. To overcome this issue, high-rate quantum codes, such as quantum low-density parity-check codes, have been studied over the past decade. However, such codes have complex structure, making it difficult to perform logical gate operations in parallel without sacrificing their advantage. Observing the simple structure and high rates of quantum error-detecting codes, here we propose concatenated high-rate quantum error-detecting codes as a new family of high-rate quantum codes. Their simple structure allows for a geometrical interpretation using hypercubes, each of which corresponds to a logical qubit. We thus call them many-hypercube codes. The encoding rate is remarkably high, e.g., 30% (64 logical qubits are encoded into 216 physical qubits). Developing a dedicated high-performance decoder, we achieve high error thresholds even in a circuit-level noise model. Logical gate operations are also parallelizable. Thus, the many-hypercube codes will pave the way to high-performance fault-tolerant quantum computation.
Multi-Scale Texture Loss for CT denoising with GANs

Generative Adversarial Networks (GANs) have proved as a powerful framework for denoising applications in medical imaging. However, GAN-based denoising algorithms still suffer from limitations in capturing complex relationships within the images. In this regard, the loss function plays a crucial role in guiding the image generation process, encompassing how much a synthetic image differs from a real image. To grasp highly complex and non-linear textural relationships in the training process, this work presents a loss function that leverages the intrinsic multi-scale nature of the Gray-Level-Co-occurrence Matrix (GLCM). Although the recent advances in deep learning have demonstrated superior performance in classification and detection tasks, we hypothesize that its information content can be valuable when integrated into GANs' training. To this end, we propose a differentiable implementation of the GLCM suited for gradient-based optimization. Our approach also introduces a self-attention layer that dynamically aggregates the multi-scale texture information extracted from the images. We validate our approach by carrying out extensive experiments in the context of low-dose CT denoising, a challenging application that aims to enhance the quality of noisy CT scans. We utilize three publicly available datasets, including one simulated and two real datasets. The results are promising as compared to other well-established loss functions, being also consistent across three different GAN architectures. The code is available at: https://github.com/FrancescoDiFeola/DenoTextureLoss
Transferring BCI models from calibration to control: Observing shifts in EEG features

Public Motor Imagery-based brain-computer interface (BCI) datasets are being used to develop increasingly good classifiers. However, they usually follow discrete paradigms where participants perform Motor Imagery at regularly timed intervals. It is often unclear what changes may happen in the EEG patterns when users attempt to perform a control task with such a BCI. This may lead to generalisation errors. We demonstrate a new paradigm containing a standard calibration session and a novel BCI control session based on EMG. This allows us to observe similarities in sensorimotor rhythms, and observe the additional preparation effects introduced by the control paradigm. In the Movement Related Cortical Potentials we found large differences between the calibration and control sessions. We demonstrate a CSP-based Machine Learning model trained on the calibration data that can make surprisingly good predictions on the BCI-controlled driving data.
On the Variational Interpretation of Mirror Play in Monotone Games

Mirror play (MP) is a well-accepted primal-dual multi-agent learning algorithm where all agents simultaneously implement mirror descent in a distributed fashion. The advantage of MP over vanilla gradient play lies in its usage of mirror maps that better exploit the geometry of decision domains. Despite extensive literature dedicated to the asymptotic convergence of MP to equilibrium, the understanding of the finite-time behavior of MP before reaching equilibrium is still rudimentary. To facilitate the study of MP's non-equilibrium performance, this work establishes an equivalence between MP's finite-time primal-dual path (mirror path) in monotone games and the closed-loop Nash equilibrium path of a finite-horizon differential game, referred to as mirror differential game (MDG). Our construction of MDG rests on the Brezis-Ekeland variational principle, and the stage cost functional for MDG is Fenchel coupling between MP's iterates and associated gradient updates. The variational interpretation of mirror path in static games as the equilibrium path in MDG holds in deterministic and stochastic cases. Such a variational interpretation translates the non-equilibrium studies of learning dynamics into a more tractable equilibrium analysis of dynamic games, as demonstrated in a case study on the Cournot game, where MP dynamics corresponds to a linear quadratic game.
Tracking development from early speech-language acquisition to reading skills at age 13

Objective: Previous studies have indicated a link between speech-language and literacy development. To add to this body of knowledge, we investigated whether lexical and grammatical skills from toddler to early school age are related to reading competence in adolescence. Methods: Twenty-three typically developing children were followed from age 1;6 to 13;6 (years;months). Parental checklists and standardized tests were used to assess the development of mental lexicon, grammatical and reading capacities of the children. Results: Direct assessment of early speech-language functions positively correlated with later reading competence, whereas lexical skills reported by parents were not associated with this capacity. At (pre-) school age, larger vocabulary and better grammatical abilities predicted advanced reading abilities in adolescence. Conclusion: Our study contributes to the understanding of typical speech-language development and its relation to later reading outcome, extending the body of knowledge on these developmental domains for future early identification of children at risk for reading difficulties.
Team Coordination on Graphs: Problem, Analysis, and Algorithms

Team Coordination on Graphs with Risky Edges (TCGRE) is a recently emerged problem, in which a robot team collectively reduces graph traversal cost through support from one robot to another when the latter traverses a risky edge. Resembling the traditional Multi-Agent Path Finding (MAPF) problem, both classical and learning-based methods have been proposed to solve TCGRE, however, they lacked either computation efficiency or optimality assurance. In this paper, we reformulate TCGRE as a constrained optimization and perform rigorous mathematical analysis. Our theoretical analysis shows the NP-hardness of TCGRE by reduction from the Maximum 3D Matching problem and that efficient decomposition is a key to tackle this combinatorial optimization problem. Further more, we design three classes of algorithms to solve TCGRE, i.e., Joint State Graph (JSG) based, coordination based, and receding-horizon sub-team based solutions. Each of these proposed algorithms enjoy different provable optimality and efficiency characteristics that are demonstrated in our extensive experiments.
"We Have No Idea How Models will Behave in Production until Production": How Engineers Operationalize Machine Learning

Organizations rely on machine learning engineers (MLEs) to deploy models and maintain ML pipelines in production. Due to models' extensive reliance on fresh data, the operationalization of machine learning, or MLOps, requires MLEs to have proficiency in data science and engineering. When considered holistically, the job seems staggering -- how do MLEs do MLOps, and what are their unaddressed challenges? To address these questions, we conducted semi-structured ethnographic interviews with 18 MLEs working on various applications, including chatbots, autonomous vehicles, and finance. We find that MLEs engage in a workflow of (i) data preparation, (ii) experimentation, (iii) evaluation throughout a multi-staged deployment, and (iv) continual monitoring and response. Throughout this workflow, MLEs collaborate extensively with data scientists, product stakeholders, and one another, supplementing routine verbal exchanges with communication tools ranging from Slack to organization-wide ticketing and reporting systems. We introduce the 3Vs of MLOps: velocity, visibility, and versioning -- three virtues of successful ML deployments that MLEs learn to balance and grow as they mature. Finally, we discuss design implications and opportunities for future work.
Detecting Moving Shadows : Formulation , Algorithms and Evaluation

Moving shadows need careful consideration in the development of robust dynamic scene analysis systems. Moving shadow detection is critical for accurate object detection in video streams, since shadow points are often misclassified as object points causing errors in segmentation and tracking. Many algorithms have been proposed in the literature that deal with shadows. However, a comparative evaluation of the existing approaches is still lacking. In this paper, we present a comprehensive survey of moving shadow detection approaches. We organize contributions reported in the literature in four classes, two of them are statistical and two are deterministic. We also present a comparative empirical evaluation of representative algorithms selected from these four classes. Novel quantitative (detection and discrimination rate) and qualitative metrics (scene and object independence, flexibility to shadow situations and robustness to noise) are proposed to evaluate these classes of algorithms on a benchmark suite of indoor and outdoor video sequences. These video sequences and associated “ground-truth” data are made available at http://cvrr.ucsd. edu/aton/shadow to allow for others in the community to experiment with new algorithms and metrics.
Terrain-Attentive Learning for Efficient 6-DoF Kinodynamic Modeling on Vertically Challenging Terrain

Wheeled robots have recently demonstrated superior mechanical capability to traverse vertically challenging terrain (e.g., extremely rugged boulders comparable in size to the vehicles themselves). Negotiating such terrain introduces significant variations of vehicle pose in all six Degrees-of-Freedom (DoFs), leading to imbalanced contact forces, varying momentum, and chassis deformation due to non-rigid tires and suspensions. To autonomously navigate on vertically challenging terrain, all these factors need to be efficiently reasoned within limited onboard computation and strict real-time constraints. In this paper, we propose a 6-DoF kinodynamics learning approach that is attentive only to the specific underlying terrain critical to the current vehicle-terrain interaction, so that it can be efficiently queried in real-time motion planners onboard small robots. Physical experiment results show our Terrain-Attentive Learning demonstrates on average 51.1% reduction in model prediction error among all 6 DoFs compared to a state-of-the-art model for vertically challenging terrain.
Understanding the Impact of Referent Design on Scale Perception in Immersive Data Visualization

Referents are often used to enhance scale perception in immersive visualizations. Common referent designs include the considerations of referent layout (side-by-side vs. in-situ) and referent size (small vs. medium vs. large). This paper introduces a controlled user study to assess how different referent designs affect the efficiency and accuracy of scale perception across different data scales, on the performance of the size-matching task in the virtual environment. Our results reveal that in-situ layouts significantly enhance accuracy and confidence across various data scales, particularly with large referents. Linear regression analyses further confirm that in-situ layouts exhibit greater resilience to changes in data scale. For tasks requiring efficiency, medium-sized referents emerge as the preferred choice. Based on these findings, we offer design guidelines for selecting referent layouts and sizes in immersive visualizations.
A Linear Time-Delay Scheme to Propagate Reduced Electron Density Matrices

For any linear system where the unreduced dynamics are governed by unitary propagators, we derive a closed, time-delayed, linear system for a reduced-dimensional quantity of interest. We apply this method to understand the memory-dependence of reduced $1$-electron density matrices in time-dependent configuration interaction (TDCI), a scheme to solve for the correlated dynamics of electrons in molecules. Though time-dependent density functional theory has established that the reduced $1$-electron density possesses memory-dependence, the precise nature of this memory-dependence has not been understood. We derive a self-contained, symmetry/constraint-preserving method to propagate reduced TDCI electron density matrices. In numerical tests on two model systems (H$_2$ and HeH$^+$), we show that with sufficiently large time-delay (or memory-dependence), our method propagates reduced TDCI density matrices with high quantitative accuracy. We study the dependence of our results on time step and basis set. To derive our method, we calculate the $4$-index tensor that relates reduced and full TDCI density matrices. Our calculation applies to any TDCI system, regardless of basis set, number of electrons, or choice of Slater determinants in the wave function. This calculation enables a proof that the trace of the reduced TDCI density matrix is constant and equals the number of electrons.
Text-IF: Leveraging Semantic Text Guidance for Degradation-Aware and Interactive Image Fusion

Image fusion aims to combine information from different source images to create a comprehensively representative image. Existing fusion methods are typically helpless in dealing with degradations in low-quality source images and non-interactive to multiple subjective and objective needs. To solve them, we introduce a novel approach that leverages semantic text guidance image fusion model for degradation-aware and interactive image fusion task, termed as Text-IF. It innovatively extends the classical image fusion to the text guided image fusion along with the ability to harmoniously address the degradation and interaction issues during fusion. Through the text semantic encoder and semantic interaction fusion decoder, Text-IF is accessible to the all-in-one infrared and visible image degradation-aware processing and the interactive flexible fusion outcomes. In this way, Text-IF achieves not only multi-modal image fusion, but also multi-modal information fusion. Extensive experiments prove that our proposed text guided image fusion strategy has obvious advantages over SOTA methods in the image fusion performance and degradation treatment. The code is available at https://github.com/XunpengYi/Text-IF.