Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Objects Tracking Circuit using Particle Filters with Multiple Features

Object tracking is a challenging problem in a number of computer vision applications. A number of approaches have been proposed and implemented to track moving objects in image sequences. The particle filter, which recursively constructs the posterior probability distributions of the state space, is the most popular approach. In the particle filter, many kinds of features are used for tracking a moving object in cluttered environments. The specific feature for tracking is selected according to the type of moving object and condition of the tracking environment. Improved tracking performance is obtained by using multiple features concurrently. This paper proposes the particle filter algorithm, using multiple features, such as IFD (inter-frame difference) and gray level, to track a moving object. The IFD is used to detect an object and the gray level is used to distinguish the target object from other objects. This paper designs the circuit of the proposed algorithm using VHDL (VHSIC hardware description language) in an FPGA (field programmable gate array) for tracking without considerable computational cost, since the particle filter requests many computing powers to track objects in real-time. All functions of the proposed tracking system are implemented in an FPGA. A tracking system with this FPGA is implemented and the corresponding performance is measured
License plate recognition based on intrinsic image decomposition algorithm

Traditional methods of license plate location have a poor effect if they are applied into poor lighting conditions. This paper presents an intrinsic image decomposition algorithm to solve this problem. We extract out the reflection intrinsic image which is independent of light and then conduct license plate location. In addition, to work out the shortage of low recognition rate of the Chinese OCR, this paper proposes the use of R-SIFT feature matching method to authenticate vehicle. For every detecting car, we extract out its feature points using R-SIFT feature matching method and sent to various monitoring stations. When a vehicle image is shot, we first conduct license plate location, and then extract out R-SIFT feature points, finally match with photos in vehicle registration database. If the matched points exceed a set threshold, the system will ring and warn that it may be a suspect vehicle. Thus, we implement the license plate recognition.
Battery Equalization Using Bi-directional Cuk Converter in DCVM Operation

A systematic approach to the analysis and design of a bi-directional Cuk converter for the cell voltage balancing control of a series-connected battery string is presented in this paper. The proposed individual cell equalizers (ICE) are designed to operate at discontinuous-capacitor-voltage mode (DCVM) to achieve the zero-voltage turn-off switching (ZVS) for reducing the switching loss of the bi-directional DC/DC converters. Simulation and experimental results show that the proposed battery equalization scheme can not only enhance the bi-directional battery equalization performance, but also can reduce the switching loss during the equalization period. The switch power is significantly reduced by about 52.8% from MOSFETs and the equalization efficiency can be improved by 68-72% using the proposed DCVM ZVS battery equalizer under the specified cell equalization process
Parameter Estimation and Energy Minimization for Region-Based Semantic Segmentation

We consider the problem of parameter estimation and energy minimization for a region-based semantic segmentation model. The model divides the pixels of an image into non-overlapping connected regions, each of which is to a semantic class. In the context of energy minimization, the main problem we face is the large number of putative pixel-to-region assignments. We address this problem by designing an accurate linear programming based approach for selecting the best set of regions from a large dictionary. The dictionary is constructed by merging and intersecting segments obtained from multiple bottom-up over-segmentations. The linear program is solved efficiently using dual decomposition. In the context of parameter estimation, the main problem we face is the lack of fully supervised data. We address this issue by developing a principled framework for parameter estimation using diverse data. More precisely, we propose a latent structural support vector machine formulation, where the latent variables model any missing information in the human annotation. Of particular interest to us are three types of annotations: (i) images segmented using generic foreground or background classes; (ii) images with bounding boxes specified for objects; and (iii) images labeled to indicate the presence of a class. Using large, publicly available datasets we show that our methods are able to significantly improve the accuracy of the region-based model.
Alarms- Localization, Orientation, and Recognition

This paper documents a study at three dialysis departments on the use of different alarm systems. The design of the alarm systems is to some extent in line with the visions of augmented reality and ubiquitous computing. Our study has raised a range of questions which we think are relevant for the research on how to embed technology in users' environment. We will address how the properties of the specific alarm systems, concerted in the local physical environment of the dialysis department, affords the localisation, orientation and recognition of alarms. In this paper, localisation is about the ways in which the nurses exactly locate where the alarm comes from and orientation is how the nurses orientate themselves towards the alarm in a more imprecise way. Recognition is about how the nurses notice that there is an alarm in the first place and if there are multiple alarms.
Clustering-Based Ensembles as an Alternative to Stacking

One of the most popular techniques of generating classifier ensembles is known as stacking which is based on a meta-learning approach. In this paper, we introduce an alternative method to stacking which is based on cluster analysis. Similar to stacking, instances from a validation set are initially classified by all base classifiers. The output of each classifier is subsequently considered as a new attribute of the instance. Following this, a validation set is divided into clusters according to the new attributes and a small subset of the original attributes of the instances. For each cluster, we find its centroid and calculate its class label. The collection of centroids is considered as a meta-classifier. Experimental results show that the new method outperformed all benchmark methods, namely Majority Voting, Stacking J48, Stacking LR, AdaBoost J48, and Random Forest, in 12 out of 22 data sets. The proposed method has two advantageous properties: it is very robust to relatively small training sets and it can be applied in semi-supervised learning problems. We provide a theoretical investigation regarding the proposed method. This demonstrates that for the method to be successful, the base classifiers applied in the ensemble should have greater than 50% accuracy levels.
Relation between bid–ask spread, impact and volatility in order-driven markets

We show that the cost of market orders and the profit of infinitesimal market-making or -taking strategies can be expressed in terms of directly observable quantities, namely the spread and the lag-dependent impact function. Imposing that any market taking or liquidity providing strategies is at best marginally profitable, we obtain a linear relation between the bid–ask spread and the instantaneous impact of market orders, in good agreement with our empirical observations on electronic markets. We then use this relation to justify a strong, and hitherto unnoticed, empirical correlation between the spread and the volatility per trade, with R 2s exceeding 0.9. This correlation suggests both that the main determinant of the bid–ask spread is adverse selection, and that most of the volatility comes from trade impact. We argue that the role of the time-horizon appearing in the definition of costs is crucial and that long-range correlations in the order flow, overlooked in previous studies, must be carefully factored in. We find that the spread is significantly larger on the NYSE, a liquid market with specialists, where monopoly rents appear to be present.
A Novel Multi-Aperture Based Sun Sensor Based on a Fast Multi-Point MEANSHIFT (FMMS) Algorithm

With the current increased widespread interest in the development and applications of micro/nanosatellites, it was found that we needed to design a small high accuracy satellite attitude determination system, because the star trackers widely used in large satellites are large and heavy, and therefore not suitable for installation on micro/nanosatellites. A Sun sensor + magnetometer is proven to be a better alternative, but the conventional sun sensor has low accuracy, and cannot meet the requirements of the attitude determination systems of micro/nanosatellites, so the development of a small high accuracy sun sensor with high reliability is very significant. This paper presents a multi-aperture based sun sensor, which is composed of a micro-electro-mechanical system (MEMS) mask with 36 apertures and an active pixels sensor (APS) CMOS placed below the mask at a certain distance. A novel fast multi-point MEANSHIFT (FMMS) algorithm is proposed to improve the accuracy and reliability, the two key performance features, of an APS sun sensor. When the sunlight illuminates the sensor, a sun spot array image is formed on the APS detector. Then the sun angles can be derived by analyzing the aperture image location on the detector via the FMMS algorithm. With this system, the centroid accuracy of the sun image can reach 0.01 pixels, without increasing the weight and power consumption, even when some missing apertures and bad pixels appear on the detector due to aging of the devices and operation in a harsh space environment, while the pointing accuracy of the single-aperture sun sensor using the conventional correlation algorithm is only 0.05 pixels.
Strategic-Instrument Theory and the Use of Non-Authoritative Sources by Federal Judges: Explaining References to Law Review Articles

When do judges include references to non-authoritative sources in their opinions? Do strategic motivations account for the use of these sources? This study evaluates strategic explanations for citations to non-authoritative sources by examining the citation of law review articles by U.S. Courts of Appeals judges. It is hypothesized that judges are more likely to cite law review articles when they anticipate opposition from other actors who have power to limit or overturn their decisions. The study also controls for other potential explanations for citations to law review articles. The findings provide empirical support for the strategic model of judicial behavior, demonstrating that judges tend to cite more broadly in the face of conflict.
Creativity in Schools : Tensions and Dilemmas

The author considers a wide variety of issues and perspectives raised by a new attention on creativity. The book is divided into three sections that focus on: the challenges of fostering creativity in schools; the problems of separating creativity from values; and, the implications for liberal education. Questions raised and considered include: to what extent is creativity a tool; is creativity determined by its subject context; can the demise of creativity be attributed to the technicization of teaching; is there a universal concept of creativity or is it limited by its cultural specificity; how appropriate is the implication that creativity is a good thing for the economy, for society and for education, without reflection on the consequences (i.e. the "throw away" society); and how can teachers encourage students to evaluate the effect of their choices on others.
Weak spatial constancy in touch

We propose extending the concept of spatial constancy to haptic perception. In vision, spatial constancy refers to the conversion of retinotopic signals into spatiotopic representations, allowing the observer to perceive space independently of his or her own eye movements, or at least partly so. The problem would seem at least as important in haptic perception, where sensory surfaces undergo even more complex movements in space. Here we develop a methodology for studying haptic spatial constancy, which involves a tactile display mounted on a mobile platform, and which allows us to decouple movements of the sensory surface—in this case the fingertip—from movements of objects on the fingertip. Using this apparatus, we find evidence for only weak haptic spatial constancy.
Heart and Mind in Conflict: The Interplay of Affect and Cognition in Consumer Decision Making

This article examines how consumer decision making is influenced by automatically evoked task-induced affect and by cognitions that are generated in a more controlled manner on exposure to alternatives in a choice task. Across two experiments respondents chose between two alternatives: one (chocolate cake) associated with more intense positive affect but less favorable cognitions, compared to a second (fruit salad) associated with less favorable affect but more favorable cognitions. Findings from the two experiments suggest that if processing resources are limited, spontaneously evoked affective reactions rather than cognitions tend to have a greater impact on choice. As a result, the consumer is more likely to choose the alternative that is superior on the affective dimension but inferior on the cognitive dimension (e.g., chocolate cake). In contrast, when the availability of processing resources is high, cognitions related to the consequences of choosing the alternatives tend to have a bigger impact on choice compared to when the availability of these resources is low. As a result, the consumer is more likely to choose the alternative that is inferior on the affective dimension but superior on the cognitive dimension (e.g., fruit salad). The moderating roles of the mode of presentation of the alternatives and of a personality variable related to impulsivity are also reported. Copyright 1999 by the University of Chicago.
Fisher’s fundamental theorem of inclusive fitness and the change in fitness due to natural selection when conspecifics interact

Competition and cooperation is fundamental to evolution by natural selection, both in animals and plants. Here, I investigate the consequences of such interactions for response in fitness due to natural selection. I provide quantitative genetic expressions for heritable variance and response in fitness due to natural selection when conspecifics interact. Results show that interactions among conspecifics generate extra heritable variance in fitness, and that interacting with kin is the key to evolutionary success because it translates the extra heritable variance into response in fitness. This work also unifies Fisher’s fundamental theorem of natural selection (FTNS) and Hamilton’s inclusive fitness (IF). The FTNS implies that natural selection maximizes fitness, whereas Hamilton proposed maximization of IF. This work shows that the FTNS describes the increase in IF, rather than direct fitness, at a rate equal to the additive genetic variance in fitness. Thus, Hamilton’s IF and Fisher’s FTNS both describe the maximization of IF.
GENET: A Connectionist Architecture for Solving Constraint Satisfaction Problems by Iterative Improvement

New approaches to solving constraint satisfaction problems using iterative improvement techniques have been found to be successful on certain, very large problems such as the million queens. However, on highly constrained problems it is possible for these methods to get caught in local minima. In this paper we present GENET, a connectionist architecture for solving binary and general constraint satisfaction problems by iterative improvement. GENET incorporates a learning strategy to escape from local minima. Although GENET has been designed to be implemented on VLSI hardware, we present empirical evidence to show that even when simulated on a single processor GENET can outperform existing iterative improvement techniques on hard instances of certain constraint satisfaction problems.
Fluoride Increases Superoxide Production and Impairs the Respiratory Chain in ROS 17/2.8 Osteoblastic Cells

It is known that fluoride produces oxidative stress. Inflammation in bone tissue and an impairment of the respiratory chain of liver have been described in treatments with fluoride. Whether the impairment of the respiratory chain and oxidative stress are related is not known. The aim of this work was to study the effects of fluoride on the production of superoxide radical, the function of the respiratory chain and the increase in oxidative stress in ROS 17/2.8 osteoblastic cells. We measured the effect of fluoride (100 µM) on superoxide production, oxygen consumption, lipid peroxidation and antioxidant enzymes activities of cultured cells following the treatment with fluoride. Fluoride decreased oxygen consumption and increased superoxide production immediately after its addition. Furthermore, chronic treatment with fluoride increased oxidative stress status in osteoblastic cells. These results indicate that fluoride could damage bone tissue by inhibiting the respiratory chain, increasing the production of superoxide radicals and thus of the others reactive oxygen species.
Trendy bugs: Topic trends in the Android bug reports

Studying vast volumes of bug and issue discussions can give an understanding of what the community has been most concerned about, however the magnitude of documents can overload the analyst. We present an approach to analyze the development of the Android open source project by observing trends in the bug discussions in the Android open source project public issue tracker. This informs us of the features or parts of the project that are more problematic at any given point of time. In turn, this can be used to aid resource allocation (such as time and man power) to parts or features. We support these ideas by presenting the results of issue topic distributions over time using statistical analysis of the bug descriptions and comments for the Android open source project. Furthermore, we show relationships between those time distributions and major development releases of the Android OS.
Late Preterm Birth and Neurocognitive Performance in Late Adulthood: A Birth Cohort Study

OBJECTIVES: We studied if late preterm birth (34 weeks 0 days–36 weeks 6 days of gestation) is associated with performance on the Consortium to Establish a Registry for Alzheimer’s Disease Neuropsychological Battery (CERAD-NB) in late adulthood and if maximum attained lifetime education moderated these associations. METHODS: Participants were 919 Finnish men and women born between 1934 and 1944, who participated in the Helsinki Birth Cohort Study. They underwent the CERAD-NB at a mean age of 68.1 years. Data regarding gestational age (late preterm versus term) were extracted from hospital birth records, and educational attainment data were gathered from Statistics Finland. RESULTS: After adjustment for major confounders, those born late preterm scored lower on word list recognition (mean difference: –0.33 SD; P = .03) than those born at term. Among those who had attained a basic or upper secondary education, late preterm birth was associated with lower scores on word list recognition, constructional praxis, constructional praxis recall, clock drawing, Mini–Mental State Examination, and memory total and CERAD total 2 compound scores (mean differences: >0.40 SD; P values <.05), and had a 2.70 times higher risk of mild cognitive impairment (Mini–Mental State Examination score: <26 points) (P = .02). Among those with tertiary levels of education, late preterm birth was not associated with CERAD-NB scores. CONCLUSIONS: Our findings offer new insight into the lifelong consequences of late preterm birth, and they add late preterm birth as a novel risk factor to the list of neurocognitive impairment in late adulthood. Our findings also suggest that attained lifetime education may mitigate aging-related neurocognitive impairment, especially among those born late preterm.
Hyperspaces for Object Clustering and Approximate Matching in Peer-to-Peer Overlays

Existing distributed hash tables provide efficient mechanisms for storing and retrieving a data item based on an exact key, but are unsuitable when the search key is similar, but not identical, to the key used to store the data item. In this paper, we present a scalable and efficient peer-to-peer system with a new search primitive that can efficiently find the k data items with keys closest to the search key. The system works via a novel assignment of virtual coordinates to each object in a high-dimensional, synthetic space such that the proximity between two points in the coordinate space is correlated with the similarity between the strings that the points represent. We examine the feasibility of this approach for efficient, peer-to-peer search on inexact string keys, and show that the system provides a robust method to handle key perturbations that naturally occur in applications, such as file-sharing networks, where the query strings are provided by users.
Wavenet: A Wavelet-Based Approach to Monitor Changes on Data Distribution in Networks

A massive amount of data is available in distributed fashion on various networks, including Internet, peer-to-peer networks, and wireless sensor networks. Users are often interested in monitoring interesting patterns or abnormal events hidden in these data. Transferring all the raw data from each host node to a central coordinator for processing is costly and unnecessary. In this study, we investigate the problem of monitoring changes on the data distribution in the networks (MCDN). To address this problem, we propose a technique, called wavenet, by compressing the local item set in each host node into a compact yet accurate summary, called local wavelet, for communication with the coordinator. We also propose adaptive monitoring to address the issues of local wavelet propagation in wavenet. An extensive performance evaluation has been conducted to validate our proposal and demonstrates the efficiency of wavenet.
Localization and Mapping Using Only a Rotating FMCW Radar Sensor

Rotating radar sensors are perception systems rarely used in mobile robotics. This paper is concerned with the use of a mobile ground-based panoramic radar sensor which is able to deliver both distance and velocity of multiple targets in its surrounding. The consequence of using such a sensor in high speed robotics is the appearance of both geometric and Doppler velocity distortions in the collected data. These effects are, in the majority of studies, ignored or considered as noise and then corrected based on proprioceptive sensors or localization systems. Our purpose is to study and use data distortion and Doppler effect as sources of information in order to estimate the vehicle's displacement. The linear and angular velocities of the mobile robot are estimated by analyzing the distortion of the measurements provided by the panoramic Frequency Modulated Continuous Wave (FMCW) radar, called IMPALA. Without the use of any proprioceptive sensor, these estimates are then used to build the trajectory of the vehicle and the radar map of outdoor environments. In this paper, radar-only localization and mapping results are presented for a ground vehicle moving at high speed.