Miracle-dz/UniFAR-FTUs huggingface dataset:
Data fusion for predicting ARDS using the MIMIC II physiological database

This study aims to predict Acute Respiratory Distress Syndrome (ARDS) in hospitalized patients using their physiological signals such as heart rate, breathing rate, peripheral arterial oxygen saturation and mean airway blood pressure. A data fusion approach based on hypothesis testing was developed, and applied to mechanically ventilated subjects in the MIMIC II database. By combining the information extracted from the signals using an aggregation rule, we are able to enhance the sensitivity of the ARDS prediction process. As a result, we obtained a sensitivity of up to 85% for individual signals, reaching approximately 92% using the data fusion rule.
Identifying and Mapping Wetland-Based Ecotourism Areas in the First Meander of the Yellow River: Incorporating Tourist Preferences

Abstract: We identified and mapped ecotourism areas in the First Meander of the Yellow River (FMYR) by incorporating tourist preferences, and regional division was also conducted. Considering wetland protection and local natural worship, we identified available ecotourism areas and used GIS to overlay six layers of the criteria to generate a suitability map of available areas. This process incorporated the opinions of 158 tourists to weight each criterion. Available areas were classified into five levels of suitability, with class I being the least suitable for ecotourism and class V being the most suitable. Distance from water area was found to be the most important criterion, and presence of forest to be the least important. The results show that a majority of the FMYR is class II, III or IV. FMYR was further divided into five sub-regions, and it is clear that the particular ecological characteristics and service functions of each sub-region warrant particular development. This study provides useful insights for tourism planners, local managers and visitors.
Scalable Constrained Spectral Clustering

Constrained spectral clustering (CSC) algorithms have shown great promise in significantly improving clustering accuracy by encoding side information into spectral clustering algorithms. However, existing CSC algorithms are inefficient in handling moderate and large datasets. In this paper, we aim to develop a scalable and efficient CSC algorithm by integrating sparse coding based graph construction into a framework called constrained normalized cuts. To this end, we formulate a scalable constrained normalized-cuts problem and solve it based on a closed-form mathematical analysis. We demonstrate that this problem can be reduced to a generalized eigenvalue problem that can be solved very efficiently. We also describe a principled k-way CSC algorithm for handling moderate and large datasets. Experimental results over benchmark datasets demonstrate that the proposed algorithm is greatly cost-effective, in the sense that (1) with less side information, it can obtain significant improvements in accuracy compared to the unsupervised baseline; (2) with less computational time, it can achieve high clustering accuracies close to those of the state-of-the-art.
Greater power and computational efficiency for kernel-based association testing of sets of genetic variants

Motivation: Set-based variance component tests have been identified as a way to increase power in association studies by aggregating weak individual effects. However, the choice of test statistic has been largely ignored even though it may play an important role in obtaining optimal power. We compared a standard statistical test—a score test—with a recently developed likelihood ratio (LR) test. Further, when correction for hidden structure is needed, or gene–gene interactions are sought, state-of-the art algorithms for both the score and LR tests can be computationally impractical. Thus we develop new computationally efficient methods. Results: After reviewing theoretical differences in performance between the score and LR tests, we find empirically on real data that the LR test generally has more power. In particular, on 15 of 17 real datasets, the LR test yielded at least as many associations as the score test—up to 23 more associations—whereas the score test yielded at most one more association than the LR test in the two remaining datasets. On synthetic data, we find that the LR test yielded up to 12% more associations, consistent with our results on real data, but also observe a regime of extremely small signal where the score test yielded up to 25% more associations than the LR test, consistent with theory. Finally, our computational speedups now enable (i) efficient LR testing when the background kernel is full rank, and (ii) efficient score testing when the background kernel changes with each test, as for gene–gene interaction tests. The latter yielded a factor of 2000 speedup on a cohort of size 13 500. Availability: Software available at http://research.microsoft.com/en-us/um/redmond/projects/MSCompBio/Fastlmm/. Contact: heckerma@microsoft.com Supplementary information: Supplementary data are available at Bioinformatics online.
The Distributed Constraint Satisfaction Problem: Formalization and Algorithms

We develop a formalism called a distributed constraint satisfaction problem (distributed CSP) and algorithms for solving distributed CSPs. A distributed CSP is a constraint satisfaction problem in which variables and constraints are distributed among multiple agents. Various application problems in distributed artificial intelligence can be formalized as distributed CSPs. We present our newly developed technique called asynchronous backtracking that allows agents to act asynchronously and concurrently without any global control, while guaranteeing the completeness of the algorithm. Furthermore, we describe how the asynchronous backtracking algorithm can be modified into a more efficient algorithm called an asynchronous weak-commitment search, which can revise a bad decision without exhaustive search by changing the priority order of agents dynamically. The experimental results on various example problems show that the asynchronous weak-commitment search algorithm is, by far more, efficient than the asynchronous backtracking algorithm and can solve fairly large-scale problems.
Convergence to and quality of equilibria in distributed systems

A natural property of a distributed system to study is its set of stable points, or equilibria. In a system comprising individual agents where the communication between the agents is local, determining whether the system converges over time to an equilibrium is important for understanding whether a global consensus is reached by the agents. A proof that such a system converges to a fixed point shows that the dynamic process executed by the agents can be considered a local computation that produces a global output. In a distributed system with selfish users, an equilibrium in which no user can benefit via a unilateral deviation is an expected outcome of the system. From the point of view of a system designer, a comparison between the aggregate welfare of all the users at an equilibrium and that at an optimal state provides a useful measure of system performance. In this work, we study the equilibria of several distributed systems. One setting that we consider is a network in which nodes obtain global information by repeatedly communicating with their neighbors. We study a gossip algorithm of Deb and Medard for information dissemination in the network. For a general network topology, we provide an upper bound on the time required for the network to converge to a state in which every node has every message to be disseminated. We also show that if each node begins with a positive number and executes a simple gossip algorithm, the system converges to a state in which every node has an accurate estimate of the sum of the numbers. Using this approximate summation algorithm as a subroutine, we develop a simple distributed algorithm for a class of convex optimization problems. In this algorithm, the nodes repeatedly execute a gradient ascent procedure that converges to an approximate solution to the convex optimization problem. Another setting that we consider involves the production of a good that is to be consumed by multiple users. Each user requests a quantity of the good. The system produces enough of the good to satisfy all the requests, and recovers the cost of producing the total amount requested from the users by assigning a cost share to each user. The utility of a user is a function of the user's requested quantity and assigned cost share. When users act to maximize their individual utilities, a strategic game is a natural model of the system. Under standard assumptions on the utility functions and a quadratic cost function, and two well-known cost sharing methods, average cost pricing and serial cost sharing, this game is guaranteed to have a Nash equilibrium in which no user can benefit by requesting a different quantity. We show how to determine the worst-case ratio between the aggregate welfare of the users under a Nash equilibrium and the optimal aggregate welfare for each cost sharing method in a class that interpolates between these two methods.
Visualization of the concentration distribution of Asian dust in the Google Earth

A method for identifying the released region of Asian dust using the wind velocity near the surface and the long-range inverse transport model that traces the wind field in the backward direction from positions where Asian dust was observed is described. The spatio-temporal concentration distribution of dust clouds over the East Asia was computed in the case that Asian dust clouds were observed in Japan from April 1 to April 2, 2007. In this case, the released mass flux of Asian dust in source regions was determined such that the simulated concentration of Asian dust almost corresponds to the concentration of the suspended particulate matter (SPM) measured at various places in Japan. In order to better understand through which paths sand dust particles are transported to Japan, the time variation of the concentration distribution of Asian dust clouds and the wind field at 950hPa from 22:00 on March 30 to 15:00 on April 4, 2007was animated every one hour in the Google Earth.
Using speculative execution to automatically hide i/o latency

The gap between processing speeds and disk access times is widening. This trend is causing applications that must fetch data from disk to spend an increasing proportion of their execution times stalled on disk I/O. I/O prefetching, a well-known technique for hiding disk latency, has the potential to alleviate this problem, particularly when the data that needs to be fetched is distributed across multiple disks. A major hurdle to benefiting from this technique in practice is the difficulty of generating accurate and timely prefetches. In this dissertation, I put forth a new approach to generating accurate and timely prefetches without programmer involvement. The key to the proposed approach is its unique method for predicting what data an executing process will access in the future. The approach involves adding an execution of each target process's code that exploits spare processing cycles. These added executions skip some operations, like accesses to uncached data, so that they can run ahead of their target normal executions. This permits differences between the data values used during the added speculative executions and their target normal executions. Despite any such differences, the approach predicts that the data accesses encountered during speculative executions will often be the same as the data accesses that will be encountered during their target normal executions such that, by initiating prefetching of that data, speculative executions could reduce the I/O stall time of their target normal executions. To investigate the viability of this approach, I developed and evaluated SpecHint, a design and implementation for applying the approach automatically. SpecHint is based on binary modification and requires no operating system support specific to this approach. I evaluated SpecHint using six benchmarks from the TIP benchmark suite. Experiments demonstrate that, with the file system striped across four disks, SpecHint reduces the elapsed times of five of the benchmarks (text search, scientific visualization, object linking, and two database queries) by 24% to 71%, with an average of 53%. Moreover, simulation experiments suggest that, on future systems, the benefit of SpecHint would not decrease, and may even increase for some applications.
Results of the Ontology Alignment Evaluation Initiative 2021

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2021 campaign offered 13 tracks and was attended by 21 participants. This paper is an overall presentation of that campaign.
O( √ Log N) Approximation Algorithms for Min Uncut, Min 2cnf Deletion, and Directed Cut Problems

We give O(√ log n)-approximation algorithms for the Min UnCut, Min 2CNF Deletion, Directed Balanced Sep-arator, and Directed Sparsest Cut problems. The previously best known algorithms give an O(log n)-approximation for Min UnCut [9], Directed Balanced Separator [17], Directed Sparsest Cut [17], and an O(log n log log n)-approximation for Min 2CNF Deletion [14]. We also show that the integrality gap of an SDP relaxation of the Minimum Multicut problem is Ω(log n). Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.
Always-On 674μ W@4GOP/s Error Resilient Binary Neural Networks With Aggressive SRAM Voltage Scaling on a 22-nm IoT End-Node

Binary Neural Networks (BNNs) have been shown to be robust to random bit-level noise, making aggressive voltage scaling attractive as a power-saving technique for both logic and SRAMs. In this work, we introduce the first fully programmable IoT end-node system-on-chip (SoC) capable of executing software-defined, hardware-accelerated BNNs at ultra-low voltage. Our SoC exploits a hybrid memory scheme where error-vulnerable SRAMs are complemented by reliable standard-cell memories to safely store critical data under aggressive voltage scaling. On a prototype in 22nm FDX technology, we demonstrate that both the logic and SRAM voltage can be dropped to 0.5V without any accuracy penalty on a BNN trained for the CIFAR-10 dataset, improving energy efficiency by 2.2X w.r. t. nominal conditions. Furthermore, we show that the supply voltage can be dropped to 0.42V (50% of nominal) while keeping more than 99% of the nominal accuracy (with a bit error rate ~1/1000). In this operating point, our prototype performs 4Gop/s (15.4 Inference/s on the CIFAR-10 dataset) by computing up to 13 binary ops per pJ, achieving 22.8 Inference/s/mW while keeping within a peak power envelope of 674uW – low enough to enable always-on operation in ultra-low power smart cameras, long-lifetime environmental sensors, and insect-sized pico-drones.
Deep learning methods for automatic classification of medical images and disease detection based on chest X-Ray images

Detecting and classifying diseases using X-Ray images is one of the more challenging core tasks in the medical and research world. Innovations and revolutions of Computer Vision with Deep learning methods offer great promise for fast and accurate diagnosis of screening and detection from chest X-Ray images (CXR). This work presents rapid detection of diseases in the lung using the efficient Deep learning pre-trained RepVGG algorithm for deep feature extraction and classification. We performed automatic classification of X-Ray images into three categories as Covid-19, Pneumonia, and Normal X-Ray cases. For evaluation, first, we used a histogram-oriented gradient (HOG) to detect the shape of the region of interest (ROI). We used the ROI object to improve the detection accuracy for lung extraction, followed by data pre-processing and augmentation. Then a pre-trained RepVGG model is used for deep feature extraction and classification, similar to VGG and ResNet convolutional neural network for the training-time and inference-time architecture transformed from the multi to the flat mode by a structural re-parameterization technique. Next, using the Computer Vision technique, we created a feature map and superimposed it on the original images. We used this technique for the automatic highlighted detection of affected areas of people's lungs. Based on the X-Ray images, we developed an algorithm that classifies X-Ray images with height accuracy and power faster thanks to the architecture transformation of the model. We compare deep learning frameworks' accuracy and detection of disease. The study shows the high power of deep learning methods for X-Ray images based on COVID-19 detection utilizing chest X-Ray. The proposed framework shows better diagnostic accuracy by comparing popular deep learning models, i.e., VGG, ResNet50, inceptionV3, DenseNet, and InceptionResnetV2.
Operation-guided Neural Networks for High Fidelity Data-To-Text Generation

Recent neural models for data-to-text generation are mostly based on data-driven end-to-end training over encoder-decoder networks. Even though the generated texts are mostly fluent and informative, they often generate descriptions that are not consistent with the input structured data. This is a critical issue especially in domains that require inference or calculations over raw data. In this paper, we attempt to improve the fidelity of neural data-to-text generation by utilizing pre-executed symbolic operations. We propose a framework called Operation-guided Attention-based sequence-to-sequence network (OpAtt), with a specifically designed gating mechanism as well as a quantization module for operation results to utilize information from pre-executed operations. Experiments on two sports datasets show our proposed method clearly improves the fidelity of the generated texts to the input structured data.
Optimizing Data Shuffling in Data-Parallel Computation by Understanding User-Defined Functions

Map/Reduce style data-parallel computation is characterized by the extensive use of user-defined functions for data processing and relies on data-shuffling stages to prepare data partitions for parallel computation. Instead of treating user-defined functions as "black boxes", we propose to analyze those functions to turn them into "gray boxes" that expose opportunities to optimize data shuffling. We identify useful functional properties for user-defined functions, and propose SUDO, an optimization framework that reasons about data-partition properties, functional properties, and data shuffling. We have assessed this optimization opportunity on over 10,000 data-parallel programs used in production SCOPE clusters, and designed a framework that is incorporated it into the production system. Experiments with real SCOPE programs on real production data have shown that this optimization can save up to 47% in terms of disk and network I/O for shuffling, and up to 48% in terms of cross-pod network traffic.
Bank productivity in the eurozone

This paper examines the total factor productivity growth of commercial banks in nine eurozone countries over the period 1992-2009. We utilise a parametric metafrontier Divisia index, which allows for technology heterogeneity and the identification of technology gaps among different countries. A series of tests are employed to determine whether there is long run productivity convergence. The results suggest that while technical improvements have certainly taken place, not all countries have taken full advantage of them. The analysis of convergence indicates that all countries are progressively moving towards the best available technology. The speed of efficiency convergence accelerates after the introduction of the single currency, before decreasing after the 2007 crisis.
Mapping LAI using BRDF model in arid and semi-arid Northwestern China

Leaf area index (LAI) is a critical vegetation parameter for the global and regional scale studies of the climatic and environmental change. There are many methods that can be used to get LAI. In this paper, the method, developed by Qi et al. (2000) was selected. The process includes three steps: the first step is model inversion, using BRDF model to produce LAI with pixels chose randomly in one vegetation type region; the second step is quality control, removing the outliers, fitting equations using the LAI from second step and satellite data NDVI; the third step is LAI mapping, selecting the best equation and applying it to the whole region to mapping spatial LAI distribution. The main objective of this paper is to get one method that can be used in Arid and Semi-arid Northwestern China to derive LAI in the case of lack of LAI measurements. The results derived by the above approach were compared with ones derived from the empirical method (Sellers et al. 1996) and the LAI measured in field. The results suggested that the method can get good result and R2 was 0.7947, though they were greater than field measurements. The results from empirical method were closer to the measurements than ones from Qi's method, but the higher the values of NDVI were, the greater the values of estimated LAI were than LAI measurements, when the values of NDVI were greater than a certain values (here 0.74). However, the result derived from Qi's method is closer to the LAI measured in field. In general, this method was feasible in arid and semi-arid northwestern China and can get satisfactory results.
A Non-linear Differential CNN-Rendering Module for 3D Data Enhancement

In this work we introduce a differential rendering module which allows neural networks to efficiently process cluttered data. The module is composed of continuous piecewise differentiable functions defined as a sensor array of cells embedded in 3D space. Our module is learnable and can be easily integrated into neural networks allowing to optimize data rendering towards specific learning tasks using gradient based methods in an end-to-end fashion. Essentially, the module's sensor cells are allowed to transform independently and locally focus and sense different parts of the 3D data. Thus, through their optimization process, cells learn to focus on important parts of the data, bypassing occlusions, clutter and noise. Since sensor cells originally lie on a grid, this equals to a highly non-linear rendering of the scene into a 2D image. Our module performs especially well in presence of clutter and occlusions. Similarly, it deals well with non-linear deformations and improves classification accuracy through proper rendering of the data. In our experiments, we apply our module to demonstrate efficient localization and classification tasks in cluttered data both 2D and 3D.