Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating Evidence Based Interpretation of Hematology Screens via Anomaly Characterization

Introduction: We propose a simple, workable algorithm that provides assistance for interpreting any set of data from the screen of a blood analysis with high accuracy, reliability, and inter-operability with an electronic medical record. This has been made possible at least recently as a result of advances in mathematics, low computational costs, and rapid transmission of the necessary data for computation. Materials and Methods: The database used for this study is a file of 22,000 laboratory hemograms generated by two Beckman-Coulter Gen-S analyzers over a two month period in a 630 bed acute care facility in Brooklyn. All control samples, patient identifiers, and patients under 23 years old were stripped from the dataset. An experienced medical practitioner reviewed all of the data used in generating the algorithm described. The differential diagnoses were outlined prior to beginning the study, and preliminary studies were done to determine the reference ranges for each predictor. An algorithm for anomaly detection and classification via anomaly characterization is proposed. For each patient, the algorithm characterizes its anomalous profile and builds a differential metric to identify similar patients who are mapped into a classification. Results: The algorithm successfully classified patients into the diagnosis that were sufficient in sample size, and others are still under observation. The algorithm correctly classified the patients as follows: Microcytic Anemia - 99.63%, Normocytic Anemia - 98.03%, Mild SIRS - 73.42%, Thrombocytopenia - 99.52%, Leukocytopenia - 84.83%, Moderate / Severe SIRS - 96.69% and Normal - 93.18%. Discussion: This limited analysis of automated hematological results can be extended to the case of more complicated conditions than presented, and can be extended to a combination of chemistry, hematology, immunology, and other data.
Measuring the Meltdown: Drivers of Global Amphibian Extinction and Decline

Habitat loss, climate change, over-exploitation, disease and other factors have been hypothesised in the global decline of amphibian biodiversity. However, the relative importance of and synergies among different drivers are still poorly understood. We present the largest global analysis of roughly 45% of known amphibians (2,583 species) to quantify the influences of life history, climate, human density and habitat loss on declines and extinction risk. Multi-model Bayesian inference reveals that large amphibian species with small geographic range and pronounced seasonality in temperature and precipitation are most likely to be Red-Listed by IUCN. Elevated habitat loss and human densities are also correlated with high threat risk. Range size, habitat loss and more extreme seasonality in precipitation contributed to decline risk in the 2,454 species that declined between 1980 and 2004, compared to species that were stable (n = 1,545) or had increased (n = 28). These empirical results show that amphibian species with restricted ranges should be urgently targeted for conservation.
Inquisitiveness: Distributing rational thinking

Purpose This study aims at redefining bounded rationality on the basis of a more socialized view of the individual. In doing so, it introduces “inquisitiveness” as a key disposition that some team members use to assemble and integrate knowledge when solving problems. Design/methodology/approach Using an agent-based computational simulation, this research models different simulated employees working together in “ad hoc” teams to solve problems. Findings Results show that inquisitiveness may work as an efficiency “driver” that, when present, economizes on the knowledge needed by team members to solve problems. In addition to that, results also show that environments with many problems are more suitable for inquisitive individuals to be effective. Originality/value Following the late Herbert Simon, the paper takes the stance that rationality should be redefined as a socially oriented process and introduces inquisitiveness as one – although probably not the only one – of the characteristics that help individuals and teams to make rational decisions.
IBP-MIME: Controlled Delivery of Large Mail Files

The current model of mail delivery over the Internet guarantees that an email message, when delivered, will have all its parts present at the recipient's mail server. The message is typically spooled pending retrieval by the recipient at a later time. This model has serious eeciency problems when handling messages that contain large les. Such messages impose a heavy burden on storage resources at the mail server, where they can occupy space for an indeenite period of time pending retrieval. The situation is made worse when the message is directed to multiple users who share the same mail server. In this paper we present a new model for delivery of email messages containing large les. The proposed model separates the text part of the email message from the non-text part, and entrusts the latter to a set of network storage servers that keep it until prompted for delivery by the recipient. The proposed model makes use of new initiatives in distributed storage that are part of the Internet2 project. In addition, it allows for recipient initiated preprocessing of the sent le remotely, before actually receiving it. This option is becoming more feasible with the advent of computational servers such as NetSolve and can have wide implications in areas such as electronic commerce and security.
Pricing of Software Services

We analyze and compare fixed-fee and usage-fee software pricing schemes - in fixed-fee pricing, all users pay the same price; in usage-fee pricing, the users' fees depend on the amount that they use the software (e.g., the user of an online-database service might be charged for each data query). We employ a two-dimensional model of customer heterogeneity - specifically, we assume that customers vary in the amount that they will use the software (usage heterogeneity) and also in their per-use valuation of the software. To understand the performance of these pricing schemes and their sensitivity to the competitive environment in which they are used, we look at a number of different scenarios: a monopolist offering just one of these schemes, a monopolist offering a choice of pricing schemes, and several duopoly scenarios. We characterize and compare the equilibria that arise in these scenarios and provide insights into optimal pricing strategies.
Probabilistic decomposition of sequential force interaction tasks into Movement Primitives

Learning sequential force interaction tasks from kinesthetic demonstrations is a promising approach to transfer human manipulation abilities to a robot. In this paper we propose a novel concept to decompose such demonstrations into a set of Movement Primitives (MPs). The decomposition is based on a probability distribution we call Directional Normal Distribution (DND). To capture the sequential properties of the manipulation task, we model the demonstrations with a Hidden Markov Model (HMM). Here, we employ mixtures of DNDs as the HMM's output emissions. The combination of HMMs and mixtures of DNDs allows to infer the MP's composition, i.e., its coordinate frames, control variables and target coordinates from the demonstration data. In addition, it permits to determine an appropriate number of MPs that explains the demonstrations best. We evaluate the approach on kinesthetic demonstrations of a light bulb unscrewing task. Decomposing the task leads to intuitive and meaningful MPs that reflect the natural structure of the task.
Patchiness of forest landscape can predict species distribution better than abundance: the case of a forest-dwelling passerine, the short-toed treecreeper, in central Italy

Environmental heterogeneity affects not only the distribution of a species but also its local abundance. High heterogeneity due to habitat alteration and fragmentation can influence the realized niche of a species, lowering habitat suitability as well as reducing local abundance. We investigate whether a relationship exists between habitat suitability and abundance and whether both are affected by fragmentation. Our aim was to assess the predictive power of such a relationship to derive advice for environmental management. As a model species we used a forest specialist, the short-toed treecreeper (Family: Certhiidae; Certhia brachydactyla Brehm, 1820), and sampled it in central Italy. Species distribution was modelled as a function of forest structure, productivity and fragmentation, while abundance was directly estimated in two central Italian forest stands. Different algorithms were implemented to model species distribution, employing 170 occurrence points provided mostly by the MITO2000 database: an artificial neural network, classification tree analysis, flexible discriminant analysis, generalized boosting models, generalized linear models, multivariate additive regression splines, maximum entropy and random forests. Abundance was estimated also considering detectability, through N-mixture models. Differences between forest stands in both abundance and habitat suitability were assessed as well as the existence of a relationship. Simpler algorithms resulted in higher goodness of fit than complex ones. Fragmentation was highly influential in determining potential distribution. Local abundance and habitat suitability differed significantly between the two forest stands, which were also significantly different in the degree of fragmentation. Regression showed that suitability has a weak significant effect in explaining increasing value of abundance. In particular, local abundances varied both at low and high suitability values. The study lends support to the concept that the degree of fragmentation can contribute to alter not only the suitability of an area for a species, but also its abundance. Even if the relationship between suitability and abundance can be used as an early warning of habitat deterioration, its weak predictive power needs further research. However, we define relationships between a species and some landscape features (i.e., fragmentation, extensive rejuvenation of forests and tree plantations) which could be easily controlled by appropriate forest management planning to enhance environmental suitability, at least in an area possessing high conservation and biodiversity values.
Divide-and-Conquer Methods for Solving MDPs

The Markov Decision Process (MDP) is the principal theoretical formalism in the area of Reinforcement Learning (RL). An import from optimal control in operations research, this construct is generic enough to represent problems comprising almost all of AI research, but consequently, it suffers from the curse of dimensionality where learning involves an exponential number of parameters. Researchers have proposed many methods to tame the MDP beast, and they fall under the broad categorizations of factored, hierarchical, and relational approaches. These methods are essentially different flavors of the classic divide-and-conquer approach to problem-solving in computer science – breaking up a large, unwieldy problem into smaller components and solving the parts. This paper reviews most of the methods proposed in the associated literature, weighing their pros and cons, and their applicability to the transfer of learned knowledge betweens MDPs.
On biometric encryption using fingerprint and it's security evaluation

Biometric person authentication has been attracting considerable attention in recent years. Conventional biometric person authentication systems, however, simply store each user's template as-is on the system. If registered templates are not properly protected, the risk arises of template leakage to a third party and impersonation using biometric data restored from a template. We propose a technique that encrypts and stores the user template and uses a ldquofuzzy vault schemerdquo to generate secret data from the user template and the query biometric data. It incorporates a measure to prevent the secret data and the user template used to obtain the secret data from being recovered from information stored in the system, and it enables the secret data to be generated from the user's biometric data. In this paper, we introduce this technique and evaluate template security with it by simulating a fingerprint authentication system.
A sparse representation based approach for steganography

In recent years, there has been a growing interest in exploiting sparsity of natural signals for use in a large number of applications including encryption and steganography. Steganography refers to the practice of hiding a secret message, known as stegotext, within a simple looking message called plaintext so that it can only be retrieved by the intended user. It is also desired that the hidden data should in no way affect the perceived quality of the cover message. In this paper, we propose an approach to steganography that is based on sparse representation of signals. The proposed method hides data within an audio clip or an image without compromising on their perceived qualities. It is also demonstrated in our experiments that the hidden data can be successfully separated out from the cover message. However, the proposed method is a fragile one in the sense that it is not robust to any sort of lossy processing like compression, cropping, etc.
ARTISAN: a shape retrieval system based on boundary family indexing

Successful retrieval of images by shape feature is likely to be achieved only if we can mirror human similarity judgments. Following Biederman's theory of recognition-by-components, we postulate that shape analysis for retrieval should characterize an image by identifying properties such as collinearity, shape similarity and proximity in component boundaries. Such properties can then be used to group image components into families, from which indexing features can be derived. We are currently applying these principles in the development of the ARTISAN shape retrieval system for the UK Patent Office. The trademark images, supplied in compressed bit-map format, are processed using standard edge-extraction techniques to derive a set of region boundaries, which are approximated as a sequence of straight-line and circular-arc segments. These are then grouped into families using criteria such as proximity and shape similarity. Shape features for retrieval are then extracted from the image as a whole, each boundary family, and each individual boundary. Progress to date with the project is analyzed, evaluation plans described, and possible future directions for the research discussed.
Quantum wiretap channel with non-uniform random number and its exponent of leaked information

A usual code for quantum wiretap channel requires an auxiliary random variable subject to the perfect uniform distribution. However, it is difficult to prepare such an auxiliary random variable. We propose a code that requires only an auxiliary random variable subject to a non-uniform distribution instead of the perfect uniform distribution. Further, we evaluate the exponential decreasing rate of leaked information.
Notes on the use of data transformations.

Data transformations are commonly used tools that can serve many functions in quantitative analysis of data. The goal of this paper is to focus on the use of three data transformations most commonly discussed in statistics texts (square root, log, and inverse) for improving the normality of variables. While these are important options for analysts, they do fundamentally transform the nature of the variable, making the interpretation of the results somewhat more complex. Further, few (if any) statistical texts discuss the tremendous influence a distribution's minimum value has on the efficacy of a transformation. The goal of this paper is to promote thoughtful and informed use of data transformations.
The Unintended Consequences of Checklists

Research suggests that checklists reduce errors in fields ranging from aviation to medicine. Checklists are effective in part because their content is not randomly selected from available information but strongly sampled from information experts believe is critical. This sampling process supports the inference that unlisted information is unlikely to be important. However, this predicts that checklists might leave learners selectively vulnerable to unlisted sources of error. In Experiment 1, we show that adults in an aviation class detect fewer unlisted sources of error given a checklist than at baseline. In Experiment 2, we show that this inductive bias does not require previous experience with checklists: given a checklist for organizing a room, children (mean: 62 months) selectively overlooked unlisted items relative to baseline, and did so even when told the list might be incomplete.
Reliability sensitivity analysis with Monte Carlo methods

In many practical applications of structural reliability analysis, one is interested in knowing the sensitivity of the probability of failure to design parameters that enter the definition of the limit-state function. This information is required for example in reliability-based design optimization. Parameter sensitivities are obtained by FORM/SORM, in terms of the sensitivity of the respective probability approximation. However, in several cases FORM/SORM approximations are inaccurate or difficult to obtain. Hence, it is useful to obtain parameter sensitivities as a by-product of Monte Carlo methods. The derivative of the failure probability with respect to a parameter of the limit state function is given by a surface integral over the limit state surface. Application of Monte Carlo methods for the estimation of surface integrals is not straightforward. The surface integral can be computed applying sampling techniques that detect the failure surface, such as directional or line sampling. In this paper, we derive an approximation of the surface integral in terms of a domain integral. The domain integral can then be estimated through standard Monte Carlo or importance sampling simulation. A numerical example demonstrates the effectiveness of the proposed approximation for estimation with an importance sampling method.
POPEYE: A simple and reliable collaborative working environment over mobile ad-hoc networks

Although useful and increasingly put into regular use, current collaborative working environment (CWE) solutions have several limitations and relevant needs for further research. Most systems address either rather traditional and rigid intra-organizational collaboration scenarios or, at the opposite, completely free and unstructured open communitiespsilas interactions. Emerging dynamic, flexible and ad-hoc collaboration schemes are hardly or not supported at all. Typically, explicit representation of the collaboration context, of workerspsila and teampsilas goals and the semantics of underlying business process are not addressed, and this makes difficult to ensure the context and process awareness, personalisation and collaboration support required in knowledge-rich interactions. Aiming at getting collaborative working environments free from rigid constraints, POPEYE considers P2P over wireless ad hoc groups, where fixed infrastructure is not a prerequisite, where virtual communities can emerge spontaneously and share data with the appropriate quality of service for business applications (persistence, synchronisation, security,.).
CRLB Calculations for Joint AoA, AoD and Multipath Gain Estimation in Millimeter Wave Wireless Networks

In this report we present an analysis of the non-random and the Bayesian Cramer-Rao lower bound (CRLB) for the joint estimation of angle-of-arrival (AoA), angle-of-departure (AoD), and the multipath amplitudes, for the millimeter-wave (mmWave) wireless networks. Our analysis is applicable to multipath channels with Gaussian noise and independent path parameters. Numerical results based on uniform AoA and AoD in $[0,\pi)$, and Rician fading path amplitudes, reveal that the Bayesian CRLB decreases monotonically with an increase in the Rice factor. Further, the CRLB obtained by using beamforming and combining code books generated by quantizing directly the domain of AoA and AoD was found to be lower than those obtained with other types of beamforming and combining code books.
Transcript Normalization and Segmentation of Tiling Array Data

For the analysis of transcriptional tiling arrays we have developed two methods based on state-of-the-art machine learning algorithms. First, we present a novel transcript normalization technique to alleviate the effect of oligonucleotide probe sequences on hybridization intensity. It is specifically designed to decrease the variability observed for individual probes complementary to the same transcript. Applying this normalization technique to Arabidopsis tiling arrays, we are able to reduce sequence biases and also significantly improve separation in signal intensity between exonic and intronic/intergenic probes. Our second contribution is a method for transcript mapping. It extends an algorithm proposed for yeast tiling arrays to the more challenging task of spliced transcript identification. When evaluated on raw versus normalized intensities our method achieves highest prediction accuracy when segmentation is performed on transcript-normalized tiling array data.
The (L, L) bilinear Hardy-Littlewood function for the tail

Let (X,B, μ, T ) be a measure preserving dynamical system on a finite measure space. Consider the maximal function R : (f, g) ∈ L × L → R(f, g)(x) = sup n f(T nx)g(T 2nx) n . We prove that if p and q are greater or equal than one and 1p + 1 q < 2 then R ∗ maps Lp × Lq into any Lr as long as 0 < r < 1/2. This implies that R∗(f, g) is finite almost everywhere and f(T x)g(T x) n → 0 for a.e. x as n → ∞.
Sample-guided progressive image coding

We introduce a low-level image representation using local orientation on a steerable pyramid. Its performance is demonstrated for a progressive image coder which transmits and reconstructs parts of an image guided by their similarity to a given sample image. Similar to human visual attention, the image is scanned in the most-important-first order to gain a maximum of visual information with minimum code.