Miracle-dz/UniFAR-FTUs huggingface dataset:
Diffusion-Based Signed Distance Fields for 3D Shape Generation

We propose a 3D shape generation framework (SDF-Diffusion in short) that uses denoising diffusion models with continuous 3D representation via signed distance fields (SDF). Unlike most existing methods that depend on discontinuous forms, such as point clouds, SDF-Diffusion generates high-resolution 3D shapes while alleviating memory issues by separating the generative process into two-stage: generation and super-resolution. In the first stage, a diffusion-based generative model generates a low-resolution SDF of 3D shapes. Using the estimated low-resolution SDF as a condition, the second stage diffusion model performs super-resolution to generate high-resolution SDF. Our framework can generate a high-fidelity 3D shape despite the extreme spatial complexity. On the ShapeNet dataset, our model shows competitive performance to the state-of-the-art methods and shows applicability on the shape completion task without modification.
Health Monitoring of Old Buildings in Bangladesh: Detection of Cracks and Dampness Using Image Processing

Numerous buildings in Bangladesh were constructed without following standard building codes. As a result, those are vulnerable to increased earthquake frequency and variable loads. To address this issue, old buildings need to be monitored frequently by non-destructive testing (NDT) to avoid any failures. The identification of cracks and dampness is one of the most important parts of this test. Generally, this detection part is costly and time-consuming to conduct it manually. To resolve this, the current study has been performed for intelligent structural damage identification based on deep learning techniques. A state-of-the-art Convolutional Neural Network (CNN)-based object detection model YOLOv4-tiny has been used to detect cracks and dampness and repair cost analysis of damages. The study outcome suggests that using our proposed deep model, it is possible to detect building cracks with a mean Average Precision (%mAP) of 72.46%. In addition to traditional structural health monitoring, computer vision-based structural health monitoring enables the development of an easily accessible, cost-effective, real-time crack and dampness detection system.
The Exact Determinant of a Specific Class of Sparse Positive Definite Matrices

For a specific class of sparse Gaussian graphical models, we provide a closed-form solution for the determinant of the covariance matrix. In our framework, the graphical interaction model (i.e., the covariance selection model) is equal to replacement product of ${\mathcal{K}_n}$ and ${\mathcal{K}_{n - 1}}$, where ${\mathcal{K}_n}$ is the complete graph with n vertices. Our analysis is based on taking the Fourier transform of the local factors of the model, which can be viewed as an application of the Normal Factor Graph Duality Theorem and holographic algorithms. The closed-form expression is obtained by applying the Matrix Determinant Lemma on the transformed graphical model. In this context, we will also define a notion of equivalence between two Gaussian graphical models.
Bounding the Permanent of a Non-negative Matrix via its Degree- M Bethe and Sinkhorn Permanents

The permanent of a non-negative square matrix can be well approximated by finding the minimum of the Bethe free energy functions associated with some suitably defined factor graph; the resulting approximation to the permanent is called the Bethe permanent. Vontobel gave a combinatorial characterization of the Bethe permanent via degree-M Bethe permanents, which is based on degree-M covers of the underlying factor graph.In this paper, we prove a degree- M-Bethe-permanent-based lower bound on the permanent of a non-negative matrix, which solves a conjecture proposed by Vontobel in [IEEE Trans. Inf. Theory, Mar. 2013]. We also prove a degree- M-Bethe-permanent-based upper bound on the permanent of a non-negative matrix. In the limit $M \to \infty$, these lower and upper bounds yield known Bethe-permanent-based lower and upper bounds on the permanent of a non-negative matrix. Moreover, we prove similar results for an approximation to the permanent known as the (scaled) Sinkhorn permanent.
Fusing Pre-Trained Language Models with Multimodal Prompts through Reinforcement Learning

Language models are capable of commonsense reasoning: while domain-specific models can learn from explicit knowledge (e.g. commonsense graphs [6] ethical norms [25]), and larger models like GPT-3 [7] mani-fest broad commonsense reasoning capacity. Can their knowledge be extended to multimodal inputs such as images and audio without paired domain data? In this work, we propose ‡ESPER (Extending Sensory PErception with Reinforcement learning) which enables text-only pretrained models to address multimodal tasks such as visual commonsense reasoning. Our key novelty is to use rein-forcement learning to align multimodal inputs to language model generations without direct supervision: for example, our reward optimization relies only on cosine similarity derived from CLIP [52] and requires no additional paired (image, text) data. Experiments demonstrate that ESPER outperforms baselines and prior work on a variety of multimodal text generation tasks ranging from captioning to commonsense reasoning; these include a new benchmark we collect and release, the ESP dataset, which tasks models with generating the text of several different domains for each image. Our code and data are publicly released at https://github.com/JiwanChung/esper.
Using NASA Satellite and Model Analysis for Renewable Energy and Energy Efficiency Applications

This presentation describes the successful tailoring of NASA research data sets to meet environmental information needs of the renewable energy sector. The data sets currently used for these purposes include the NASA/GEWEX (Global Energy and Water Cycle Experiment) Surface Radiation Budget dataset (SRB), the FLASHFlux (Fast Longwave and SHortwave Fluxes from Global CERES and MODIS observations), and the NASA GSFC Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System (GEOS) versions 4.0. 3 and 5.0/5.1. These data are available through the Surface meteorology and Solar Energy (SSE) web interface (http://eosweb.larc.nasa.gov/sse). The NASA Earth Science Applied Science program has supported the development of the SSE web interface through a project called the Prediction of World Energy Resource (POWER, http://power.larc.nasa.gov/). The paths of modifying/preparing these data sets for energy applications for the SSE web site are described. These data help engineers, architects, and project analysts develop feasibility studies for renewable energy technology projects, make regional assessments and long-term energy market forecasts. Thus, small-scale projects to regional energy analysis may benefit from this information. The SSE web site has nearly 50,000 users worldwide and version 6.0 is now averaging 250,000 and 60,000 hits and data downloads per month, respectively. Examples of the usage of these data sets are shown to help describe the need and impact of this information. These examples come from the many collaborative partners in this work such as the DOE National Renewable Energy Laboratory (NREL), the Pacific Northwest National Laboratory (PNNL), and the Natural Resources Canada RETScreen project. The presentation also gives potential future data needs of these types of technologies and how NASA data could help contribute to meeting those needs. This is particularly pertinent facing the growing needs to develop clean energy sources to achieve energy independence and climate change mitigation and adaptation. Publication Date: December 2009 Source Publication: Proceedings of the American Geophysical Union Fall Meeting, San Francisco, CA (poster)
Uniqueness of Distributional BP Fixed Point in Ising Model on Trees

In the study of Ising models on large locally tree-like graphs, in both rigorous and non-rigorous methods one is often led to understanding the so-called belief propagation (BP) distributional recursions and its fixed points. We prove that there is at most one non-trivial BP fixed point, which was only known previously for sufficiently "low-temperature" models. Our main innovation is in applying information-theoretic ideas of channel comparison leading to a new metric (degradation index) between binary-input-symmetric (BMS) channels under which the BP operator is a strict contraction (albeit non-multiplicative). A key ingredient of our proof is a strengthening of the classical stringy tree lemma of [1].
A Rotation & Scale Invariant CNN Model to Detect Humans in Disaster Area from Aerial View

Human detection in aerial images is incredibly challenging because of the characteristics aerial images possess. One of the main applications of human detection in aerial images is for search and rescue operations after a disaster. An efficient human detection approach will hugely optimize the search and rescue operation, which helps reduce the loss of lives due to disasters. A custom dataset has been made for this study using an Unmanned Aerial Vehicle (UAV) or drone. The dataset is made to simulate real-life disaster scenarios. Various types of preprocessing are done on the dataset considering the wide range of aerial images, i.e., different scales, orientations, and postures of the human body cover different areas in aerial images. A new convolutional neural network (CNN) based model has been proposed for classification in this paper. The model has been made both scaling and rotation invariant. A comparative study has been done among 12 pretrained models and compared to the proposed model. You Only Look Once (YOLOv3) has been used for transfer learning for the task of localization. The proposed classification model has shown satisfactory results with accuracy as high as 94.18%.
Bayes-Optimal Estimation in Generalized Linear Models via Spatial Coupling

We consider the problem of signal estimation in a generalized linear model (GLM). GLMs cover many canonical problems in statistical estimation including linear regression and phase retrieval. Recent work has precisely characterized the asymptotic minimum mean-squared error (MMSE) for GLMs with i.i. d. Gaussian sensing matrices. However, in many models there is a significant gap between the MMSE and the performance of the best known feasible estimators. In this work we address this gap by considering GLMs defined via spatially coupled sensing matrices. We propose an efficient approximate message passing (AMP) algorithm for estimation and prove that with a simple choice of spatially coupled design, the MSE of a carefully tuned AMP estimator approaches the asymptotic MMSE. Numerical results show that for finite signal dimensions, spatially coupled designs can yield substantially lower MSE than i.i. d. Gaussian designs when used with AMP algorithms.
Feedback Increases the Capacity of Queues with Finite Support Service Times

In their "Bits Through Queues" paper, Anantharam and Verdú showed that if the service time is memoryless feedback does not increase capacity under a FIFO policy, and further conjectured that feedback increases capacity for all other service times. Towards this conjecture, a recent paper by Aptel and Tchamkerten provided a sufficient condition on the service time under which feedback increases capacity. While this condition yields examples of service times for which feedback is helpful, it does not provide explicit structural properties of such service times.In this paper, we consider the discrete-time setting and show that feedback increases capacity for any service time with finite support. We also show that the above sufficient condition is inconclusive for service times with infinite support.
Wind and infiltration interaction for small buildings

The major influence of the wind is observed in infiltration, the random leakage of outside air into the conditioned volume of a residential-scale building. A model that predicts infiltration from both wind and temperature influence to within 20% will be presented. A comparison is made between the predicted and the measured infiltration from a full-scale test structure, revealing an average discrepancy of less than 10 m/sup 3//hr (out of an average of approximately 150 m/sup 3//hr). Direct measurements of the wind velocity and pressure coefficients induced by the wind on the full-scale test structure are also presented.
Importance and Barriers of Establishing Educational/Training Programs in Electric Vehicles/Hybrid-Electric Vehicles in Jordan

Jordan has set priority plans to mitigate climate change impacts, where Jordan moves towards low emissions by promoting the Electric Vehicle and Hybrid-Electric Vehicle (EV/HEV) market. Therefore, there will be a pressing need for professionals in the field of maintenance and design of EV/HEVs. Accordingly, surveys were conducted to address the market needs in Jordan. The surveys targeted main stakeholders from different geographical locations in Jordan. This study was concerned with project partners, Academic staff, and students from eight universities located in Balqa’a, Irbid, Al-Karak, Tafilah, and the capital city Amman. The responses obtained are from 8, 140, and 799 project partners, academic staff, and students, respectively. The results, including surveys on teaching and training facilities, are analyzed and discussed. In conclusion, this study presents a crucial foundation for three specialized diploma programs (vocational diploma, technical diploma, and higher diploma) and a bachelor program tailored to Jordan’s EV/HEV market needs. Through a deliberate alignment of the curriculum with the evolving demands of the EV/HEV sector, the diploma programs will foster graduates who hold the essential knowledge and skills to excel in this rapidly expanding field. Furthermore, the programs address the distinct competencies and expertise that the local market requires, ensuring that graduates are well prepared to meet industry needs. The significance of this work serves as a bridge between academia and the market, resulting in graduates who possess the knowledge and skills, that are highly sought after by prospective employers.
Robust High-Dimensional Linear Discriminant Analysis under Training Data Contamination

The problem of robust Sparse Linear Discriminant Analysis (LDA) in high-dimensions is studied, in which a fraction of the training data may be corrupted by an adversary. A computationally efficient algorithm is proposed by adapting robust mean estimation along with a calibration framework for LDA. Theoretical properties of the proposed algorithm are established for both the estimation error of the optimal projection vector and the mis-classification rate. Results from extensive numerical studies on both synthetic and real datasets are reported to show the usefulness of our algorithm.
Household Food Security in the United States in 2014

An estimated 86.0 percent of American households were food secure throughout the entire year in 2014, meaning that they had access at all times to enough food for an active, healthy life for all household members. The remaining households (14.0 percent) were food insecure at least some time during the year, including 5.6 percent with very low food security, meaning that the food intake of one or more household members was reduced and their eating patterns were disrupted at times during the year because the household lacked money and other resources for food. The change in food insecurity overall from the prior year (from 14.3 percent in 2013) was not statistically significant. The cumulative decline in food insecurity from 2011 (14.9 percent) to 2014 (14.0 percent) was statistically significant. The prevalence rate of very low food security was essentially unchanged from 5.6 percent in 2013 and 5.7 percent in 2011 and 2012. Children and adults were food inse - cure in 9.4 percent of households with children in 2014, essentially unchanged from 9.9 percent in 2013 and 10.0 percent in 2011 and 2012. In 2014, the typical food-secure house - hold spent 26 percent more on food than the typical food-insecure household of the same size and household composition. Sixty-one percent of all food-insecure households partic - ipated in one or more of the three largest Federal food and nutrition assistance programs during the month prior to the 2014 survey.
How to Generate Missing Data For Simulation Studies

Missing data are common in psychological and educational research. With the improvement in computing technology in recent decades, more researchers have begun developing missing data techniques. In their research, they often conduct Monte Carlo simulation studies to compare the performances of different missing data techniques. During such simulation studies, researchers must generate missing data in the simulated dataset by deciding which data values to delete. However, in the current literature, there are limited guidelines on how to generate missing data for simulation studies. Our paper is one of the ﬁrst that examines ways of generating missing data for simulation studies. I emphasize the importance of specifying missing data rules which are statistical models for generating missing data. I begin the paper by reviewing the types of missing data mechanisms and missing data patterns. I then explain how to specify missing data rules to generate missing data with different mechanisms and patterns. I emphasize the advantages and disadvantages of using different missing data rules and algorithms to generate missing data for simulation studies. Next, I discuss other important aspects of simulation studies involving missing data. I end the paper by offering recommendations for generating missing data for simulation studies.
Proceedings of the Annual Meeting of the Cognitive Science Society

Child-directed language (CDL) features words such as doggy , night-night , and tummy that are rarely used in adult-directed language (ADL). Characteristics of CDL variants, such as diminutivization and reduplication, explain why they may be learned and produced earlier by children. However, it is not yet clear how or when children switch to using ADL equivalents— dog , goodnight , stomach . Through analysis of speech transcripts from CHILDES and the Language Development Project corpus, we show that children signiﬁcantly increase their production of ADL variants across age, with the average CDL-to-ADL transition point at 2.5 years. Many of the linguistic features that distinguish CDL vs. ADL registers (e.g., lexical and syntactic complexity) similarly differentiated the local speech contexts surrounding CDL vs. ADL variants in children’s input. Notably, these differences emerged even in speech that was primarily child-directed. Learners may therefore be able to capitalize on these linguistic cues to support their discovery of register along with context-appropriate CDL/ADL pair use.
Development of a School-based Intervention Program for Waste Management in a Rural School in Northern Thailand

: Background: Waste management is a challenging environmental and public health issue in Thailand and throughout the world. Objective: This study aimed to develop a school-based intervention program for waste management in one rural school in Northern Thailand. Methods: A specific research and development method was employed to develop the school-based intervention program. This consisted of three steps: 1) a problem and need assessment step, wherein 38 key stakeholders associated with the school, including a school administrator, teachers, students, a food vendor, and a janitor, were interviewed to understand the existing waste management problems and intervention components; 2) an intervention development step, involving students, teachers, and researchers who worked together to design an intervention program based on the perspectives of the stakeholders; 3) an intervention implementation and evaluation step, wherein students, teachers, and researchers implemented the developed intervention program in the school and evaluated the appropriateness of the program using an online questionnaire. Qualitative data were analyzed using content analysis, while one-way ANOVA was used to compare waste management knowledge among students at the pre-, right after, and one-month-after implementation stages.
Finite-time and Fixed-time Synchronization for Inertial Memristive Neural Networks with Time-varying Delay and Linear Coupling

—This paper studies the ﬁnite-time and ﬁxed-time synchronization for the coupled inertial memristive neural networks (CIMNNs) with time-varying delays. First, with the help of the differential inclusions theory and the interval matrix theory, the CIMNNs with state-dependent parameters which may be mismatched are transformed into an uncertain interval parametric system. Then, two novel controllers are designed to deal with the mismatched parameters and the time-varying delay. Moreover, according to the interval matrix theory, two sufﬁcient conditions are attained which make the CIMNNs synchronize with the target trajectory in the ﬁxed time. Finally, one numerical simulation example is given to demonstrate the effectiveness of the theoretical results.
Distilling Representations from GAN Generator via Squeeze and Span: Appendix

Default representation source Although Table 1 in the main text shows that good performance can be obtained by distillation from both latent variable and generator feature, we choose generator feature (Equ 4 in the main text) as default since generator feature shows better properties (Fig. 2 in the main text) than others. Moreover, this choice can easily apply to other GAN architecture like BigGAN [2].
Cobalt-ferrite functionalized graphitic carbon nitride (CoFe2O4@g-C3N4) nanoconfined catalytic membrane for efficient water purification: Performance and mechanism

Membrane-based nanoconfinement catalysis combined with advanced oxidation processes (AOPs) and membrane filtration can efficiently and rapidly remove organic pollutants. Herein, cobalt-ferrite-doped graphitic carbon nitride (CoFe2O4@g-C3N4) nanoconfined catalytic membranes were fabricated.