Miracle-dz/UniFAR-FTUs huggingface dataset:
Simple but Effective Tree Structures for Dynamic Programming-Based Stereo Matching

This work describes a fast method for computing dense stereo correspondences that is capable of generating results close to the state-of-the-art. We propose running a separate disparity computation process in each image pixel. The idea is to root a tree graph on the pixel whose disparity needs to be reconstructed. The tree thereby forms an individual approximation of the standard four-connected grid for this specific pixel. An exact optimum of a predefined energy function on the applied tree structure is determined via dynamic programming (DP), and the root pixel is assigned to the disparity of optimal costs. We present two simple tree structures that allow for the efficient calculation of all trees’ optima with only four scanline-based DP passes. These simple trees are designed to capture all pixels of the reference frame and incorporate horizontal and vertical smoothness edges in order to weaken the scanline streaking problem inherent in DP-based approaches. We evaluate our results using the Middlebury test set. Our algorithm currently ranks at the eighth position of approximately 30 algorithms in the Middlebury database. More importantly, it is the currently best-performing method that does not use image segmentation and is significantly faster than most competing algorithms. Our method needs less than a second to determine the disparity map for typical stereo pairs.
Faster Agreement via a Spectral Method for Detecting Malicious Behavior

We address the problem of Byzantine agreement, to bring processors to agreement on a bit in the presence of a strong adversary. This adversary has full information of the state of all processors, the ability to control message scheduling in an asynchronous model, and the ability to control the behavior of a constant fraction of processors which it may choose to corrupt adaptively. In 1983, Ben-Or proposed an algorithm for solving this problem with expected exponential amount of communication. In 2013, the algorithm was improved to expected polynomial communication time, but still an exponential amount of computation per individual processor was required. In this paper, we improve that result to require both expected polynomial computation and communication time. We use a novel technique for detecting malicious behavior via spectral analysis. In particular, our algorithm uses coin flips from individual processors to repeatedly try to generate a fair global coin. The corrupted processors can bias this global coin by generating biased individual coin flips. However, we can detect which processors generate biased coin flips by analyzing the top right singular vector of a matrix containing the sums of coin flips generated by each processor. Entries in this singular vector with high absolute value correspond to processors that are trying to bias the global coin, and this information can be used to blacklist malicious processors.
Development and Application of PTSD and IES in Survivors after Disaster

This article reviewed the development and application of a very popular self-evaluation tool: Impact of Event Scale (IES) and its revised version (IES-R), and reported some results on 956 survivors in using IES-R in Mianzhu, Sichuan. We referred that about half of the survivors will have the main symptoms of the PTSD and will have the significant differences in gender: female had high scores than male, who should accept the psychological intervention early.
Social Factors and Leukocyte DNA Methylation of Repetitive Sequences: The Multi-Ethnic Study of Atherosclerosis

Epigenetic changes are a potential mechanism contributing to race/ethnic and socioeconomic disparities in health. However, there is scant evidence of the race/ethnic and socioeconomic patterning of epigenetic marks. We used data from the Multi-Ethnic Study of Atherosclerosis Stress Study (N = 988) to describe age- and gender- independent associations of race/ethnicity and socioeconomic status (SES) with methylation of Alu and LINE-1 repetitive elements in leukocyte DNA. Mean Alu and Line 1 methylation in the full sample were 24% and 81% respectively. In multivariable linear regression models, African-Americans had 0.27% (p<0.01) and Hispanics 0.20% (p<0.05) lower Alu methylation than whites. In contrast, African-Americans had 0.41% (p<0.01) and Hispanics 0.39% (p<0.01) higher LINE-1 methylation than whites. These associations remained after adjustment for SES. In addition, a one standard deviation higher wealth was associated with 0.09% (p<0.01) higher Alu and 0.15% (p<0.01) lower LINE-1 methylation in age- and gender- adjusted models. Additional adjustment for race/ethnicity did not alter this pattern. No associations were observed with income, education or childhood SES. Our findings, from a large community-based sample, suggest that DNA methylation is socially patterned. Future research, including studies of gene-specific methylation, is needed to understand better the opposing associations of Alu and LINE-1 methylation with race/ethnicity and wealth as well as the extent to which small methylation changes in these sequences may influence disparities in health.
On the Sense of College Students’ Social Responsibility and Factors Involved

The sense of social responsibility is a stable psychological inclination demonstrated in individuals’ attitude,motivation,and strategies towards the social responsibility.The essay is aiming to explore the characteristics and influential factors in college students’ sense of the responsibility through sampling 817 subjects and by means of the Independent-Samples T Test analysis and the One-Way ANOVA analysis.The major findings reveal that: university students’ sense of the social responsibility stands over the intermediate level;the female subjects display higher sense of the social responsibility than their male counterparts,but without remarkable grade difference;the variables of being only children or not show no significant difference while parents' education has much influence on university students’ sense of social responsibility;and those accepting self-assessments sense the social responsibility more than those who fail to.
Battery-free multichannel digital ECG biotelemetry using UHF RFID techniques

We propose to leverage UHF RFID techniques to yield a continuously wearable, battery-free wireless multichannel ECG telemetry device that is potentially disposable, low-cost and suitable for integration with multiple electrodes in a flexible circuit assembly. Such a device could have broad applicability, ranging from initial patient assessment by first responders, to continuous monitoring in various clinical settings. We employ a recently described single-chip data acquisition system including RF power harvesting to eliminate the need for a battery. The single-chip system includes 14 channels of integrated biopotential amplification, an 11-bit ADC, and a 5 Mbps digital backscatter telemetry link. We present an initial characterization of the telemetry chip in this application including battery-free, wireless 3 and 5 channel ECG recordings made from an ambulatory human subject at a range of ≈ 1 meter.
A Levinson-Galerkin Algorithm for Regularized Trigonometric Approximation

Trigonometric polynomials are widely used for the approximation of a smooth function from a set of nonuniformly spaced samples. If the samples are perturbed by noise, a good choice for the polynomial degree of the trigonometric approximation becomes an essential issue to avoid overfitting and underfitting of the data. Standard methods for trigonometric least squares approximation assume that the degree for the approximating polynomial is known a priori, which is usually not the case in practice. We derive a multilevel algorithm that recursively adapts to the least squares solution of suitable degree. We analyze under which conditions this multilevel approach yields the optimal solution. The proposed algorithm computes the solution in at most $\mathcal{O}(rM + M^2)$ operations (M being the polynomial degree of the approximation and r being the number of samples) by solving a family of nested Toeplitz systems. It is shown how the presented method can be extended to multivariate trigonometric approximation. We demonstrate the performance of the algorithm by applying it in echocardiography to the recovery of the boundary of the left ventricle of the heart.
Flight Control System Design Optimisation via Genetic Programming

This chapter presents a methodology which is developed to design a controller that satisfies the objectives of shipboard recovery of a fixed-wing UAV. The methodology itself is comprehensive and should be readily applicable for different types of UAVs and various task objectives. With appropriate modification of control law representation, the methodology can be applied to a broad range of control problems. Development of the recovery controller for the UAV Ariel is a design example to support the methodology. This chapter focuses on adaptation of Evolutionary Algorithms for aircraft control problems. It starts from analysis of typical control laws and control design techniques. Then, the structure of the UAV controller and the representation of the control laws suitable for evolutionary design are developed. This is followed by the development of the general evolutionary design algorithm, which is then applied to the UAV recovery problem. Finally the presented results demonstrate robust properties of the developed control system.
Industrial real-time regression testing and analysis using firewalls

Industrial real-time systems are complex and need to be thoroughly tested before being released to the customer. We have found that last minute changes are often responsible for the introduction of defects, causing serious problems for the customer. We demonstrate that these defects can be introduced into real-time software in diverse ways, and there is no simple regression testing method that can deal with all of these defect sources. This paper describes the application of a testing firewall for regression testing whose form differs depending upon the defect. The idea of the testing firewall is to limit the regression testing to those potentially affected system elements directly dependent upon changed system elements, and then to thoroughly test these elements. This has resulted in substantial savings in regression testing costs, and yet has been effective in detecting critical defects with significant implication in terms of customer acceptance at ABB. Empirical studies are reported for these experiences in an industrial setting.
Narrative intelligence in nursing: Storying patient lives in dementia care

This paper examines narrative approaches to care within the context of dementia. It reviews the function of stories and explores some of the narrative genres that shape the cultural perceptions of dementia. We argue that narrative intelligence within healthcare is an important element in nurturing communal self-identity for people living with dementia. Listening and responding to stories and the cultural framework that this encompasses is an embodied action that is not just related to cognitive recall but situates us within a cultural community. People with dementia may have challenges in maintaining narrative legitimacy in the face of fractured stories and incoherent narratives. Health professionals can offer support in reframing and presenting counterstories that maintain identities that can potentially be marginalised, silenced and open to narrative foreclosure. This process requires health professionals to be attentive and responsive to how patients with dementia and their supporters construct and position the stories they tell and the meaning(s) they attach to them.
Active recognition and pose estimation of household objects in clutter

This paper presents an active object recognition and pose estimation system for household objects in a highly cluttered environment. A sparse feature model, augmented with the characteristics of features when observed from different viewpoints is used for recognition and pose estimation while a dense point cloud model is used for storing geometry. This strategy makes it possible to accurately predict the expected information available during the Next-Best-View planning process as both the visibility as well as the likelihood of feature matching can be considered simultaneously. Experimental evaluations of the active object recognition and pose estimation with an RGB-D sensor mounted on a Turtlebot are presented.
A meta‐analysis of insect pest behavioral manipulation with plant volatiles

Many insect pests utilize plant volatiles for host location and untangling the mechanisms of this process can provide tools for pest management. Numerous experimental results have been published on the effect of plant volatiles on insect pests. We used a meta‐analysis to summarize this knowledge and to look for patterns. Our goal was to identify herbivore and plant traits that might explain the herbivores’ behavioral response to plant volatiles in field applications. We scored a total of 374 unique plant volatile‐insect herbivore interactions obtained from 34 published studies investigating 50 herbivore pest species. Attractants had a significant effect on insect herbivore abundance but repellents did not; this latter result could be a result of the comparatively small number of field studies that tested plant volatiles as repellents (3%). Females were significantly more attracted to plant volatile baits than males. The diet breadth of herbivores was independent of a behavioral response to plant volatiles, but more case studies show effects of volatiles on chewers, followed by wood‐borers and sap‐feeders. There are more demonstrations of attraction to plant volatiles in Lepidoptera than in Thysanoptera. The method of plant volatile application had a significant effect on herbivore abundance and increasing the number of chemicals in individual baits attracted more herbivores. The magnitude of the response of herbivores to plant volatiles in forest and agricultural habitats was similar. We explore consistent patterns and highlight areas needing research in using plant volatiles to manage insect pests.
Exploring microRNA Biomarker for Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is among the severe neuro degenerative diseases that lack widely available effective treatments. As the disease progresses, patients lose the control of voluntary muscles. Although the neuronal degeneration is the cause of this disease, the failure mechanism is still unknown. In order to seek genetic mechanisms that initiate and progress ALS, the association of microRNA (miRNA) expression with this disease was considered. Serum miRNAs from healthy controls, sporadic ALS (sALS), familial ALS (fALS) and ALS mutation carriers were investigated. Principal component analysis (PCA)-based unsupervised feature extraction (FE) was applied to these serum miRNA profiles. As a result, we predict miRNAs that can discriminate patients from healthy controls with high accuracy. Thus, these miRNAs can be potential prognosis miRNA biomarkers for ALS.
Maximizing the reliability of two-state automaton for burst feature detection in news streams

The capture of temporal dynamics of news streams has drawn increasing attentions in recent sequential data mining works. Most of them are based on the intuition that a “burst” of a topic is signaled by a growth of relevant words in a high intensity during a period of time. Such “burst features” can be efficiently identified by Kleinberg's two-state automaton model. The resolution is an important parameter of the model. It affects the reliability of the results greatly. This paper maximizes the reliability of the results by estimating adaptive resolution for each word with EM algorithm. Experiments with the public news corpora prove that the unified resolution is a bottleneck of the performance, and the results with word-adaptive resolutions approximate to the maximum reliability well.
Performance-constrained Distributed DVS Scheduling for Scientific Applications on Power-aware Clusters

Left unchecked, the fundamental drive to increase peak performance using tens of thousands of power hungry components will lead to intolerable operating costs and failure rates. High-performance, power-aware distributed computing reduces power and energy consumption of distributed applications and systems without sacrificing performance. Generally, we use DVS (Dynamic Voltage Scaling) technology now available in high-performance microprocessors to reduce power consumption during parallel application runs when peak CPU performance is not necessary due to load imbalance, communication delays, etc. We propose distributed performance-directed DVS scheduling strategies for use in scalable power-aware HPC clusters. By varying scheduling granularity we can obtain significant energy savings without increasing execution time (36% for FT from NAS PB). We created a software framework to implement and evaluate our various techniques and show performance-directed scheduling consistently saves more energy (nearly 25% for several codes) than comparable approaches with less impact on execution time (< 5%). Additionally, we illustrate the use of energy-delay products to automatically select distributed DVS schedules that meet users’ needs.
A framework for recognizing and executing verb phrases

Today, the physical capabilities of robots enable them to perform a wide variety of useful tasks for humans, making the need for simple and intuitive interaction between humans and robots readily apparent. Taking natural language as a key element of this interaction, we present a novel framework that enables robots to learn qualitative models of the semantics of an important class of verb phrases, such as “follow me to the kitchen,” and leverage these verb models to perform two tasks: Executing verb phrase commands, and recognizing when another agent has performed a given verb. This framework is based on a qualitative, relational model of verb semantics called the Verb Finite State Machine, or VFSM. We describe the VFSM in detail, motivating its design and providing a characterization of the class of verbs it can represent. The VFSM supports the recognition task natively, and we show how to combine it with modern planning techniques to support verb execution in complex environments. Grounded natural language semantics must be learned through interaction with humans, so we describe methods from learning VFSM verb models through natural interaction with a human teacher in the apprenticeship learning paradigm. To demonstrate the efficacy of our framework, we present empirical results showing rapid learning and high performance on both the recognition and execution tasks. In these experiments, the VFSM is able to consistently outperform a baseline method based on recent work in the verb learning literature. We close with a discussion of some of the current limitations of the framework, and a roadmap for future work in this area.
CONVERGENCE RATE OF MARKOV CHAINS ON

Acknowledgments. This work is part of my doctoral research done under the direction of Jeerey S. Rosenthal. I thank Peter Rosenthal for helpful discussions about the operator theory issues. Abstract. Quantitative geometric rates of convergence for reversible Markov chains are closely related to the spectral gap of the corresponding operator, which is hard to calculate for general state spaces. This article describes a geometric argument to give diierent types of bounds for spectral gaps of Markov chains on bounded subsets of R n and to compare the rates of convergence of diierent Markov chains.
Fundamental Characteristics of a Finger Module for Human-care Robot

This paper describes a new human-sized robot finger with passive compliance for multi-directional external forces and with the ability to estimate these forces. In order to achieve the above merits, silicone gum cushions (SGC) are used effectively at the finger-tip and the MP joint (metacarpal-phalangeal joint). These SGCs have a hollow shape and deform easily. The elastic deformation of SGC enables the robot to react softly to the external forces exerted from multiple directions. Furthermore, these forces can be inferred by measuring the pressure in the SGCs hollow. The basic characteristics of the module's passive compliance and its accuracy in sensing external forces are examined through several experiments. In addition, in order to confirm the possible application of this finger to a human-care services, we executed an experiment that simulates the motion of wiping a human body, taking advantage of the system's merits
Mathematical modeling and simulation of thick Ethernet

Thick Ethernet is a common LAN technology. In order to decrease response time and reduce network traffic, in this paper, we identified and established the mathematical model and computer simulation model of thick Ethernet In addition, we developed a software system which can simulate thick Ethernet and display the simulation results. Simulation results show that the influence of the sending probability for both the collisions and the average waiting time must be considered in order to improve thick Ethernet performance. The application demonstrates that the systematic design method is effective and feasible.
Querying Heterogeneous Document Stores

NoSQL document stores offer support to store documents described using various structures. Hence, the user has to formulate queries using the possible representations of the desired information from different schemas. In this paper, we propose a novel approach that enables querying operators over a collection of documents with structural heterogeneity. Our work introduces an automatic query rewriting mechanism based on combinations of elementary operators: project, restrict and aggregate. We generate a custom dictionary that tracks all representations for attributes used in the documents. Finally, we discuss the results of our approach with a series of experiments.
A multifaceted approach to smart energy city concept through using big data analytics

Improving the level of municipal energy efficiency is one of the most groundbreaking ways to increase energy safety and effect on climate change conditions. Nowadays energy consumption data are influenced by many factors. Traditional methods for data analysis are insufficient. This paper nibble at a subject of data mining technology and Big data analytics in order to estimate its applicability in municipal energy consumption data analysis. Recent investigation in this sphere are reviewed and summarized, the problems faced by data mining technology in the area of energy consumption data analysis are enumerated, and research points for future studies are given.
Rapid and Non-Enzymatic In Vitro Retrieval of Tumour Cells from Surgical Specimens

The study of tumourigenesis commonly involves the use of established cell lines or single cell suspensions of primary tumours. Standard methods for the generation of short-term tumour cell cultures include the disintegration of tissue based on enzymatic and mechanical stress. Here, we describe a simple and rapid method for the preparation of single cells from primary carcinomas, which is independent of enzymatic treatment and feeder cells. Tumour biopsies are processed to 1 mm3 cubes termed explants, which are cultured 1–3 days on agarose-coated well plates in specified medium. Through incisions generated in the explants, single cells are retrieved and collected from the culture supernatant and can be used for further analysis including in vitro and in vivo studies. Collected cells retain tumour-forming capacity in xenotransplantation assays, mimic the phenotype of the primary tumour, and facilitate the generation of cell lines.