Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal tracing and replay for debugging shared-memory parallel programs

Execution replay is a crucial part of debugging. Because explicitly parallel shared-memory programs can be nondeterministic, a tool is required that traces executions so they can be replayed for debugging. We present an adaptive tracing strategy that is optimal and records the minimal number of shared-memory references required to exactly replay executions. Our algorithm makes runtime tracing decisions by detecting and tracing a certain type of race condition on-the-fly . Unlike past schemes, we make no assumptions about the execution’ s correctness (it need not be race free). Experiments show that only 0.01−2% of the shared-memory references are usually traced, a 2−4 order of magnitude reduction over past techniques which trace every access.
Cut-Set Bound 1 Upper Bounds on Relaying Rate

Though the unicast capacity of wireless networks is unknown, the upper bound on capacity is known. In [1], Cover et al defined the information theoretic cut-set bound which serves an upper bound for any multi-terminal communications. This is a natural extension of the Max-Flow Min-Cut theorem defined for multicommodity flow problems. In this section, we study the cut-set upper bounds for full-duplex relay networks [1] and half-duplex relay networks [2].
Blind qubit state disentanglement with Quantum processing: Principle, criterion and algorithm using measurements along two directions

In the framework of Blind Quantum Source Separation, we investigate the adaptation of a separating system which receives coupled quantum bit (qubit) states and processes them with quantum means in its feedforward path, to uncouple them. We propose the first separation principle which ensures that the output qubit states of this system are disentangled and that they restore the non-entangled source qubit states up to limited indeterminacies. This separation principle exploits measurements of output spin components along two directions and has some links with the non-quantum Independent Component Analysis (ICA) principle. It opens the way to various practical separation criteria and algorithms, some of which are described here.
Automatic Identification of English Collocation Errors Based on Dependency Relations

We present an English miscollocation identification system based on dependency relations drawn from the Stanford parser. We test our system against a subset of error-tagged Chinese Learner English Corpus (CLEC)and obtain an overall precision of 0.75. We describe some applications and limitations of our system and suggest directions for future research.
Determination of factors governing fibrillogenesis of polypeptide chains using lattice models

Using lattice models we explore the factors that determine the tendencies of polypeptide chains to aggregate by exhaustively sampling the sequence and conformational space. The morphologies of the fibril-like structures and the time scales ($\tau_{fib}$) for their formation depend on a subtle balance between hydrophobic and coulomb interactions. The extent of population of a fibril-prone structure in the spectrum of monomer conformations is the major determinant of $\tau_{fib}$. This observation is used to determine the aggregation-prone consensus sequences by exhaustively exploring the sequence space. Our results provide a basis for genome wide search of fragments that are aggregation prone.
Load balancing using time series analysis for soft real time systems with statistically periodic loads

This thesis provides design and analysis of techniques for global load balancing on ensemble architectures running soft-real-time object-oriented applications with statistically periodic loads. It focuses on estimating the instantaneous average load over all the processing elements. The major contribution is the use of explicit stochastic process models for both the loading and the averaging itself. These models are exploited via statistical time-series analysis and Bayesian inference to provide improved average load estimates, and thus to facilitate global load balancing. This thesis explains the distributed algorithms used and provides some optimality results. It also describes the algorithms' implementation and gives performance results from simulation. These results show that our techniques allow more accurate estimation of the global system loading, resulting in fewer object migrations than local methods. Our method is shown to provide superior performance, relative not only to static load-balancing schemes but also to many adaptive load-balancing methods. Results from a preliminary analysis of another system and from simulation with a synthetic load provide some evidence of more general applicability.
A watershed segmentation algorithm based on ridge detection and rapid region merging

Due to the effects of noise, quantization error and regional texture details, traditional watershed algorithm tended to lead to over-segmentation. Especially, when the ridges of image are of discontinuity and the edges are of fuzziness, the over-segmentation is significantly worse and the blob of image is easy to lose vital boundary. In this paper, in order to solve these drawbacks, a watershed segmentation algorithm based on ridge detection and rapid region merging was proposed. This algorithm reconstructed discontinuity ridge using ridge historical information which has reserved the information of image segmentation after opening and closing operation and completed pseudo-blobs marks based on Bayes rule, and achieved the mergence and elimination pseudo-blobs by the way of the rapid merging of the maximum similar region. Test results showed that the new image segmentation algorithm could solve the problems of over-segmentation and under-segmentation to the greatest extent, which could greatly increase the accuracy of segmentation of metallographic microgram.
Airway inflammation in waste handlers exposed to bioaerosols assessed by induced sputum

Work-associated lower airway inflammation in waste collectors was examined by induced sputum and correlated with the bioaerosol exposure. Organic waste collectors (n=25) underwent induced sputum collection and spirometry before work on Monday and the following Thursday. Total cells, cell differentials, interleukin (IL)-8 and eosinophilic cationic protein were determined. Personal full-shift exposure measurements were performed Monday, Tuesday and Wednesday and analysed for total bacteria, fungal spores, endotoxins and β(1–3)-glucans. The percentage of neutrophils (46–58%) and the IL-8 concentration (1.1–1.4 ng·mL−1) increased from Monday to Thursday. Forced expiratory volume in one second (FEV1) was significantly reduced on Thursday, and the decrease in FEV1/forced vital capacity correlated with the increase in the percentage of neutrophils. The median exposure to endotoxin (range 7–180 EU·m−3) and β(1–3)-glucan (range 5–220 ng·m−3) was correlated with the increase in IL-8. Bioaerosol exposure during waste collection induced an inflammatory response in the lower airways, characterised by neutrophils and interleukin-8 secretion, that influenced the lung function. The inflammatory response was related to microbial components in the bioaerosol and was more pronounced for endotoxin than β(1–3)-glucan exposure. No associations were found for mould spores or bacteria.
Effects of Artificial Flooding for Hydroelectric Development on the Population of Mansonia humeralis (Diptera: Culicidae) in the Paraná River, São Paulo, Brazil

The closure of two phases of the dam at the Porto Primavera Hydroelectric Plant on the Paraná River flooded a flawed system located in the Municipality of Presidente Epitácio, São Paulo state, favoring the proliferation of aquatic weeds. This study aimed to observe the population of Mansonia humeralis in the area, monitoring the richness, diversity, and dominance of this species both before and during different phases of reservoir flooding as well as evaluate its possible consequences concerning human and animal contact. Adult mosquitoes were collected monthly in the following periods: at the original level, after the first flood, and after the maximum level had been reached between 1997 and 2002. Collection methods used were an aspirator, a Shannon trap, and the Human Attractive Technique. A total of 30,723 mosquitoes were collected, Ma. humeralis accounting for 3.1% in the preflood phase, 59.6% in the intermediate, and 53.8% at maximum level. This species is relevant to public health, since the prospect of continued contact between Ma. humeralis and the human population enhances the dam's importance in the production of nuisance mosquitoes, possibly facilitating the transmission of arboviruses. Local authorities should continue to monitor culicid activity through sustainable entomological surveillance.
CLR: a collaborative location recommendation framework based on co-clustering

GPS data tracked on mobile devices contains rich information about human activities and preferences. In this paper, GPS data is used in location-based services (LBSs) to provide collaborative location recommendations. We observe that most existing LBSs provide location recommendations by clustering the User-Location matrix. Since the User-Location matrix created based on GPS data is huge, there are two major problems with these methods. First, the number of similar locations that need to be considered in computing the recommendations can be numerous. As a result, the identification of truly relevant locations from numerous candidates is challenging. Second, the clustering process on large matrix is time consuming. Thus, when new GPS data arrives, complete re-clustering of the whole matrix is infeasible. To tackle these two problems, we propose the Collaborative Location Recommendation (CLR) framework for location recommendation. By considering activities (i.e., temporal preferences) and different user classes (i.e., Pattern Users, Normal Users, and Travelers) in the recommendation process, CLR is capable of generating more precise and refined recommendations to the users compared to the existing methods. Moreover, CLR employs a dynamic clustering algorithm CADC to cluster the trajectory data into groups of similar users, similar activities and similar locations efficiently by supporting incremental update of the groups when new GPS trajectory data arrives. We evaluate CLR with a real-world GPS dataset, and confirm that the CLR framework provides more accurate location recommendations compared to the existing methods.
Experimental Study of High Performance Priority Queues David Lan Roche Supervising Professor :

The priority queue is a very important and widely used data structure in computer science, with a variety of applications including Dijkstra’s Single Source Shortest Path algorithm on sparse graph types. This study presents the experimental results of a variety of priority queues. The focus of the experiments is to measure the speed and performance of highly specialized priority queues in out-of-core and memory intensive situations. The priority queues are run in-core on small input sizes as well as out-of-core using large input sizes and restricted memory. The experiments compare a variety of well-known priority queue implementations such as Binary Heap with highly specialized implementations, such as 4-ary Aligned Heap, Chowdhury and Ramachandran’s Auxiliary Buffer Heap, and Fast Binary Heap. The experiments include Cache-Aware as well as Cache-Oblivious priority queues. The results indicate that the high-performance priority queues easily outperform traditional implementations. Also, overall the Auxiliary Buffer Heap has the best performance among the priority queues considered in most in-core and out-of-core situations.
A Decomposed Gradient-Based Approach for Generalized Platform Selection and Variant Design in Product Family

A core challenge in product family optimization is to jointly determine (1) the optimal selection of components to be shared across product variants and (2) the optimal values for design variables that define those components. Each of these subtasks depends on the other; however, due to the combinatorial nature and high computational cost of the joint problem, prior methods have forgone optimality of the full problem by fixing the platform a priori, restricting the platform configuration to all-or-none component sharing, or optimizing the joint problem in multiple stages. In this paper, we address these restrictions by (1) introducing an extended metric to account for generalized commonality, (2) relaxing the metric to the continuous space to enable gradient-based optimization, and (3) proposing a decomposed single-stage method for optimizing the joint problem. The approach is demonstrated on a family of ten bathroom scales. Results indicate that generalized commonality dramatically improves the quality of optimal solutions, and the decomposed single-stage approach offers substantial improvement in scalability and tractability of the joint problem, providing a practical tool for optimizing families consisting of many variants.
Application of Machine Learning Algorithms for Automatic Knowledge Acquisition and Readability Analysis Technical Report

A large knowledge base is a prerequisite for a lot of tasks in natural language processing (NLP). To build a handcrafted knowledge base, which is applicable to real world scenarios, a vast amount of effort is required. Furthermore, experts are needed with a strong background in linguistics, artificial intelligence and knowledge representation which may not be available to the extent necessary (knowledge acquisition bottleneck). For these reasons, machine learning techniques are widely used to construct a knowledge base automatically. Learning techniques are also relevant to many other areas, e.g., for readability analysis. In the latter area, a lot of work is needed to find the optimal settings for a readability formula and it usually involves a large amount of trial and error iterations. Thus, it is preferable to learn the necessary parameter settings automatically. This report investigates the application of machine learning techniques in both areas. Finally, several freely available machine learning tools, which can be employed to accomplish both tasks, are introduced and compared with each other.
MAX-MIN Ant System and local search for the traveling salesman problem

Ant System is a general purpose algorithm inspired by the study of the behavior of ant colonies. It is based on a cooperative search paradigm that is applicable to the solution of combinatorial optimization problems. We introduce MAX-MIN Ant System, an improved version of basic Ant System, and report our results for its application to symmetric and asymmetric instances of the well known traveling salesman problem. We show how MAX-MIN Ant System can be significantly improved, extending it with local search heuristics. Our results clearly show that MAX-MIN Ant System has the property of effectively guiding the local search heuristics towards promising regions of the search space by generating good initial tours.
Facial Image Reconstruction from a Corrupted Image by Support Vector Data Description

This paper proposes a method of automatic facial reconstruction from a facial image partially corrupted by noise or occlusion. There are two key features of this method; the one is the automatic extraction of the correspondences between the corrupted input face and reference face without additional manual tasks; the other is the reconstruction of the complete facial information from corrupted facial information based on these correspondences. In this paper, we propose a non-iterative approach that can match multiple feature points in order to obtain the correspondences between the input image and the reference face. Furthermore, shape and texture of the whole face are reconstructed by SVDD (Support Vector Data Description) from the partial correspondences obtained by matching. The experimental results of facial image reconstructions show that the proposed SVDD-based reconstruction method gives smaller reconstruction errors for a facial image corrupted by Gaussian noise and occlusion than the existing linear projection reconstruction method with a regulation factor. The proposed method also reduces the mean intensity error per pixel by an average of 35 %, especially in the reconstruction of a facial image corrupted by Gaussian noise.
CARIMBÓ - A Flexible Framework Proposal for the Return Path in Brazilian Digital Television

This paper proposes a framework to the return path on the Brazilian digital television. It presents an alternative way to achieve user based interactivity by utilizing the existing Wireless Distribution System resource (WDS) The utilization of this resource in areas with no infrastructure of telecommunications, like the Amazonian region, creates a perspective of integration for the native people to the digital inclusion programs organized by federal government. In order to evaluate the feasibility of this proposal the Network Simulator (NS) was used.
Classification-based close talk speech enhancement

This paper addresses the problem of close talk speech enhancement as a binary classification using dual microphones features in noisy and reverberant environments. In this work, we investigate a speech segregation framework, in which deep neural networks (DNN) are employed as a mechanism to find the robustness classifier from two microphones inputs. The paper reports the successful attempt to use dual microphones signals and energy difference features and monaural features as the segregation cues with this framework. Results with recording corpus show that robust performance can be achieved across a variety of multi location, noise types and reverberant conditions.
Adaptive Non-parametric Foreground Subtraction Using Rotation-invariant Local Frequency Pattern

Foreground subtraction in video surveillance is a very challenging task, especially in complex scenes. This paper presents a novel method for foreground object detection based on adaptive non-parametric model using rotation invariant local frequency pixel patterns. The aim of this method is to provide an efficient and robust foreground objects from videos which may contain illumination variations, camera gain and exposure variations, noises and dynamic background motions, etc. First, we propose an efficient scale-invariant local frequency pattern, which describes relations between adjacent pixels in frequency domain with illumination changes and shadows suppressed. Then, non-parametric model is used to estimate the probability distribution of pixel features in the process and an adaptive multi-modal framework is also introduced for learning. Extensive experimental evaluations on complex scenes of surveillance videos show that the method has obtained satisfactory results.
mgtA Expression Is Induced by Rob Overexpression and Mediates a Salmonella enterica Resistance Phenotype

ABSTRACT Rob is a member of the Sox/Mar subfamily of AraC/XylS-type transcriptional regulators implicated in bacterial multidrug, heavy metal, superoxide, and organic solvent resistance phenotypes. We demonstrate that, in Salmonella enterica, Rob overexpression upregulates the transcription of mgtA, which codes for the MgtA Mg2+ transporter. mgtA was previously characterized as a member of the Mg2+-modulated PhoPQ regulon. Here we demonstrate that Rob (but not its paralog protein SoxS or MarA) is able to induce mgtA transcription in a PhoP-independent fashion by binding to a conserved Mar/Sox/Rob motif localized downstream of the PhoP-box and overlapping the PhoP-dependent transcriptional start site. We found that Rob-induced mgtA expression confers low-level cyclohexane resistance on Salmonella. Because mgtA intactness is required for Rob-induced cyclohexane resistance, provided the AcrAB multidrug efflux pump can be expressed, we postulate that MgtA is involved in the AcrAB-mediated cyclohexane detoxification mechanism promoted by Rob in Salmonella.
Performance Enhancement of Routers in Networks-on-Chip Using Dynamic Virtual Channels Allocation

This study proposes a new router architecture to improve the performance of dynamic allocation of virtual channels. The proposed router is designed to reduce the hardware complexity and to improve power and area consumption, simultaneously. In the new structure of the proposed router, all of the controlling components have been implemented sequentially inside the allocator router modules. This optimizes communications between the controlling components and eliminates the most of hardware overloads of modular communications. Eliminating additional communications also reduces the hardware complexity. In order to show the validity of the proposed design in real hardware resources, the proposed router has been implemented onto a Field-Programmable Gate Array (FPGA). Since the implementation of a Network-on-Chip (NoC) requires certain amount of area on the chip, the suggested approach is also able to reduce the demand of hardware resources. In this method, the internal memory of the FPGA is used for implementing control units. This memory is faster and can be used with specific patterns. The use of the FPGA memory saves the hardware resources and allows the implementation of NoC based FPGA.
Different kinetic fates of apolipoproteins A-I and A-II from lymph chylomicra of nonhuman primates. Effect of saturated versus polyunsaturated dietary fat.

Monkeys fed polyunsaturated fat had significantly lower plasma cholesterol (186 +/- 18 vs. 276 +/- 31 mg/dl) and high density lipoprotein (HDL) mass concentrations (466 +/- 28 vs. 518 +/- 34 mg/dl) than did animals fed saturated fat. Plasma apoA-I concentrations also were significantly lower and apoA-II levels were generally, though not significantly, lower in the group fed polyunsaturated fat. In vivo reinjection studies, using thoracic duct lymph chylomicra labeled with (131)I and HDL labeled with (125)I, were done in order to study the mechanism of plasma HDL-lowering by polyunsaturated dietary fat. The peak specific activity (SA) of HDL apoA-I derived from (131)I-labeled chylomicra occurred at 3 hr after injection (172 +/- 11% of 1 min S.A.) and then an exponential decay occurred indicative of a precursor-product relationship between chylomicron apoA-I and HDL apoA-I. In contrast, HDL apoA-II derived from (131)I-labeled chylomicra had no early S.A. increase and began to die away immediately after injection. Labeled apoA-I from chylomicron and HDL origin had similar plasma fractional catabolic rates (FCR = 0.34-0.38 vs. 0.32-0.38 d(-1), respectively); apoA-II from chylomicron or HDL origin also had similar FCR (0.46-0.51 vs. 0.42-0.51 d(-1), respectively), which were significantly shorter than those for HDL apoA-I. There was a consistent trend toward a higher FCR for HDL apoA-I or A-II of polyunsaturated fat-fed recipients. Chylomicron apoA-I/triglyceride and apoA-II/triglyceride mass ratios were lower in polyunsaturated fat-fed animals (A-I/TG = 1.56 x 10(-3); A-II/TG = 1.47 x 10(-3)) vs. saturated fat-fed animals (A-I/TG = 2.58 x 10(-3); A-II/TG = 2.77 x 10(-3)). It was concluded that: (1) dietary polyunsaturated fat significantly lowered plasma cholesterol, HDL, and apoA-I concentrations relative to saturated fat; (2) the HDL-lowering effect of the dietary polyunsaturated fat may be due to the combined effects of decreased apoprotein production by the intestine and increased HDL catabolism; and (3) in the blood, chylomicron apoA-I and A-II differ in their metabolic fates. -Parks, J. S. , and L. L. Rudel. Different kinetic fates of apolipoproteins A-I and A-II from lymph chylomicra of nonhuman primates. Effect of saturated versus polyunsaturated dietary fat.