Assessing and Remedying Coverage for a Given Dataset

Data analysis impacts virtually every aspect of our society today. Often, this analysis is performed on an existing dataset, possibly collected through a process that the data scientists had limited control over. The existing data analyzed may not include the complete universe, but it is expected to cover the diversity of items in the universe. Lack of adequate coverage in the dataset can result in undesirable outcomes such as biased decisions and algorithmic racism, as well as creating vulnerabilities such as opening up room for adversarial attacks. In this paper, we assess the coverage of a given dataset over multiple categorical attributes. We first provide efficient techniques for traversing the combinatorial explosion of value combinations to identify any regions of attribute space not adequately covered by the data. Then, we determine the least amount of additional data that must be obtained to resolve this lack of adequate coverage. We confirm the value of our proposal through both theoretical analyses and comprehensive experiments on real data.Comment: in ICDE 201

RRR: Rank-Regret Representative

Selecting the best items in a dataset is a common task in data exploration. However, the concept of "best" lies in the eyes of the beholder: different users may consider different attributes more important, and hence arrive at different rankings. Nevertheless, one can remove "dominated" items and create a "representative" subset of the data set, comprising the "best items" in it. A Pareto-optimal representative is guaranteed to contain the best item of each possible ranking, but it can be almost as big as the full data. Representative can be found if we relax the requirement to include the best item for every possible user, and instead just limit the users' "regret". Existing work defines regret as the loss in score by limiting consideration to the representative instead of the full data set, for any chosen ranking function. However, the score is often not a meaningful number and users may not understand its absolute value. Sometimes small ranges in score can include large fractions of the data set. In contrast, users do understand the notion of rank ordering. Therefore, alternatively, we consider the position of the items in the ranked list for defining the regret and propose the {\em rank-regret representative} as the minimal subset of the data containing at least one of the top-$k$ of any possible ranking function. This problem is NP-complete. We use the geometric interpretation of items to bound their ranks on ranges of functions and to utilize combinatorial geometry notions for developing effective and efficient approximation algorithms for the problem. Experiments on real datasets demonstrate that we can efficiently find small subsets with small rank-regrets

Responsible Scoring Mechanisms Through Function Sampling

Human decision-makers often receive assistance from data-driven algorithmic systems that provide a score for evaluating objects, including individuals. The scores are generated by a function (mechanism) that takes a set of features as input and generates a score.The scoring functions are either machine-learned or human-designed and can be used for different decision purposes such as ranking or classification. Given the potential impact of these scoring mechanisms on individuals' lives and on society, it is important to make sure these scores are computed responsibly. Hence we need tools for responsible scoring mechanism design. In this paper, focusing on linear scoring functions, we highlight the importance of unbiased function sampling and perturbation in the function space for devising such tools. We provide unbiased samplers for the entire function space, as well as a $\theta$-vicinity around a given function. We then illustrate the value of these samplers for designing effective algorithms in three diverse problem scenarios in the context of ranking. Finally, as a fundamental method for designing responsible scoring mechanisms, we propose a novel approach for approximating the construction of the arrangement of hyperplanes. Despite the exponential complexity of an arrangement in the number of dimensions, using function sampling, our algorithm is linear in the number of samples and hyperplanes, and independent of the number of dimensions

Spin Filtering and Entanglement Swapping through Coherent Evolution of a Single Quantum Dot

We exploit the non-dissipative dynamics of a pair of electrons in a large square quantum dot to perform singlet-triplet spin measurement through a single charge detection and show how this may be used for entanglement swapping and teleportation. The method is also used to generate the AKLT ground state, a further resource for quantum computation. We justify, and derive analytic results for, an effective charge-spin Hamiltonian which is valid over a wide range of parameters and agrees well with exact numerical results of a realistic effective-mass model. Our analysis also indicates that the method is robust to choice of dot-size and initialization errors, as well as decoherence introduced by the hyperfine interaction.Comment: 5 pages, 3 figure

Numerical Solution of Some Nonlinear Volterra Integral Equations of the First Kind

In this paper, the solving of a class of the nonlinear Volterra integral equations (NVIE) of the first kind is investigated. Here, we convert NVIE of the first kind to a linear equation of the second kind. Then we apply the operational Tau method to the problem and prove convergence of the presented method. Finally, some numerical examples are given to show the accuracy of the method

On Obtaining Stable Rankings

Decision making is challenging when there is more than one criterion to consider. In such cases, it is common to assign a goodness score to each item as a weighted sum of its attribute values and rank them accordingly. Clearly, the ranking obtained depends on the weights used for this summation. Ideally, one would want the ranked order not to change if the weights are changed slightly. We call this property {\em stability} of the ranking. A consumer of a ranked list may trust the ranking more if it has high stability. A producer of a ranked list prefers to choose weights that result in a stable ranking, both to earn the trust of potential consumers and because a stable ranking is intrinsically likely to be more meaningful. In this paper, we develop a framework that can be used to assess the stability of a provided ranking and to obtain a stable ranking within an "acceptable" range of weight values (called "the region of interest"). We address the case where the user cares about the rank order of the entire set of items, and also the case where the user cares only about the top-$k$ items. Using a geometric interpretation, we propose algorithms that produce stable rankings. In addition to theoretical analyses, we conduct extensive experiments on real datasets that validate our proposal

A novel compact fractal UWB antenna with triple reconfigurable notch reject bands applications

A compact, circular UWB fractal antenna with triple reconfigurable notch rejection bands is proposed. It rejects the crowded frequency bands WiMAX, WLAN and X band interferences produced in UWB communication systems. The proposed fractal structure consists of a basic circular patch with circular fractal iterations. By employing this new structure of fractals, the overall size of antenna is reduced 53% to 21 × 25 mm, in comparison with traditional circular monopole antenna. The implemented antenna operates at 3.1–10 GHz. Re-configurability is realized by designing slots and split ring resonators in desired frequencies with the attached PIN diodes. WLAN band rejection was realized by creating a pair of optimized L-shaped slots in the ground plane. By etching a split ring resonator and a U-shaped slot, X and WiMAX bands were also rejected. Furthermore, by attaching diodes to aforementioned slots and designating the diodes on/off, different bands can be included or rejected. In time domain, the antenna properties are evaluated by a figure of merit called fidelity factor. Finally, the antenna properties are measured in anechoic chamber and the results agrees with simulation findings

A dynamic and context-aware semantic mediation service for discovering and fusion of heterogeneous sensor data

Sensors play an increasingly critical role in capturing and distributing observations of phenomena in our environment. The vision of the semantic sensor web is to enable the interoperability of various applications that use sensor data provided by semantically heterogeneous sensor services. However, several challenges still need to be addressed to achieve this vision. More particularly, mechanisms that can support context-aware semantic mapping and that can adapt to the dynamic metadata of sensors are required. Semantic mapping for the sensor web is required to support sensor data fusion, sensor data discovery and retrieval, and automatic semantic annotation, to name only a few tasks. This paper presents a context-aware ontology-based semantic mediation service for heterogeneous sensor services. The semantic mediation service is context-aware and dynamic because it takes into account the real-time variability of thematic, spatial, and temporal elements that describe sensor data in different contexts. The semantic mediation service integrates rule-based reasoning to support the resolution of semantic heterogeneities. An application scenario is presented showing how the semantic mediation service can improve sensor data interpretation, reuse, and sharing in static and dynamic settings

A survey on some risk factors and evaluation of their impacts on streptococcosis in rainbow trout farms in some provinces in Iran (Mazandaran, Fars)

One of the most important bacterial fish diseases which has caused some outbreaks in rainbow trout farms in Iran is streptococcusis. The farmers were suffering from huge economic losses due to the disease outbreaks in different rainbow trout farms in Iran. The aim of our study was to determine rate of streptococcusis incidence in different stage of growth in farmed rainbow trout in Mazandaran and Fars province. Fish and water samples were randomly collected and measured randomly in selected farms, monthly throughout a year. After clinical observations, Isolation and recognition of strep strains were made using biochemical and molecular tests. Some Environmental factors include Nitrate, Nitrite, Temperature, pH, Ammonia and DO measure during sampling periods. According to our results incidence of disease in juvenile is more than growers. Some samples showed clinical signs of streptococcusis without strep. contamination. Main isolated strain were S. iniae and S. garviea and S. uberis recognized for first time in east of Mazandaran province (Haraz River). Incidence of streptococcusis in rainbow trout affected by fluctuation of Nitrite, temperature and DO. Management of these factors can decrease rate of disease outbreaks