Miracle-dz/UniFAR-FTUs huggingface dataset:
A Perceptual Quality Metric with Internal Generative Mechanism

—Objective image quality assessment (IQA) aims to evaluate the image quality consistently with the human perception. Most of the existing perceptual IQA metrics cannot accurately represent the degradations from different types of distortion, e.g., existing structural similarity metrics perform well on content-dependent distortions while not as well as PSNR on content-independent distortions. In this paper, we integrate the merits of the existing IQA metrics with the guide of the recently revealed internal generative mechanism (IGM). The IGM indicates that the human visual system (HVS) actively predicts sensory information and tries to avoid the residual uncertainty for image perception and understanding. Inspired by the IGM theory, we adopt an AR prediction algorithm to decompose an input scene into two portions, the predicted portion with the predicted visual content and the disorderly portion with the residual content. Distortions on the predicted portion degrade the primary visual information and structural similarity procedures are employed to measure its degradation; distortions on the disorderly portion mainly change the uncertain information and the PNSR is employed for it. Finally, according to the noise energy deployment on the two portions, we combine the two evaluation results to acquire the overall quality score. Experimental results on six publicly available databases demonstrate that the proposed metric is comparable with the state-of-the-art quality metrics.
Convex Optimization for Linear Query Processing under Approximate Differential Privacy

Differential privacy enables organizations to collect accurate aggregates over sensitive data with strong, rigorous guarantees on individuals' privacy. Previous work has found that under differential privacy, computing multiple correlated aggregates as a batch, using an appropriate strategy, may yield higher accuracy than computing each of them independently. However, finding the best strategy that maximizes result accuracy is non-trivial, as it involves solving a complex constrained optimization program that appears to be non-convex. Hence, in the past much effort has been devoted in solving this non-convex optimization program. Existing approaches include various sophisticated heuristics and expensive numerical solutions. None of them, however, guarantees to find the optimal solution of this optimization problem. This paper points out that under (ε, ཬ)-differential privacy, the optimal solution of the above constrained optimization problem in search of a suitable strategy can be found, rather surprisingly, by solving a simple and elegant convex optimization program. Then, we propose an efficient algorithm based on Newton's method, which we prove to always converge to the optimal solution with linear global convergence rate and quadratic local convergence rate. Empirical evaluations demonstrate the accuracy and efficiency of the proposed solution.
An FPGA implementation of pipelined multiplicative division with IEEE Rounding

We report the results of an FPGA implementation of double precision floating-point division with IEEE rounding. We achieve a total latency (i.e., cycles times clock period) that is 2:6 times smaller than the latency of the fastest previous implementation on FPGAs. The amount of hardware, on the other hand, is comparable to commercial cores. The division circuit is based on Goldschmidt's algorithm. All IEEE rounding modes are supported and are implemented using dewpoint rounding. The precision of the initial approximation of the reciprocal is 14 bits. To save hardware and reduce the critical path, a half-sized 62x30 Booth radix-8 multiplier is used. This multiplier can receive both the multiplicand and the multiplier in carry-save representation. The division circuit is partitioned into four pipeline stages, has a latency of 11 cycles, and may restart a new double precision division operation after 8 cycles. Synthesis results of an implementation (not including the computation of the initial approximation of the reciprocal and the exponent path) guarantee a clock frequency of 131 MHz on an Altera Stratix II using 3592 ALMs. The implementation was successfully tested with over 10 million random vectors as well as over a million hard-to-round vectors.
EVOLUTION RESCUES FOLDING OF HIV-1 ENVELOPE GLYCOPROTEIN GP 120 LACKING A CONSERVED DISULFIDE BOND Running head : A disulfide bond in β-barrel folding

The majority of eukaryotic secretory and membrane proteins contain disulfide bonds, which are strongly conserved within protein families because of their crucial role in folding or function. The exact role of these disulfide bonds during folding is unclear. Using virus-driven evolution we generated a viral glycoprotein variant, which is functional despite the lack of an absolutely conserved disulfide bond that links two antiparallel β-strands in a 6-stranded β-barrel. Molecular dynamics simulations revealed that improved hydrogen bonding and side-chain packing led to stabilization of the β-barrel fold, implying that β-sheet preference co-directs glycoprotein folding in vivo. Our results show that the interactions between two β-strands that are important for the formation and/or integrity of the β-barrel, can be supported by either a disulfide bond or β-sheet favoring residues.
Network cell routing model for control of throughput and delay of air traffic

Attempts to manage air traffic by either decreasing delay or increasing flow can have the reverse effect on the other. In the National Airspace System (NAS), flights typically depart at their chosen times, and flow management techniques are implemented in the air in an effort to maximize flow, which could lead to delay for individual aircraft. Other areas of the world allow flights to depart only at predetermined slot times knowing that there is a clear unobstructed path, leading to minimal delays but possibly underutilizing airspace. We propose a new approach in leveraging the highway cell transmission model (CTM) to the airspace in the form of a multi-objective optimization that trades between maximizing throughput and minimizing delay. The model is posed as a multi-commodity traffic flow integer program where the constraints are relaxed slightly from the CTM to examine strategies for achieving optimal throughput and delay. In practice, the model is envisioned to initially run offline to determine a preliminary solution to the initial state of the system. Solutions for subsequent changes in actual state can be determined by running the model online for the incremental state change. Stochastic events such as convective weather clearing times or capacities can be included in the model to better see the benefits and impacts of pre-positioning traffic to take advantage of possible future clearing.
Analogue integrated circuit sizing with several optimization runs using heuristics for setting initial points

Circuit sizing (i.e., determining MOSFET channel widths and lengths which result in the most appropriate and robust circuit) is an optimization process. When it is completed, there always remains a dilemma; namely, whether a better solution exists. With different starting points one can arrive at different local minima. A heuristic process, consisting of many optimization runs starting from different initial points, is proposed. It tries to find another local minimum of the cost function in every run and thus reveals some additional information about the circuit. The mathematical background of the algorithm used is described. Finally, the heuristic algorithm is tested on some real integrated operating amplifier designs. The results show that from the cost-function point of view surprisingly many equivalent solutions exist.
Data Mining as Driven by Knowledge Management in Higher Education -persistence Clustering and Prediction Data Mining as Driven by Knowledge Management in Higher Education -persistence Clustering and Prediction

• What is data mining? • Where does data mining fit in the context of Knowledge Management? • What is a backpropogating neural net, entropy, decision tree? • How to transfer data mining techniques developed for the corporate world to higher education? • Which data mining model is the best for predicting a student's chance of persistence? Retention? Success? • Who are the ones that are transferring or dropping out? • Read on… EXECUTIVE SUMMARY This paper introduces a brand new and powerful decision support tool, data mining, in the context of knowledge management. Among other things, the most striking features of data mining techniques are clustering and prediction. The clustering aspect of data mining offers comprehensive characteristics analysis of students, while the predicting function estimates the likelihood for a variety of outcomes of them, such as transferability, persistence, retention and success in classes. Compared to traditional analytical studies that are often hindsight and aggregate, data mining is forward looking and is oriented to individual students. A real life project presents the work of data mining in predicting the possibility of returning to school for every student currently enrolled at a community college in Silicon Valley. The project applies neural network, C&RT and C5.0 to choose the best prediction followed by a clustering analysis using TwoStep. The list of students who are predicted as less likely to return to school by data mining is then turned over to faculty and management for direct or indirect intervention. The benefits of data mining are its ability to gain deeper understanding of the patterns previously unseen using current available reporting capabilities. Further, prediction from data mining allows the college an opportunity to act before a student drops out or to plan for resource allocation with confidence gained from knowing how many students will transfer or take a particular course.
A dynamic subgoal path planner for unpredictable environments

Although lots of planning algorithms have focused on the planning of fixed manipulators and mobile robots in moderate dynamic environments, seldom planning algorithms can be employed to deal with mobile agents in the presence of large scenario scales and unpredictable changing obstacles. Path planning for mobile robots in unpredictable environments would be an extreme challenge since computational complexity increase dramatically with high dimensionality, unpredictability and large scales. In this paper, a novel and real-time approach is proposed to solve this problem by generating subgoals dynamically according to time and potential values. This dynamic subgoal based approach includes two procedures, the subgoal generator and the inter-subgoal or inner replanner. On the one hand, a set of high-level subgoals is generated dynamically by an improved single shot strategy that could tailor itself adaptively. On the other hand, a roadmap is built during the preprocessing phase by employing a localized Dynamic Roadmap Mapping (local-DRM) for inter-subgoal replanning. Finally these two procedures will collaborate according to the potential field criterion to ensure completeness. Our approach can not only generate paths rapidly enough to satisfy the requirements of an anytime planer but also work for large scenario scales. Experimental results on different kinds of mobile agents, in large scenario scales and in the presence of unpredictable changing obstacles show that our approach can find out a collision free path on an average of 0.11s for a single planning, indicating an anytime planner.
Primary care budget holding in the United Kingdom National Health Service: learning from a decade of health service reform

The United Kingdom National Health Service (NHS) has experienced 10 years of primary care budget holding in a variety of forms. Half of all general practitioners had joined the GP fundholding scheme by 1997, and many others had joined broader GP commissioning groups, but fundholders controlled only about 20% of the budget for hospital and community health services. Research on fundholding and commissioning groups suggests that delegation of budgets produced some gains in the range and effectiveness of services, but also had significant management costs and inequities. From 1999, all primary care professionals joined Primary Care Groups, which are now becoming Primary Care Trusts (PCTs). PCTs will control three‐quarters of the healthcare budget and provide all primary and community services as well as commissioning hospital care. Control of a unified healthcare budget presents opportunities to improve quality, increase integration of services, reduce inequities and improve health. However, PCTs are threatened by a growing gap between capacity and expectations, and by continuing tension between devolution of power and increasingly prescriptive management by central government.
A scalable distributed IDS Architecture for High speed Networks

Summary As networks become faster there is a need for security analysis techniques that can keep up with the increased network throughput. Traditional centralized approaches to traffic analysis cannot scale with the increase of bandwidth advances mainly due to their memory and computational requirements. In the last few years a number of distributed architectures have already been proposed for dedicated network monitoring tasks but they are not scalable in the context of high speed networks. In this paper we present an optimized scalable distributed architecture which is about 10 times quicker than the centralized architecture. The solution is based on switch-based splitting approach that supports intrusion detection on high-speed links by balancing the traffic load among different sensors running Snort.
Evaluating a Multidimensional Reading Comprehension Program and Reconsidering the Lowly Reputation of Tests of Near–Transfer

We conducted a 14–week experimental study of 2 versions of a relatively comprehensive RC intervention that involved 50 classroom teachers, 15 tutors, and 116 children drawn in equal proportions from grades 3 and 5 in 13 schools in a large urban school district. Students were randomly assigned in equal numbers to the two tutoring conditions and a control group. Results indicated that students in the two tutored groups tended to perform comparably on all tests and to outperform controls (more so in grade 5 than grade 3) on near–transfer but not far–transfer measures of RC. This differential pattern of program effects for near– versus far–transfer measures raises questions about how tests of near–transfer and far–transfer are conventionally understood.
Development of Collective Intentionality

When and how does collective intentionality develop? Th is question has come into the focus of research in cognitive development in recent years with the establishment of collective intentionality as a phenomenon to be studied empirically by the cognitive sciences. In the last two decades, cognitive science has been investigating, from an ontogenetic point of view, how collective intentionality emerges and develops in humans and how it relates to other forms of intentionality, and from a comparative point of view, whether or to which degree it might mark one of the cognitive foundations of human uniqueness. Th e present chapter will give an overview of this research on the emergence and early development of diff erent forms of collective intentionality in human ontogeny, with an eye to the comparative question of which of these kinds of collective intentionality might be shared with other species and which might be uniquely human. Historically, the empirical interest of the cognitive sciences in collective intentionality arose as a consequence of the establishment of collective intentionality as a separate fi eld of study in the, by now, classical philosophical literature of the 1980s and 1990s (Bratman 1992; Gilbert 1990; Searle 1990; Tuomela & Miller 1988). Th e empirical phenomena studied and the questions asked have been drawing much inspiration from the diff erent philosophical accounts. Importantly, however, most empirical approaches start off from pre-theoretical folk notions of collective intentionality and thus remain theoretically neutral vis-à-vis the diff erent philosophical accounts of how to best explicate these folk notions—much like, for example, how cognitive science research of people’s “theory of mind” investigates how people ascribe mental states to others and themselves without thereby committing to any one specifi c position in the philosophy of mind. So, while the cognitive science of the development of collective intentionality does neither have to wait until an agreed upon philosophical analysis comes forth (if this ever happens) nor to commit to any one of the currently debated accounts, the empirical results of Development of Collective Intentionality
Green Software Engineering: The Curse of Methodology

Computer Science often seems distant from itsnatural science cousins, especially software engineering whichfeels closer to sociology and psychology than to physics. Physicalmeasurements are often rare in software engineering, except in afew niches. One such important niche is that of software energyconsumption, green mining, green IT, and sustainable computing, which all fall under the umbrella of green software engineering. With the physical measurement of energy consumption comesall of the limitations of measurement and experimentation thatexist in the natural sciences and engineering. Issues abound, fromattribution of energy use, isolation of components, to replicableexperiments. These get further complicated by cloud computingwhereby systems are virtualized and attribution of resource usageis a serious issue. Thus in this work we discuss the current state of softwareenergy consumption, and where will it go.
CAD Model Building from Multiple Range Images

Reverse engineering is a process by which a geometric model is created from sensor data. In this paper we show how to create a geometric model that can be read into a Computer Aided Design system using as input multiple range images of an object. Our reverse engineering method combines a number of new algorithms in a novel way to create the parametric patches that make up the model. The resulting system greatly accelerates the model building process, and makes it possible to guarantee that the model has a speci ed accuracy relative to the original range data.
pathClass: an R-package for integration of pathway knowledge into support vector machines for biomarker discovery

UNLABELLED Prognostic and diagnostic biomarker discovery is one of the key issues for a successful stratification of patients according to clinical risk factors. For this purpose, statistical classification methods, such as support vector machines (SVM), are frequently used tools. Different groups have recently shown that the usage of prior biological knowledge significantly improves the classification results in terms of accuracy as well as reproducibility and interpretability of gene lists. Here, we introduce pathClass, a collection of different SVM-based classification methods for improved gene selection and classfication performance. The methods contained in pathClass do not merely rely on gene expression data but also exploit the information that is carried in gene network data. AVAILABILITY pathClass is open source and freely available as an R-Package on the CRAN repository at http://cran.r-project.org.
Visual SLAM for 3D large-scale seabed acquisition employing underwater vehicles

This paper presents a novel technique to align partial 3D reconstructions of the seabed acquired by a stereo camera mounted on an autonomous underwater vehicle. Vehicle localization and seabed mapping is performed simultaneously by means of an Extended Kalman Filter. Passive landmarks are detected on the images and characterized considering 2D and 3D features. Landmarks are re-observed while the robot is navigating and data association becomes easier but robust. Once the survey is completed, vehicle trajectory is smoothed by a Rauch-Tung-Striebel filter obtaining an even better alignment of the 3D views and yet a large-scale acquisition of the seabed.
Merging Indiscriminable Diagnoses: An Approach Based on Automatic Domains Abstraction

Abstract : The paper presents an approach suitable for on-line diagnosis, which aims at automatically abstracting the domains of discrete variables in the model (i.e., behavioral modes of system components) in order to keep only those distinctions that are relevant given the available observations and their granularity. In particular the paper describes an algorithm which identifies indistinguishable behavioral modes by taking into account specific classes of available observations and derives an abstract model where such modes are merged and the domain model is revised accordingly. By considering increasingly restricted classes of available observations (and/or granularity of observations), a set of abstract models can be derived that can be exploited through model selection each time a new diagnostic problem has to be solved. The approach has been tested within the framework of a diagnostic agent for a space robotic arm, and experimental results showing the reduction in the number of diagnoses are reported.
DevOps: Making It Easy to Do the Right Thing

Wotif Group used DevOps principles to recover from the downward spiral of manual release activity that many IT departments face. Its approach involved the idea of "making it easy to do the right thing. " By defining the right thing (deployment standards) for development and operations teams and making it easy to adopt, Wotif drastically improved the average release cycle time. This article is part of a theme issue on DevOps.
CASITA: A Tool for Identifying Critical Optimization Targets in Distributed Heterogeneous Applications

Programming of high performance computing systems has become more complex over time. Several layers of parallelism need to be exploited to efficiently utilize the available resources. To support application developers and performance analysts we propose a technique for identifying the most performance critical optimization targets in distributed heterogeneous applications. We have developed CASITA, a tool which uses an execution trace and the knowledge about the programming models MPI, OpenMP and CUDA as well as their hierarchy among each other to build a distributed event dependency graph. After locating wait states in this graph, we detect their root cause and compute the critical path, an important property for performance optimizations. Compared to existing analysis approaches, we incorporate the hierarchy of multiple programming models and derive a metric from both the time an activity spends on the critical path and the waiting time it caused. For the purpose of visualization, CASITA enriches the input trace with additional counter information so that results can be inspected in the Vampir trace viewer.
No 769 December 2006 Multi-Product Firms and Trade Liberalization

This paper develops a general equilibrium model of multi-product firms and analyzes their behavior during trade liberalization. Firm productivity in a given product is modeled as a combination of firm-level “ability” and firmproduct-level “expertise”, both of which are stochastic and unknown prior to the firm’s payment of a sunk cost of entry. Higher firm-level ability raises a firm’s productivity across all products, which induces a positive correlation between a firm’s intensive (output per product) and extensive (number of products) margins. Trade liberalization fosters productivity growth within and across firms and in aggregate by inducing firms to shed marginally productive products and forcing the lowest-productivity firms to exit. Though exporters produce a smaller range of products after liberalization, they increase the share of products sold abroad as well as exports per product. All of these adjustments are shown to be relatively more pronounced in countries’ comparative advantage industries.