Miracle-dz/UniFAR-FTUs huggingface dataset:
RTS Noise and Dark Current White Defects Reduction Using Selective Averaging Based on a Multi-Aperture System

In extremely low-light conditions, random telegraph signal (RTS) noise and dark current white defects become visible. In this paper, a multi-aperture imaging system and selective averaging method which removes the RTS noise and the dark current white defects by minimizing the synthetic sensor noise at every pixel is proposed. In the multi-aperture imaging system, a very small synthetic F-number which is much smaller than 1.0 is achieved by increasing optical gain with multiple lenses. It is verified by simulation that the effective noise normalized by optical gain in the peak of noise histogram is reduced from 1.38e- to 0.48e- in a 3 × 3-aperture system using low-noise CMOS image sensors based on folding-integration and cyclic column ADCs. In the experiment, a prototype 3 × 3-aperture camera, where each aperture has 200 × 200 pixels and an imaging lens with a focal length of 3.0 mm and F-number of 3.0, is developed. Under a low-light condition, in which the maximum average signal is 11e- per aperture, the RTS noise and dark current white defects are removed and the peak signal-to-noise ratio (PSNR) of the image is increased by 6.3 dB.
An Efficient Approach for Distributed Regression Testing of Object Oriented Programs

Testing of software is one of the most important phases in the software development life cycle. Testing is performed to ensure the quality of the product developed. As the software system grows, it becomes more and more difficult to test the entire system. Thus, testing minor changes in the software became difficult. Regression testing was introduced to overcome this issue by testing only the changed portion of the system. This paper presents a novel approach in distributed regression testing of object oriented software. The approach presented in this paper makes use of data flow and control flow analysis of the source code to aid in distribution of the software during testing. The test cases are executed in parallel, minimizing the time required for the testing process. The proposed method reduces the time required by executing common portion of code only once. This method provides excellent code coverage.
The Surf Clam ITQ Management: An Evaluation

The U.S. surf clam offshore fishery was the first fishery managed with limited entry and individual transferable quota (ITQ) systems under the Magnuson Fishery Conservation and Management Act. A vessel limited entry system was implemented for a period from 1977 to 1990 and an individual transferable quota system from 1990 to present. In 1977, the surf clam offshore fishery was brought under federal management because of over-exploitation and nature disaster. The surf clam biomass had declined to a historic low after a period of high exploitation in early 1970's and new capital and vessels yet continued to enter into the fishery in spite of the low biomass. This over-exploitation condition was aggravated by an anoxic habitat condition off the New Jersey coast which was estimated to have destroyed 25% of the New Jersey offshore fishery in the summer of 1976 and to have ruined almost 70% of the fishery by the fall of that year. The 1977 federal surf clam management system established a vessel limited entry program consisting of a vessel moratorium program, an annual fishery catch quota, a limitation of vessel fishing hours, catch logbooks and vessel permits. Other measures such as minimum clam size were added in later years. Under the vessel moratorium, only vessels that directed their fishing on surf clams between November 1976 and November 1977, were allowed to fish for surf clams with provisions to qualify surf clam vessels that were then under construction. The fishery quota was established and set within a range between 1.8 and 2.9 million bushels for the MidAtlantic area and between 0.025 and 0.100 million bushels for the New England area. Allowable fishing time was specified to maximize fishing seasons. However, the allowable fishing time was steadily shortened due to continuous increases in the fishing power. When it was first instituted in 1977, the vessel moratorium program under the limited entry system was authorized only for one year with an intent that the program would be replaced with an alternative one such as a stock certificate program. However, the moratorium program was extended to 1990 while an alternative program was under development. During that time, the allowable fishing time had been reduced substantially to a historic low: A surf clam vessel was only permitted to fish six hours every other week by 1990. The low allowable fishing time led to low capacity utilization of fishing vessels and resulted in low economic efficiency of fishing firms. Under the moratorium, the administration of the fishing
Moral dilemmas and moral principles: When emotion and cognition unite

Traditional studies on moral judgement used resolutions of moral dilemmas that were framed in terms of acceptability of the consequentialist action promoting a greater good, thus overlooking the deontological implications (choices cannot be justified by their consequences). Recently, some authors have suggested a parallelism between automatic, unreflective emotional responses and deontological moral judgements. In this study, we developed a novel experimental paradigm in which participants were required to choose between two resolutions of a moral dilemma (consequentialist and deontological). To assess whether emotions are engaged in each of the two resolutions, we asked participants to evaluate their emotional experience through the ratings of valence and arousal. Results showed that emotion is involved not only in deontological but also in consequentialist resolutions. Moreover, response times pointed out a different interplay between emotion and cognition in determining a conflict in the dilemma's resolution. In particular, when people were faced with trolley-like dilemmas we found that decisions leading to deontological resolutions were slower than decisions leading to consequentialist resolutions. We propose that this finding reflects the special (but not accepted) permission provided by the doctrine of the double effect for incidentally causing death for the sake of a good end.
Stepwise Log Summarizing Method for Real-Time Network Event Handling

In this paper, we propose a log summarizing method to help a network manager to recognize and handle a network event. Our method can accurately extract only necessary information in real-time by stepwise information extraction technique. This feature can help the network manager to handle an event quickly with less management burden. With our experimental results, we show that our method has the ability to remove more than 60% - 90% unnecessary records, without losing any necessary information. Additionally, our method can process a log containing massive records in real-time.
An alternative way of drawing

Current object-oriented drawing programs have an established wayof drawing in which the shape of an object is controlled bymanipulating control points. While the control points are intuitivein their basic use, it is not clear whether they make more complexdrawing tasks manageable for the average user. In this paper wedescribe an alternative way of drawing and editing a drawing usingnew direct manipulation tools. Our approach resembles sculpting intwo dimensions: the user begins with a large block and usesdifferent tools to give it the desired shape. We also present auser evaluation in which the users could try our new tools andcompare them to their previous experience of control points, Theusers claimed to understand the operations better with our toolsthan if they had needed to use curves and control points. However,our tools were better suited for sketching the artwork than formaking very detailed drawings.
Spatial relation extraction using relational learning

The automatic extraction of spatial information is a challenging and novel task with many applications. We motivate our definition of this task and formulate it as an information extraction step prior to mapping to spatial semantics. Each sentence gives rise to several spatial relations between words representing landmarks, trajectors and spatial indicators. Learning to extract such spatial relations can be formulated as a typical relational classification problem, for which we employ the recently introduced kLog framework. We discuss modeling and representation, and show experimental results.
VINCA4Science: A Personal Workflow System for e-Science

The uncertain and diversified nature of scientific research often prohibits scientists from pre-defining a common full-fledged workflow. It would help if end users are allowed to individually construct and execute personal workflows on the basis of a common skeleton. In such a personal workflow, scientists can do experiments in a "trail-and-error" manner, and keep provenance information for future analysis and reuse. Driven by these wishes, we proposed an approach to personal workflow and developed a supporting system called VINCA4Science. A synthesized view of local and networked resources is conceptualized, and a knowledge- based service recommendation mechanism is realized for guiding workflow construction. Tryout applications in bioinformatics research show that VINCA4Science helps to improve programming efficiency and quality, provide flexible management control, and therefore improve scientific productivity.
Toolkits for Idea Competitions: A Novel Method to Integrate Users in New Product Development

Research has shown that many innovations originate not in the manufacturer but the user domain. Internet-based toolkits for idea competitions (TIC) are a novel way for manufacturers to access innovative ideas and solutions from users. Idea competitions build on the nature of competition as a means to encourage users to participate at an open innovation process, to inspire their creativity, and to increase the quality of the submissions. When the contest ends, submissions are evaluated by an expert panel. Users whose submissions score highest receive an award from the manufacturer, which is often granted in exchange for the right to exploit the solution in its domain. Following the idea of evolutionary prototyping, we developed a TIC in cooperation with a manufacturer of sports goods. The TIC was launched as a pilot in one of the company's markets. Submissions were evaluated using the consensual assessment technique. The evaluation of this study provides suggestions for further research, but also implications for managers willing to explore TIC in their organization.
Head Gimbal Assembly circuit with vision technique and Fuzzy C-Means Clustering

In Hard Disk Drive (HDD) Industry, the automation system is the one key for manufacturing process. Head Gimbal Assembly (HGA) is a part of HDD which have the reader and writer circuit. The HGA circuit is very important for read/write process. This research proposes the new vision technique and clustering by Fuzzy C-Means algorithm for HGA circuit inspection in 3 groups. HGA circuits in 3 groups are good, bridging, and missing group. The bridging and missing groups are the defect group. Blob analysis is the one of vision technique that it can measure the properties of image. The measurement properties from blob analysis are used in clustering technique. Fuzzy C-Means Clustering is the clustering technique which is grouped the measurement data into the cluster group based on the natural grouping of data. From the experiment results of this research, the clustering performance from Fuzzy C-Means Clustering is 99.11% accuracy based on the measurement properties in blob analysis with 225 samples.
Discussion of “ Are Banks Too Big to Fail ? ”

Chen Zhou’s paper is quite ambitious. Not only is it focused on developing and comparing measures of systemic risk (abbreviated by SR in the rest of this discussion); it also tries to clarify whether and to what extent bank size triggers SR. The practical relevance of this research is obvious, as there is an urgent need for simple and economically intuitive SR indicators for policymakers (governments, central banks, regulatory authorities). During non-crisis times, it may be desirable for policymakers to monitor whether SR remains below some level that is deemed to be critical; SR may also be used as a criterion to judge whether a distressed bank is sufficiently important from a systemic point of view to justify its bailout. Although one can imagine that structural reforms in the financial sector can reduce part of the existing SR potential, it will probably never be completely wiped out in the future, which implies that SR monitoring and measuring will remain relevant. The size-SR relation that constitutes the second theme of the paper seems to be taken for granted nowadays by governments or central banks that often made liquidity provisions conditional on breaking up financial institutions into smaller pieces.1 However, are bigger banks necessarily fueling SR? This is not obvious. According to finance theory, SR is either induced by “direct” channels via interbank market linkages or “indirect” channels such as similar portfolio holdings in bank balance sheets. However, bigger banks are not necessarily more interconnected with the rest of the financial system, nor do they necessarily exhibit more diversified portfolio holdings. The paper—although mainly empirically inclined—actually contains a parsimonious model of bank interdependence that shows that there may be a relation between size and SR. In the end, however, whether the size-SR relation exists or not remains an empirical issue.
Learning to Suppress SIS Epidemics in Networks

In this paper, we introduce the class of priority planning strategies for suppressing SIS epidemics taking place in a network. This is performed via dynamic allocation of treatment resources with limited efficiency to the infected nodes, according to a precomputed priority-order. Using recent theoretical results that highlight the role of the maxcut of a node ordering and the extinction time of an epidemic, we propose a simple and efficient strategy called MaxCut Minimization (MCM) that outperforms competing state-of-the-art strategies in simulated epidemic scenarios.
Fault-secure scheduling of arbitrary task graphs to multiprocessor systems

Proposes new scheduling algorithms to achieve fault security in multiprocessor systems. We consider the scheduling of parallel programs represented by directed acyclic graphs with arbitrary computation and communication costs. A schedule is said to be 1-fault-secure if the system either produces correct output for a parallel program or it detects the presence of any single fault in the system. Although several 1-fault-secure scheduling algorithms have been proposed so far, they can all only be applied to a class of tree-structured task graphs with a uniform computation cost. In contrast, the proposed algorithms can generate a 1-fault-secure schedule for any given task graph with arbitrary computation costs. Applying the new algorithms to two kinds of practical task graphs (Gaussian elimination and LU-decomposition), we conduct simulations. Experimental results show that the proposed algorithms achieves 1-fault security at the cost of a small increase in schedule length.
Automatic Generation of Model-to-Model Transformations from Rule-Based Speciﬁcations of Operational Semantics

In this paper we present a new approach for the automatic generation of model-to-model transformations given a description of the operational semantics of the source language by means of a graph grammar. The approach is oriented to the design of transformations from Domain-Speciﬁc Visual Languages (DSVLs) into semantic domains with an explicit notion of transition, such as Petri nets. We illustrate these techniques with a DSVL in the domain of production systems, for which we generate a transformation into place/transition Petri nets starting from a rule-based description of the DSVL’s operational semantics.
Improving person re-identification by viewpoint cues

Re-identifying people in a network of cameras requires an invariant human representation. State of the art algorithms are likely to fail in real-world scenarios due to serious perspective changes. Most of existing approaches focus on invariant and discriminative features, while ignoring the body alignment issue. In this paper we propose 3 methods for improving the performance of person re-identification. We focus on eliminating perspective distortions by using 3D scene information. Perspective changes are minimized by affine transformations of cropped images containing the target (1). Further we estimate the human pose for (2) clustering data from a video stream and (3) weighting image features. The pose is estimated using 3D scene information and motion of the target. We validated our approach on a publicly available dataset with a network of 8 cameras. The results demonstrated significant increase in the re-identification performance over the state of the art.
NeatSqueak on Wheels: Neural Networks Applied to Movement Optimization

In CiberRato competitions, deliberative agent implementations commonly use path planning algorithms. This creates the need for a path following module, which would control the mouse motors and drive him through a set of calculated waypoints. A common approach is manually deﬁning a decision tree, where the threshold values and motor outputs are empirically determined, obtaining a sub-optimal implementation. We’ve addressed this problem using optimization techniques based on unsupervised learning. A neural network based solution was developed through the use of genetic algorithms. Comparative data supporting that this approach surpasses manual decision tree implementations is presented for waypoint following behaviours. The results achieved enabled us to conclude that the development platform may be a valuable tool for CiberRato competition participants.
Angiogenesis inhibitors for the treatment of small cell lung cancer (SCLC)

Background: This study aimed to assess the effectiveness and safety of angiogenesis inhibitors for the treatment of patients with small cell lung cancer (SCLC) via meta-analysis. Methods: Electronic databases including PubMed, Embase, and Cochrane Library were searched to look for eligible studies through February 1, 2016. RCTs comprising angiogenesis inhibitors and nonangiogenesis inhibitors for SCLC patients were investigated. The extracted data including overall survival (OS), progression-free survival (PFS), and objective response rate (ORR) were summarized. In addition, the common adverse events (AEs) were also explored. Results: There were 7 phase II/III RCTs, encompassing 1322 SCLC patients eligible for meta-analysis. In comparison to nonangiogenesis inhibitors, angiogenesis inhibitors treatment was not associated with improvement of PFS [HR = 0.87, 95% CI (0.74–1.02), P = 0.09), OS [HR = 0.99, 95% CI (0.88–1.12), P = 0.91], or ORR [OR = 1.12, 95% CI (0.85–1.47), P = 0.41). Also, there was no improvement in 1-year survival rate [OR = 0.96, 95% CI (0.74–1.19), P = 0.63)], 2-year survival rate [OR = 1.00, 95% CI (0.66–1.51), P = 1.00)] or 1-year progression-free survival rates [OR = 0.95, 95% CI (0.69–1.31), P = 0.76)]. However, from subgroup analyses, it was observed that angiogenesis inhibitors improved ORR [HR = 1.66 (95% CI 1.02–2.71), P = 0.04] in phase II studies and bevacizumab improved PFS [HR = 0.73 (95% CI 0.42–0.97), P = 0.04]. It is important to note that angiogenesis inhibitors reduced emesis [OR = 0.38, 95% CI (0.17–0.85), P = 0.02], but increased incidence of constipation [OR = 4.02, 95% CI (2.14–7.55), P < 0.0001) and embolism [OR = 2.24, 95% CI (1.45–3.47), P = 0.0003). Conclusion: Adding angiogenesis inhibitors to chemotherapy did not improve PFS, OS, ORR, 1-year survival rate, 2-year survival rate or 1-year progression-free survival rate for SCLC. However, subgroup analysis revealed that bevacizumab enhanced PFS. Angiogenesis inhibitors also had a high incidence of constipation and embolism.
STDF model based maritime situation assessments

The state transition data fusion (STDF) model is an extension of the dominant sensor fusion paradigm to provide a unification of both sensor and higher-level fusion. Maritime domain awareness (MDA) is the problem of situation awareness in the maritime domain. This paper outlines an application of the STDF model to perform automated situation assessments for an aspect of MDA.
Reading Comprehension of Machine Translation Output: What Makes for a Better Read?

This paper reports on a pilot experiment that compares two different machine translation (MT) paradigms in reading comprehension tests. To explore a suitable methodology, we set up a pilot experiment with a group of six users (with English, Spanish and Simplified Chinese languages) using an English Language Testing System (IELTS), and an eye-tracker. The users were asked to read three texts in their native language: either the original English text (for the English speakers) or the machine-translated text (for the Spanish and Simplified Chinese speakers). The original texts were machine-translated via two MT systems: neural (NMT) and statistical (SMT). The users were also asked to rank satisfaction statements on a 3-point scale after reading each text and answering the respective comprehension questions. After all tasks were completed, a post-task retrospective interview took place to gather qualitative data. The findings suggest that the users from the target languages completed more tasks in less time with a higher level of satisfaction when using translations from the NMT system.
Feature Tracking in Images and Video

Feature tracking is needed for many applications, for instance: 3d scene reconstruction, real and synthetic scene fusion, advanced video compression, super-resolution images creation and many others. In this paper we describe how we go about feature point tracking. We also describe feature point filtering, since some of the tracked points are actually outliers.
TECHNOLOGY Effect of Seasonal Variation on Metal Speciation in Leachate from a Thermal Power Plant Ash Pond: Impact on Ground Waters

The concentration and extent of speciation of three environmentally important heavy metals, viz., Cd, Cu and Zn in the form of free ions and labile complex (C+F), slowly labile complex (CS1) and stable complex (CS) has been investigated in terms of percentage (%) in the Leachates from an ash pond of a 720mw thermal power plant in Delhi. The seasonal variations also have great infl uence on the total concentrations and speciation of these heavy metals in the ground water as well as in the leacha tes. The total concentration and speciation of thes e heavy metals is influenced by the physico-chemical parameters of th e water column and also on the seasonal variations. For seasonal variation, the study has been carried out on the mo nsoon(MON) period (June, July and August), pre-monsoon(PRE) period (March, April and May), Post monsoon(POS) period (September, October and November) and the winter (WIN) season (December, January and February). Leachates containing different species of these heavy m etals and other contaminants may have a deleterious effect on the receiving waters of the river Yamuna. Lysimete r studies simulating the generation of ash pond leachates hav e been conducted to ascertain the seasonal variatio n on leachability of different metal species. This may i mpart metal toxicity to the water body. Estimation of the speciation of these three heavy metals in the leach ates and ground water, a picture of heavy metal dis tribution and the nature or extent of the speciation may be obtai ned.