Miracle-dz/UniFAR-FTUs huggingface dataset:
Light Field Depth Estimation With Multi-Layer Perceptron

Depth estimation is an important topic in computer vision, and light ﬁeld, providing both spatial and angular information of a scene, is valuable in estimating the depth map of the scene. In this project, we explored a multi-layer perceptron (MLP) based approach of depth estimation for light ﬁeld. First, we extract features from light ﬁeld data such as the defocus and correspondence cues. Then, we combine the various features and exploit the relations be-tween them using a MLP model to produce an accurate depth map. We trained the MLP model with data from the 4D Light Field Dataset [6] and successfully produced better depth estimation comparing to Markov Random Field (MRF) based method. The code for this project can be found at github. 1
Discovering Related Data At Scale

Analysts frequently require data from multiple sources for their tasks, but finding these sources is challenging in exabyte-scale data lakes. In this paper, we address this problem for our enterprise's data lake by using machine-learning to identify related data sources. Leveraging queries made to the data lake over a month, we build a relevance model that determines whether two columns across two data streams are related or not. We then use the model to find relations at scale across tens of millions of column-pairs and thereafter construct a data relationship graph in a scalable fashion, processing a data lake that has 4.5 Petabytes of data in approximately 80 minutes. Using manually labeled datasets as ground-truth, we show that our techniques show improvements of at least 23% when compared to state-of-the-art methods.
CYCLANEUSMA (NAEMACYCLUS) NEEDLE-CAST OF PINUS RADIATA IN NEW ZEALAND 4: CHEMICAL CONTROL RESEARCH

Fungicides were screened for ability to control needle-cast of Pinus radiata D. Don caused by Cydaneusma minus (Butin) DiCosmo et al. Undetached shoots were dipped in water-based suspensions at fortnightly intervals for 20 months from the time of flush. Significant improvements in levels of needle retention were achieved with dodine (86% retention), anilazine (76%), benomyl (74%), and dichlone (66%), in comparison with untreated controls (39%). Injections of acidified aqueous solutions of carbendazim into stems of 8- to 10-year-old trees resulted in reductions in foliage yellowing, needle loss, and numbers of C. minus colonies isolated from the foliage. Needle loss was reduced two- to three-fold on 0- to 1-year-old shoots after one season of injections; after 2 consecutive years, of injections needle loss was reduced seven-fold on 1- to 2-year-old shoots. Yellowing and defoliation were also reduced by injections of the non-fungicidal compounds ortho-phenylenediamine and L-arginine monohydrochloride, implying that not all the effects of carbendazim are a consequence of its fungicidal properties. Aerial applications of benomyl (0.25 kg/ha) in an emulsion of water (6 litres/ha) and BP crop oil (4 litres/ha) in June and July failed to check the disease in a 10-year-old P. radiata plantation.
Pasteurisation : time for a reassessment? A risk assessment strategy to investigate alternative processes and technologies

The New Zealand Food Safety Authority (NZFSA) is developing a Quantitative Risk Assessment (QRA) model to estimate the risk to New Zealand consumers from the consumption of bovine milk and milk products. In order to populate the QRA, NZFSA is working in partnership with Fonterra Co-operative to generate data relating to the heat inactivation of identified milkborne hazards under modern commercial heat treatment conditions. The outputs from the QRA will allow the establishment of genuinely risk-based Standards for dairy products either imported or produced in New Zealand. The QRA can be used to investigate alternative time and temperature parameters for heat treatment. In addition, alternative dairy processes can be investigated and assessed. This will provide the dairy industry with the ability and mechanisms to pursue opportunities and innovate within a flexible regulatory framework, with the potential for new products, new processes, and cost and energy savings.
The effect of working capital management on profitability

This research aims to analyze the impact of efficient working capital management on the profitability of the manufacturing firm in Bangladesh. Fifty-two manufacturing companies listed with Dhaka Stock Exchange (DSE) have been selected randomly from 2012 to 2017. Return on Assets (ROA) and Return on Equity (ROE) are used as indicators of profitability, while the inventory conversion period (ICP), the average collection period (ACP), the average payment period (APP), and the Cash Conversion Cycle (CCC) are used as the independent variables which are used as a measurement of working capital management of the firm. Ordinary Least Squares regression models and Pearson's Correlation are used to establish the relationship between working capital management and profitability. The results revealed a significant negative relation between ROA and CCC, ACP; a significant negative relationship exists between ROE and CCC, APP. Manufacturing companies can increase profitability by decreasing the cash conversion cycle, average payment period, and average collection period. It also revealed that ICP is also positively related to ROA and ROE. Therefore, this research concludes that efficiently and effectively managing working capital is very important for increasing manufacturing companies' profitability.
Health App Use and Its Correlates Among Individuals With and Without Type 2 Diabetes: Nationwide Population-Based Survey (e14396)

Background: The prevalence of obesity and diabetes among middle-aged and older adults is on the rise, and with an increase in the world population of adults aged 60 years and older, the demand for health interventions across age groups is growing. Noom is an mHealth behavior change lifestyle intervention that provides users with tracking features for food and exercise logging and weighing-in as well as access to a virtual 1:1 behavior change coach, support group, and daily curriculum that includes diet-, exercise-, and psychology-based content. Limited research has observed the effect of age on a mobile health (mHealth) lifestyle intervention. Objective: The goal of the research was to analyze engagement of middle-aged and older adults using a mobile lifestyle or diabetes prevention intervention. Methods: A total of 14,767 adults (aged 35 to 85 years) received one of two curricula via an mHealth intervention in a quasi-experimental study: the Healthy Weight program (HW) by Noom (84%) or the Noom-developed Diabetes Prevention Program (DPP), recognized by the US Centers for Disease Control and Prevention (CDC). The main outcome measure was weight over time, observed at baseline and weeks 16 and 52. Results: Linear mixed modeling found age to be a significant predictor of weight at week 16 (F2,1398.4=9.20; P<.001; baseline vs week 16: β=–.12, 95% CI –0.18 to –0.07), suggesting that as age increases by 1 year, weight decreased by 0.12 kg. An interaction between engagement and age was also found at week 52 (F1,14680.51=6.70; P=.01) such that engagement was more strongly associated with weight for younger versus older adults (age × engagement: β=.02, 95% CI 0.01 to 0.04). HW users lost 6.24 (SD 6.73) kg or 5.2% of their body weight and DPP users lost 5.66 (SD 7.16) kg or 8.1% of their body weight at week 52, meeting the CDC standards for weight loss effects on health. Conclusions: Age and engagement are significant predictors of weight. Older adults lost more weight using an mHealth evidence-based lifestyle intervention compared with younger adults, despite their engagement. These preliminary findings suggest further clinical implications for adapting the program to older adults’ needs. (JMIR Diabetes 2020;5(2):e18363) doi:10.2196/18363
ClaimPortal - Building a Social Media Analy7cs System for Assis7ng Fact-Checking

We are in a digital era where claims made by people can aZract aZen7on and spread like wildﬁre. Misinforma7on and disinforma7on about important social and poli7cal issues can be inten7onal and mo7ve can be malicious. Thus, we built a TwiZer monitoring pla\orm, namely, ClaimPortal. It assists its users by searching, checking, and providing analy7cs of factual claims made by poli7cians and inﬂuen7al people on TwiZer. ClaimPortal empowers users with a search API which enables ﬁltering condi7ons such as date range, tweets from/men7oning speciﬁc users, keyword based search, hashtags, check-worthiness scores, and types of claims. We explain the architecture of ClaimPortal and its back-end data collec7on and computa7on layer.
Width-Restricted Clause Learning and k -DNF Resolution

The hierarchies of proof systems RTL ( k ) of Resolution trees with lem-mas of width k and Res ∗ ( k ) of tree-like k -DNF Resolution lie between tree-like and dag-like Resolution in strength. We show that these two hierarchies are closely related: Res ∗ ( k ) polynomially simulates RTL ( k ) for every k , but does not simulate RTL ( k + 1), as there is an exponential separation between Res ∗ ( k ) and RTL ( k + 1).
Preventing disproportionate mortality in ICU overload situations: Empirical evidence from the first COVID-19 wave in Europe

Avoiding overloading the healthcare system remains a central issue during the COVID-19 pandemic. The logic of preventing such overload situations is intuitive since the level and quality of critical care is a function of the available capacity to provide it. Where this capacity is no longer available due to a surge in admissions, patient outcomes will invariably deteriorate in the long run, which ultimately leads to disproportionate mortality. In this paper, we study the three worst affected regions in Italy, the Netherlands, and Germany during the first COVID-19 wave in the spring of 2020. We report on quantitative analyses that show how mortality rises non-linearly as the proportion of COVID-19 patients in the ICU increases. We identify changes to the patient-staff ratio, increasing exhaustion and infection levels amongst staff, as well as equipment shortages as likely causes driving this rise in mortality. We explore these findings further with interviews of key stakeholders in the respective healthcare systems. Our results demonstrate that the common approach of managing COVID-19 surges by stretching ICU capacity in hotspot regions may be detrimental to patient outcomes. Instead, we posit that transferring patients proactively out of developing hotspots to less affected regions, well before high ICU workload situations emerge, will improve patient outcomes.
Surgical Gesture Recognition Based on Bidirectional Multi-Layer Independently RNN with Explainable Spatial Feature Extraction

Minimally invasive surgery mainly consists of a series of sub-tasks, which can be decomposed into basic gestures or contexts. As a prerequisite of autonomic operation, surgical gesture recognition can assist motion planning and decision-making, and build up context-aware knowledge to improve the surgical robot control quality. In this work, we aim to develop an effective surgical gesture recognition approach with an explainable feature extraction process.A Bidirectional Multi-Layer independently RNN (BMLindRNN) model is proposed in this paper, while spatial feature extraction is implemented via fine-tuning of a Deep Convolutional Neural Network (DCNN) model constructed based on the VGG architecture. To eliminate the black-box effects of DCNN, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed. It can provide explainable results by showing the regions of the surgical images that have a strong relationship with the surgical gesture classification results.The proposed method was evaluated based on the suturing task with data obtained from the public available JIGSAWS database. Comparative studies were conducted to verify the proposed framework. Results indicated that the testing accuracy for the suturing task based on our proposed method is 87.13%, which outperforms most of the state-of-the-art algorithms.
Trends, Limitations and Open Challenges in Automatic Readability Assessment Research

Readability assessment is the task of evaluating the reading difficulty of a given piece of text. This article takes a closer look at contemporary NLP research on developing computational models for readability assessment, identifying the common approaches used for this task, their shortcomings, and some challenges for the future. Where possible, the survey also connects computational research with insights from related work in other disciplines such as education and psychology.
Natural Language Generation Using Link Grammar for General Conversational Intelligence

Many current artificial general intelligence (AGI) and natural language processing (NLP) architectures do not possess general conversational intelligence--that is, they either do not deal with language or are unable to convey knowledge in a form similar to the human language without manual, labor-intensive methods such as template-based customization. In this paper, we propose a new technique to automatically generate grammatically valid sentences using the Link Grammar database. This natural language generation method far outperforms current state-of-the-art baselines and may serve as the final component in a proto-AGI question answering pipeline that understandably handles natural language material.
Embedded training of neural-network sub-grid-scale turbulence models

The weights of a deep neural network model are optimized in conjunction with the governing flow equations to provide a model for sub-grid-scale stresses in a temporally developing plane turbulent jet at Reynolds number $Re_0=6\,000$. The objective function for training is first based on the instantaneous filtered velocity fields from a corresponding direct numerical simulation, and the training is by a stochastic gradient descent method, which uses the adjoint Navier--Stokes equations to provide the end-to-end sensitivities of the model weights to the velocity fields. In-sample and out-of-sample testing on multiple dual-jet configurations show that its required mesh density in each coordinate direction for prediction of mean flow, Reynolds stresses, and spectra is half that needed by the dynamic Smagorinsky model for comparable accuracy. The same neural-network model trained directly to match filtered sub-grid-scale stresses -- without the constraint of being embedded within the flow equations during the training -- fails to provide a qualitatively correct prediction. The coupled formulation is generalized to train based only on mean-flow and Reynolds stresses, which are more readily available in experiments. The mean-flow training provides a robust model, which is important, though a somewhat less accurate prediction for the same coarse meshes, as might be anticipated due to the reduced information available for training in this case. The anticipated advantage of the formulation is that the inclusion of resolved physics in the training increases its capacity to extrapolate. This is assessed for the case of passive scalar transport, for which it outperforms established models due to improved mixing predictions.
Hierarchical Reinforcement Learning for Air-to-Air Combat

Artificial Intelligence (AI) is becoming a critical component in the defense industry, as recently demonstrated by DARPA's AlphaDogfight Trials (ADT). ADT sought to vet the feasibility of AI algorithms capable of piloting an F-16 in simulated air-to-air combat. As a participant in ADT, Lockheed Martin's (LM) approach combines a hierarchical architecture with maximum-entropy reinforcement learning (RL), integrates expert knowledge through reward shaping, and supports modularity of policies. This approach achieved a 2nd place finish in the final ADT event (among eight total competitors) and defeated a graduate of the US Air Force's (USAF) F-16 Weapons Instructor Course in match play.
DRIVE: Machine Learning to Identify Drivers of Cancer with High-Dimensional Genomic Data & Imputed Labels

Identifying the mutations that drive cancer growth is key in clinical decision making and precision oncology. As driver mutations confer selective advantage and thus have an increased likelihood of occurrence, frequency-based statistical models are currently favoured. These methods are not suited to rare, low frequency, driver mutations. The alternative approach to address this is through functional-impact scores, however methods using this approach are highly prone to false positives. In this paper, we propose a novel combination method for driver mutation identification, which uses the power of both statistical modelling and functional-impact based methods. Initial results show this approach outperforms the state-of-the-art methods in terms of precision, and provides comparable performance in terms of area under receiver operating characteristic curves (AU-ROC). We believe that data-driven systems based on machine learning, such as these, will become an integral part of precision oncology in the near future.
Comparison of linear regressions and neural networks for ﬂuid ﬂow problems assisted with error-curve analysis

Neural networks (NNs) and linear stochastic estimation (LSE) have widely been utilized as powerful tools for ﬂuid-ﬂow regressions. We investigate fundamental differences between them considering two canonical ﬂuid-ﬂow problems: 1. the estimation of high-order proper orthogonal decomposition coefﬁcients from low-order their counterparts for a ﬂow around a two-dimensional cylinder, and 2. the state estimation from wall characteristics in a turbulent channel ﬂow. In the ﬁrst problem, we compare the performance of LSE to that of a multi-layer perceptron (MLP). With the channel ﬂow example, we capitalize on a convolutional neural network (CNN) as a nonlinear model which can handle high-dimensional ﬂuid ﬂows. For both cases, the nonlinear NNs outperform the linear methods thanks to nonlinear activation functions. We also perform error-curve analyses regarding the estimation error and the response of weights inside models. Our analysis visualizes the robustness against noisy perturbation on the error-curve domain while revealing the fundamental difference of the covered tools for ﬂuid-ﬂow regressions.
Stability of shear-jammed granular materials: yielding and stabilization under small-amplitude cyclic shear

Yiqiu Zhao, 2, ∗ Yuchen Zhao, Dong Wang, 3 Hu Zheng, 4 Bulbul Chakraborty, and Joshua E. S. Socolar † Department of Physics & Center for Non-linear and Complex Systems, Duke University, Durham, NC, 27708 Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China Department of Mechanical Engineering & Materials Science, Yale University, New Haven, CT, 06520 Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai, China Martin Fisher School of Physics, Brandeis University, Waltham, MA, 02454 (Dated: May 4, 2021)
Regret and Cumulative Constraint Violation Analysis for Distributed Online Constrained Convex Optimization

This article considers the distributed online convex optimization problem with time-varying constraints over a network of agents. This is a sequential decision making problem with two sequences of arbitrarily varying convex loss and constraint functions. At each round, each agent selects a decision from the decision set, and then only a portion of the loss function and a coordinate block of the constraint function at this round are privately revealed to this agent. The goal of the network is to minimize the network-wide loss accumulated over time. Two distributed online algorithms with full-information and bandit feedback are proposed. Both dynamic and static network regret bounds are analyzed for the proposed algorithms, and network cumulative constraint violation is used to measure constraint violation, which excludes the situation that strictly feasible constraints can compensate the effects of violated constraints. In particular, we show that the proposed algorithms achieve <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(T^{\max \lbrace \kappa,1-\kappa \rbrace })$</tex-math></inline-formula> static network regret and <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(T^{1-\kappa /2})$</tex-math></inline-formula> network cumulative constraint violation, where <inline-formula><tex-math notation="LaTeX">$T$</tex-math></inline-formula> is the time horizon and <inline-formula><tex-math notation="LaTeX">$\kappa \in (0,1)$</tex-math></inline-formula> is a user-defined tradeoff parameter. Moreover, if the loss functions are strongly convex, then the static network regret bound can be reduced to <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(T^{\kappa })$</tex-math></inline-formula>. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results.
Billion-scale Pre-trained E-commerce Product Knowledge Graph Model

In recent years, knowledge graphs have been widely applied to organize data in a uniform way and enhance many tasks that require knowledge, for example, online shopping which has greatly facilitated people’s life. As a backbone for online shopping platforms, we built a billion-scale e-commerce product knowledge graph for various item knowledge services such as item recommendation. However, such knowledge services usually include tedious data selection and model design for knowledge infusion, which might bring inappropriate results. Thus, to avoid this problem, we propose a Pre-trained Knowledge Graph Model (PKGM) for our billion-scale e-commerce product knowledge graph, providing item knowledge services in a uniform way for embedding-based models without accessing triple data in the knowledge graph. Notably, PKGM could also complete knowledge graphs during servicing, thereby overcoming the common incompleteness issue in knowledge graphs. We test PKGM in three knowledge-related tasks including item classification, same item identification, and recommendation. Experimental results show PKGM successfully improves the performance of each task.
Designing Games for Enabling Co-creation with Social Agents

Digital tools have long been used for supporting children's creativity. Digital games that allow children to create artifacts and express themselves in a playful environment serve as efficient Creativity Support Tools (or CSTs). Creativity is also scaffolded by social interactions with others in their environment. In our work, we explore the use of game-based interactions with a social agent to scaffold children's creative expression as game players. We designed three collaborative games and play-tested with 146 5-10 year old children played with the social robot Jibo, which affords three different kinds of creativity: verbal creativity, figural creativity and divergent thinking during creative problem solving. In this paper, we reflect on game mechanic practices that we incorporated to design for stimulating creativity in children. These strategies may be valuable to game designers and HCI researchers designing games and social agents for supporting children's creativity.