Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Performance Evaluation of an Adaptive Resource Management Framework for Distributed Real-Time and Embedded Systems

Achieving end-to-end quality of service (QoS) in distributed real-time embedded (DRE) systems require QoS support and enforcement from their underlying operating platforms that integrates many real-time capabilities, such as QoS-enabled network protocols, real-time operating system scheduling mechanisms and policies, and real-time middleware services. As standards-based quality of service (QoS) enabled component middleware automates integration and configuration activities, it is increasingly being used as a platform for developing open DRE systems that execute in environments where operational conditions, input workload, and resource availability cannot be characterized accurately a priori. Although QoS-enabled component middleware offers many desirable features, however, it historically lacked the ability to allocate resources efficiently and enable the system to adapt to fluctuations in input workload, resource availability, and operating conditions. This paper presents three contributions to research on adaptive resource management for component-based open DRE systems. First, we describe the structure and functionality of the resource allocation and control engine (RACE), which is an open-source adaptive resource management framework built atop standards-based QoS-enabled component middleware. Second, we demonstrate and evaluate the effectiveness of RACE in the context of a representative open DRE system: NASA's magnetospheric multiscale mission system. Third, we present an empirical evaluation of RACE's scalability as the number of nodes and applications in a DRE system grows. Our results show that RACE is a scalable adaptive resource management framework and yields a predictable and high-performance system, even in the face of changing operational conditions and input workload.
Gaussian Process Based Dual Latent Function Approach to Ordinal Regression

The Gaussian process prior formulation introduced by us in this paper learns a mapping for ordinal regression task using dual sets of latent functions. In this formulation one set of latent functions are associated with data items and the other set of latent functions are associated with entities. An entity is a term introduced by us in this work to refer to the object responsible for assigning ordinal labels to data items. For example in the collaborative filtering problem an entity corresponds to a user. In our work we assume that the entities cluster, and we use latent functions to having a Gaussian process prior to model these clusters. Similarly we also assume that the data items cluster and use latent functions having a Gaussian process prior to model these clusters. We learn the parameters of these Gaussian processes in a discriminative learning framework by minimizing a loss function using an alternating minimization procedure. The purpose of introducing dual sets of latent functions is to overcome the deficiency in the predictive nature of discriminative models for ordinal regression tasks while learning from less training data unlike generative models which have a good performance even while learning from less training data. Thus we evaluate the performance of our model on two problems, collaborative filtering and image annotation, by comparing with well known baseline methods using a generative model approach so as to understand the efficacy of our model on less training data.
On the Optimal Fronthaul Compression and Decoding Strategies for Uplink Cloud Radio Access Networks

This paper investigates the compress-and-forward scheme for an uplink cloud radio access network (C-RAN) model, where multi-antenna base stations (BSs) are connected to a cloud-computing-based central processor (CP) via capacity-limited fronthaul links. The BSs compress the received signals with Wyner-Ziv coding and send the representation bits to the CP; the CP performs the decoding of all the users' messages. Under this setup, this paper makes progress toward the optimal structure of the fronthaul compression and CP decoding strategies for the compress-and-forward scheme in the C-RAN. On the CP decoding strategy design, this paper shows that under a sum fronthaul capacity constraint, a generalized successive decoding strategy of the quantization and user message codewords that allows arbitrary interleaved order at the CP achieves the same rate region as the optimal joint decoding. Furthermore, it is shown that a practical strategy of successively decoding the quantization codewords first, then the user messages, achieves the same maximum sum rate as joint decoding under individual fronthaul constraints. On the joint optimization of user transmission and BS quantization strategies, this paper shows that if the input distributions are assumed to be Gaussian, then under joint decoding, the optimal quantization scheme for maximizing the achievable rate region is Gaussian. Moreover, Gaussian input and Gaussian quantization with joint decoding achieve to within a constant gap of the capacity region of the Gaussian multiple-input multiple-output (MIMO) uplink C-RAN model. Finally, this paper addresses the computational aspect of optimizing uplink MIMO C-RAN by showing that under fixed Gaussian input, the sum rate maximization problem over the Gaussian quantization noise covariance matrices can be formulated as convex optimization problems, thereby facilitating its efficient solution.
Aging and negative priming: is ignored information inhibited or remembered?

We had younger and older adults complete two tasks that tested the attentional- and memory-based inhibition models of negative priming. One task violated May, Kane, & Hasher (1995, Psychological Bulletin, 118, 35-54) criteria for measuring just attentional inhibition, by including a repeated-target condition. The other task complied with these criteria and included a depth of processing manipulation, where participants selected prime targets based either on their letter-length (nonsemantic processing) or weight (semantic processing). On balance, results supported the memory model, because depth of processing clearly moderated younger adult negative priming, and older adults displayed negative priming only in the task satisfying the attentional-inhibition criteria (i.e., the depth of processing task). We conclude that memory factors moderate negative priming, and that May et al.'s criteria fail to predict when older adults will show the effect.
One new method for road data shape change detection

Similarity is a psychological cognition; this paper defines the Difference Distance and puts forward the Similarity Measuring Model for linear spatial data (SMM-L) based on the integration of the Distance View and the Feature Set View which are the views for similarity cognition. Based on the study of the relationship between the spatial data change and the similarity, a change detection algorithm for linear spatial data is developed, and a test on road data change detection is realized.
A Note on Fuzzy Soft Topological Spaces

The main aim of this paper is to give a characterization of the category of fuzzy soft topological spaces and their continuous mappings, denoted by FSTOP. For this reason, we construct the category of antichain soft topological spaces and their continuous mappings, denoted by ASTOP. Also, we show that the category FSTOP is isomorphic to the category ASTOP.
Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data

Deep learning (DL) is a powerful state-of-the-art technique for image processing including remote sensing (RS) images. This letter describes a multilevel DL architecture that targets land cover and crop type classification from multitemporal multisource satellite imagery. The pillars of the architecture are unsupervised neural network (NN) that is used for optical imagery segmentation and missing data restoration due to clouds and shadows, and an ensemble of supervised NNs. As basic supervised NN architecture, we use a traditional fully connected multilayer perceptron (MLP) and the most commonly used approach in RS community random forest, and compare them with convolutional NNs (CNNs). Experiments are carried out for the joint experiment of crop assessment and monitoring test site in Ukraine for classification of crops in a heterogeneous environment using nineteen multitemporal scenes acquired by Landsat-8 and Sentinel-1A RS satellites. The architecture with an ensemble of CNNs outperforms the one with MLPs allowing us to better discriminate certain summer crop types, in particular maize and soybeans, and yielding the target accuracies more than 85% for all major crops (wheat, maize, sunflower, soybeans, and sugar beet).
Mucosal immunogenicity of plant lectins in mice

The mucosal immunogenicity of a number of plant lectins with different sugar specificities was investigated in mice. Following intranasal (i.n.) or oral administration, the systemic and mucosal antibody responses elicited were compared with those induced by a potent mucosal immunogen (cholera toxin; CT) and a poorly immunogenic protein (ovalbumin; OVA). After three oral or i.n. doses of CT, high levels of specific serum antibodies were measured and specific IgA was detected in the serum, saliva, vaginal wash, nasal wash and gut wash of mice. Immunization with OVA elicited low titres of serum IgG but specific IgA was not detected in mucosal secretions. Both oral and i.n. delivery of all five plant lectins investigated [Viscum album (mistletoe lectin 1; ML‐1), Lycospersicum esculentum (tomato lectin; LEA), Phaseolus vulgaris (PHA), Triticum vulgaris (wheat germ agglutinin (WGA), Ulex europaeus I (UEA‐1)] stimulated the production of specific serum IgG and IgA antibody after three i.n. or oral doses. Immunization with ML‐1 induced high titres of serum IgG and IgA in addition to specific IgA in mucosal secretions. The response to orally delivered ML‐1 was comparable to that induced by CT, although a 10‐fold higher dose was administered. Immunization with LEA also induced high titres of serum IgG, particularly after i.n. delivery. Low specific IgA titres were also detected to LEA in mucosal secretions. Responses to PHA, WGA and UEA‐1 were measured at a relatively low level in the serum, and little or no specific mucosal IgA was detected.
Testability analysis based on structural and behavioral information

When VLSI circuits such as microprocessors are designed hierarchically, testability issues have to be considered simultaneously with functional specifications to reduce the testing complexity early in the design phase. Accurate testability measures are required to indicate the hard-to-test areas and can be used as a guidance for ATPG. This paper presents a new testability analysis technique operating at a high level using both circuit structural information and assembly-level instruction behavioral information. This testability analysis targets at the popular functional test generation and a modern high level ATPG methodology published in recent literature. The experimental results of testability measures as well as high level ATPG are presented to verify the effectiveness. <<ETX>>
Segmentation of Pulmonary Nodules Using Fuzzy Clustering Based on Coefficient of Curvature

Pulmonary nodules are potential manifestation of lung cancer. Accurate segmentation of juxta-vascular nodules and ground glass opacity (GGO) nodules are an important and active area of research in medical image processing. At present, the classical segmentation algorithm of pulmonary nodules can not accurately obtain the boundary information of pulmonary nodules. In order to solve the problem, a new segmentation algorithm of pulmonary nodules using Fuzzy clustering based on coefficient of curvature is proposed in this paper. Because the coefficient of curvature can effectively distinguish between pulmonary nodules and surrounding structures, and Fuzzy clustering algorithm is adopted, the boundary information of pulmonary nodules can be accurately obtained. In order to obtain initial segmentation results of pulmonary nodules, the expectation maximization algorithm is adopted. Experimental results show that the proposed algorithm can accurately obtain the boundary information of juxta-vascular nodules and GGO nodules.
Mountain farmers: moral economies of land & agricultural development in Arusha & Meru.

Part One Settlement of Mount Meru: mountain farmers - Meru and Chaga in the 17th-19th centuries Massai farmers - Arusha and pastoral Massai in the 18th-19th centuries. Part Two Colonialism and resistance under German rule: blood on the land - Talala and the Germans, 1881-96 conquest and colonization, 1896-1916 establishment of the mission, 1902-1916. Part Three British rule 1916-61: recolonization land, population and agricultural development Christians, coffee, culture and class. Part Four Politics of land and authority: struggles for the land - political and moral economies of land the politics of land and authority the Meru land case.
Different 8-Hydroxyquinolines Protect Models of TDP-43 Protein, α-Synuclein, and Polyglutamine Proteotoxicity through Distinct Mechanisms*

Background: Expressing TDP-43 in yeast mimics several aspects of human TDP-43-based neurodegenerative diseases. Results: A screen for compounds that rescued TDP-43 toxicity identified functionally distinct 8-hydroxyquinoline metal chelators. Conclusion: Different 8-OHQs exhibit distinct modes of action and implicate multiple metal-dependent protective mechanisms against TDP-43, α-synuclein, and polyglutamine toxicity. Significance: 8-Hydroxyquinolines may ultimately be tailored to modify diverse neurodegenerative diseases in man. No current therapies target the underlying cellular pathologies of age-related neurodegenerative diseases. Model organisms provide a platform for discovering compounds that protect against the toxic, misfolded proteins that initiate these diseases. One such protein, TDP-43, is implicated in multiple neurodegenerative diseases, including amyotrophic lateral sclerosis and frontotemporal lobar degeneration. In yeast, TDP-43 expression is toxic, and genetic modifiers first discovered in yeast have proven to modulate TDP-43 toxicity in both neurons and humans. Here, we describe a phenotypic screen for small molecules that reverse TDP-43 toxicity in yeast. One group of hit compounds was 8-hydroxyquinolines (8-OHQ), a class of clinically relevant bioactive metal chelators related to clioquinol. Surprisingly, in otherwise wild-type yeast cells, different 8-OHQs had selectivity for rescuing the distinct toxicities caused by the expression of TDP-43, α-synuclein, or polyglutamine proteins. In fact, each 8-OHQ synergized with the other, clearly establishing that they function in different ways. Comparative growth and molecular analyses also revealed that 8-OHQs have distinct metal chelation and ionophore activities. The diverse bioactivity of 8-OHQs indicates that altering different aspects of metal homeostasis and/or metalloprotein activity elicits distinct protective mechanisms against several neurotoxic proteins. Indeed, phase II clinical trials of an 8-OHQ has produced encouraging results in modifying Alzheimer disease. Our unbiased identification of 8-OHQs in a yeast TDP-43 toxicity model suggests that tailoring 8-OHQ activity to a particular neurodegenerative disease may be a viable therapeutic strategy.
Robust kernel-based regression

In this research, a robust optimization approach applied to support vector regression (SVR) is investigated. A novel kernel based-method is developed to address the problem of data uncertainty where each data point is inside a sphere. The model is called robust SVR. Computational results show that the resulting robust SVR model is better than traditional SVR in terms of robustness and generalization error.
Slices: a shape-proxy based on planar sections

Minimalist object representations or shape-proxies that spark and inspire human perception of shape remain an incompletely understood, yet powerful aspect of visual communication. We explore the use of planar sections, i.e., the contours of intersection of planes with a 3D object, for creating shape abstractions, motivated by their popularity in art and engineering. We first perform a user study to show that humans do define consistent and similar planar section proxies for common objects. Interestingly, we observe a strong correlation between user-defined planes and geometric features of objects. Further we show that the problem of finding the minimum set of planes that capture a set of 3D geometric shape features is both NP-hard and not always the proxy a user would pick. Guided by the principles inferred from our user study, we present an algorithm that progressively selects planes to maximize feature coverage, which in turn influence the selection of subsequent planes. The algorithmic framework easily incorporates various shape features, while their relative importance values are computed and validated from the user study data. We use our algorithm to compute planar slices for various objects, validate their utility towards object abstraction using a second user study, and conclude showing the potential applications of the extracted planar slice shape proxies.
Towards automatic discovery of agile gaits for quadrupedal robots

Developing control methods that allow legged robots to move with skill and agility remains one of the grand challenges in robotics. In order to achieve this ambitious goal, legged robots must possess a wide repertoire of motor skills. A scalable control architecture that can represent a variety of gaits in a unified manner is therefore desirable. Inspired by the motor learning principles observed in nature, we use an optimization approach to automatically discover and fine-tune parameters for agile gaits. The success of our approach is due to the controller parameterization we employ, which is compact yet flexible, therefore lending itself well to learning through repetition. We use our method to implement a flying trot, a bound and a pronking gait for StarlETH, a fully autonomous quadrupedal robot.
Genome Sequences of Mycobacteriophages Findley, Hurricane, and TBond007

ABSTRACT We report here the genome sequences of three newly isolated phages that infect Mycobacterium smegmatis mc2155. Phages Findley, Hurricane, and TBond007 were discovered in geographically distinct locations and are related to cluster K mycobacteriophages, with Findley being similar to subcluster K2 phages and Hurricane and TBond007 being similar to subcluster K3 phages.
Associative-Commutative Rewriting

We are currently extending the rewrite system laboratory REVE to handle associative-commutative operators. In particular, we are incorporating a set of rules for Boolean algebra that provides a refutationally-complete theorem prover and a new programming paradigm. To that end, we describe methods for proving termination of associative-commutative systems.
Object Tracking for Laparoscopic Surgery Using theAdaptive Mean-Shift Kalman Algorithm

this paper, we propose the adaptive mean-shift Kalman tracking algorithm based on the mean-shift algorithm and the Kalman filter for tracking a laparoscopic instrument in laparoscopic surgery. With an iterative update of the target candidate in the mean-shift process, the proposed algorithm has improved the tracking performance over a typical mean-shift algorithm. In addition, the Kalman filter is employed to enhance the chance of tracking accuracy, especially when the object disappears from the scene. We tested the tracking performance of our proposed algorithm through simulated videos and real laparoscopic surgery videos. From all experimental results, the proposed algorithm can locate the target object correctly even when the size and the shape of the target have been changed. In the difficult situation when the target is hiding behind an obstacle, this algorithm can still track the target object correctly after it comes out. In addition, the proposed algorithm can be applied to locate different types of laparoscopic instruments.
Evidence for a Binary Origin of the Young Planetary Nebula Hubble 12

Young planetary nebulae play an important role in stellar evolution when intermediate- to low-mass stars (0.8-8 M⊙) evolve from the proto-planetary nebula phase to the planetary nebula phase. Many young planetary nebulae display distinct bipolar structures as they evolve away from the proto-planetary nebula phase. One possible cause of their bipolarity could be a binary origin for their energy source. Here we report on our detailed investigation of the young planetary nebula Hubble 12, which is well known for its extended hourglass-like envelope. We present evidence with time-series photometric observations for the existence of an eclipsing binary at the center of Hubble 12. In addition, low-resolution spectra of the central source show absorption features such as CN, G band, and Mg b, which can be suggestive of a low-mass nature for the secondary component.
Efficient SAT-Based Mapping and Scheduling of Homogeneous Synchronous Dataflow Graphs for Throughput Optimization

As Moore's law comes to an end, multiprocessor systems are becoming ubiquitous in today's embedded systems design. In this paper, we address the problem of mapping a homogeneous synchronous dataflow (HSDF) graph onto a multiprocessor platform with the objective of maximizing system throughput. We present two optimization approaches based on branch-and-bound and SAT-solving to explore the design space of all possible actor-to-processor mappings and static order schedules on each processor. In the logic-based benders decomposition (LBBD) approach, we decompose the problem into a master problem of finding a feasible actor mapping and scheduling, and a sub-problem of deadlock-checking and throughput computation. In the integrated approach, we integrate branch-and-bound search into the SAT engine to achieve more effective search tree pruning and better scalability. Performance evaluation shows that the integrated approach outperforms the LBBD approach by a large margin.
Joint Conference on Neural Networks , Orlando , Florida , USA , August 12-17 , 2007 Information Theoretic Vector Quantization with Fixed Point Updates

In this paper, we revisit information theoretic vector quantization (ITVQ) algorithm introduced in [1] and make it practical. We derive a fixed point update rule to minimize the Cauchy-Schwartz(CS) pdf divergence between the set of codewords and the actual data. In doing so, we overcome two severe deficiencies of the previous gradient based method namely, the number of parameters to be optimized and slow convergence rate, thus making this algorithm more efficient and useful as a compression algorithm.