Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization

We compare the plug-in rule approach for optimizing the Fβ-measure in multilabel classification with an approach based on structured loss minimization, such as the structured support vector machine (SSVM). Whereas the former derives an optimal prediction from a probabilistic model in a separate inference step, the latter seeks to optimize the Fβ-measure directly during the training phase. We introduce a novel plug-in rule algorithm that estimates all parameters required for a Bayes-optimal prediction via a set of multinomial regression models, and we compare this algorithm with SSVMs in terms of computational complexity and statistical consistency. As a main theoretical result, we show that our plug-in rule algorithm is consistent, whereas the SSVM approaches are not. Finally, we present results of a large experimental study showing the benefits of the introduced algorithm.
A Numerical Huffman Decoder

We formulate the Huffman decoding as a root-solving problem so that powerful numerical techniques such as the Newton method and the bisection method are employed for solution. The sectioning technique that narrows the searching scope for solution is further proposed for increasing the decoding speed. The numerical decoder is fast and memory efficient; it is also simple to realize and suitable for integrated circuit implementation.
Spatial Variability of Snow Water Equivalent – The Case Study from the Research Site in Khibiny Mountains, Russia

Abstract The aim of the investigation was assessment of spatial variability of the characteristics of snowpack, including the snow water equivalent (SWE) as the main hydrological characteristic of a seasonal snow cover. The study was performed in Khibiny Mountains (Russia), where snow density and snow cover stratigraphy were documented with the help of the SnowMicropen measurements, allowing to determine the exact position of the snow layers’ boundaries with accuracy of 0.1 cm. The study site was located at the geomorphologically and topographically uniform area with uniform vegetation cover. The measurement was conducted at maximum seasonal SWE on 27 March 2016. Twenty vertical profiles were measured along the 10 m long transect. Vertical resolution depended on the thickness of individual layers and was not less than 10 cm. The spatial variation of the measured snowpack characteristics was substantial even within such a homogeneous landscape. Bulk snow density variability was similar to the variability in snow height. The total variation of the snowpack SWE values along the transect was about 20%, which is more than the variability in snow height or snow density, and should be taken into account in analysis of the results of normally performed in operational hydrology snow course SWE estimations by snow tubes.
Bis(μ-5-carboxylato-1-carboxylatomethyl-2-oxidopyridinium)-κ2 O 5:O 1;κ2 O 1:O 5-[diaqua(phenanthroline-κ2 N,N′)manganese(II)] dihydrate

The centrosymmetric binuclear title complex, [Mn2(C8H5NO5)2(C12H8N2)2(H2O)4]·2H2O, was obtained by the reaction of manganese chloride with 5-carboxy-1-carboxymethyl-2-oxidopyridinium and 1,10-phenanthroline. The MnII atom is coordinated by two N atoms from the 1,10-phenanthroline ligand, two O atoms from two 5-carboxylato-1-carboxylatomethyl-2-oxidopyridinium ligands and two water molecules, leading to a distorted octahedral MnN2O4 environment. Intermolecular O—H⋯O hydrogen bonds link neighbouring molecules into a layer structure parallel to (001).
A New Framework for Privacy-Preserving Aggregation of Time-Series Data

Aggregator-oblivious encryption is a useful notion put forward by Shi et al. in 2011 that allows an untrusted aggregator to periodically compute an aggregate value over encrypted data contributed by a set of users. Such encryption schemes find numerous applications, particularly in the context of privacy-preserving smart metering. This article presents a general framework for constructing privacy-preserving aggregator-oblivious encryption schemes using a variant of Cramer-Shoup’s paradigm of smooth projective hashing. This abstraction leads to new schemes based on a variety of complexity assumptions. It also improves upon existing constructions, providing schemes with shorter ciphertexts and better encryption times.
Clinical Data Mining in Small Hospital PACS: Contributions for Radiology Department Improvement

Technological developments in the medical imaging acquisition and storage process have triggered the use of PACS (Picture Archiving and Communication Systems) with gradually larger archives. Nowadays, there is data stored in the DICOM (Digital Imaging and Communication in Medicine) file that is not searchable using the traditional PACS database. However, it may represent an important source of information for continuous professional practice improvement. The use of DICOM Data Mining tools has been a valuable asset to analyze the information stored in the DICOM file and can result in gathering important data for the professional practice improvement. These tools can also contribute to the PACS information audit and facilitate access to relevant clinical data within programs for quality continuous improvement. By allowing the construction of multiple views over data repository in a flexible and quickly way and with the possibility to export data for further statistical analysis, Dicoogle permits the identification of data and process inconsistencies that can contribute to radiology department improvement, such as in dose surveillance and patient safety programs and image quality control initiatives. However, the assessment of relevant data for practice improvement must take into account several factors related to the informational environment, professional reality, and healthcare goals and mission. This chapter describes a method to examine and perform studies over a medical imaging repository. Moreover, a case study of a small hospital where the obtained results are discussed is shown. DOI: 10.4018/978-1-4666-3667-5.ch016
Direct Observations of Fine Primary Particles From Residential Coal Burning: Insights Into Their Morphology, Composition, and Hygroscopicity

Emissions of residential coal burning are an important contributor to air pollution in developing countries, but few studies have yet comprehensively characterized the physicochemical properties of individual primary particles from residential coal burning. Fine primary particles emitted from eight types of coal with low, medium, and high maturity were collected in the flaming and burn‐out stages in a typical residential stove. Based on morphology and composition of individual particles, they were divided into six types: organic matter (OM), OM‐S, soot‐OM, S‐rich, metal, and mineral particles. Low‐maturity coals (e.g., lignite) dominantly emitted soot‐OM particles in the flaming stage, the medium‐maturity coals (e.g., medium‐maturity bituminous coals) emitted abundant OM particles, and high‐maturity coals (e.g., anthracite) emitted abundant OM‐S particles. We found that carbonaceous particles from coal burning significantly decreased with an increase of coal maturity and that soot particles were mainly formed in the flaming stage of low‐maturity coals under higher burning temperatures. We concluded that coal maturity and burning temperature both determine particulate properties in coal emissions. In addition, OM and soot particles from residential coal burning displayed extremely weak hygroscopicity, while inorganic salts within individual particles determined particle hygroscopic growth. Understanding the characteristics of particulate matter emitted from residential coal burning is helpful to trace sources of ambient particles and clarify their possible aging mechanism in air influenced by coal burning emissions. Our results suggest that air quality improvements can benefit substantially from the replacement of low‐ and medium‐maturity coals with high‐maturity coals, natural gas, or electricity in rural areas.
Robot Space Exploration Using Peano Paths Generated by Self-Organizing Maps

Autonomous exploration by a team of robots has many important applications in rescue operations, clearing of mine fields and other military applications, and even space exploration. With limited range of sensors robots have to divide exploration tasks among themselves working under multiple constraints. An optimal covering of two-dimensional area by robot trajectories requires formation of space-filling Peano curves. This may be achieved using self-organizing feature map (SOFM) algorithm. There are two steps involved in the proposed approach: first optimal trajectories are defined generating Peano curves for space of arbitrary shape using the SOFM algorithm, and second, robots are deployed for exploration based on selection of start/end nodes and radius of robot sensors. The same approach may be used to direct people or teams exploring some area in rescue operations. Tests simulations show that this approach achieves better coverage and faster exploration than competing algorithms
Abstracts from the 1st Annual Meeting of the Scottish Society of Cytomics (SCC) 2014. “Translational Cytometry from Bench to Bedside”

s from the 1st Annual Meeting of the Scottish Society of Cytomics (SCC) 2014. “Translational Cytometry from Bench to Bedside” Aberdeen, UK. 25 September 2014 Published: 16 April 2015 These abstracts are available online at http://www.journal-inflammation.com/supplements/12/S1
Canonical state models for automatic speech recognition

Current speech recognition systems are often based on HMMs with state-clustered Gaussian Mixture Models (GMMs) to represent the context dependent output distributions. Though highly successful, the standard form of model does not exploit any relationships between the states, they each have separate model parameters. This paper describes a general class of model where the context-dependent state parameters are a transformed version of one, or more, canonical states. A number of published models sit within this framework, including, semi-continuous HMMs, subspace GMMs and the HMM error model. A set of preliminary experiments illustrating some of this model’s properties using CMLLR transformations from the canonical state to the context dependent state are described.
International Journal of Emerging Electric Power Systems Non-Contact Determination of the Phase Sequence and Voltage Level of Overhead Conductors in Air Insulated Substations

This paper describes how the phase sequence and voltage level (132 kV, 275 kV or 400 kV) of single-circuit overhead conductors can be calculated from measurements made by passive, noncontact sensors situated on a moving platform at ground level. The investigation relies on sensing the electric field along the profile of the conductors through the use of passive sensors. The fabrication of the passive sensor is described together with an analysis of its performance. The variation of the vertical component of electric field strength along the lateral profile beneath single-circuit conductors has been studied using finite element modeling software, ANSYS. The results of this simulation have led to a proposed method of determining the magnitude and phase sequence information from overhead conductors via analysis of the induced voltages on the sensors. The paper concludes with an investigation made in a 400 kV substation in the UK. Analysis of the experimental results shows that the proposed technique can successfully evaluate the phase sequence, voltage level, the spacing between phases and the orientation of high-voltage overhead conductors.
Speeding Up Tracking by Ignoring Features

Most modern object trackers combine a motion prior with sliding-window detection, using binary classifiers that predict the presence of the target object based on histogram features. Although the accuracy of such trackers is generally very good, they are often impractical because of their high computational requirements. To resolve this problem, the paper presents a new approach that limits the computational costs of trackers by ignoring features in image regions that -- after inspecting a few features -- are unlikely to contain the target object. To this end, we derive an upper bound on the probability that a location is most likely to contain the target object, and we ignore (features in) locations for which this upper bound is small. We demonstrate the effectiveness of our new approach in experiments with model-free and model-based trackers that use linear models in combination with HOG features. The results of our experiments demonstrate that our approach allows us to reduce the average number of inspected features by up to 90% without affecting the accuracy of the tracker.
Control of nonlinear systems with explicit-MPC-like controllers

This paper describes synthesis of controllers involving Quadratic Programming (QP) optimization problems for control of nonlinear systems. The QP structure allows an implementation of the controller as a piecewise affine function, pre-computed offline, which is a technique extensively studied in the field of explicit model predictive control (EMPC). The method is based on a sum-of-squares (SOS) stability verification for polynomial discrete-time systems, described in continuous-time in this paper. The presented synthesis method allows a design of EMPC controllers with closed-loop stability guarantees without relying on a terminal cost and/or constraint, and even without using the prediction horizon concept to formulate the control optimization problem. In particular, for a specified QP structure the method directly searches for the stabilizing coefficients in the cost and/or the constraint set so that a polynomial Lyapunov function for the closed-loop system exists. The method involves two phases that are formulated as optimization problems which can be tackled by using a blackbox optimization technique, such as Bayesian optimization which is used in this paper. The synthesis is demonstrated on a numerical example involving a bilinear model of a permanent magnet synchronous machine (PMSM), where in order to demonstrate the modeling flexibility of the proposed synthesis method a QP-based controller for speed regulation of PMSM is synthesized that is robust to parametric uncertainty coming from the temperature-dependent stator resistance of the PMSM.
Simple Destination-Swap Strategies for Adaptive Intra- and Inter-Tenant VM Migration

This paper investigates the opportunities and limitations of adaptive virtual machine (VM) migration to reduce communication costs in a virtualized environment. We introduce a new formal model for the problem of online VM migration in two scenarios: (1) VMs can be migrated arbitrarily in the substrate network; e.g., a private cloud provider may have an incentive to reduce the overall communication cost in the network. (2) VMs can only be migrated within a given tenant; e.g., a user that was assigned a set of physical machines may exchange the functionality of the VMs on these machines. We propose a simple class of Destination-Swap algorithms which are based on an aggressive collocation strategy (inspired by splay datastructures) and which maintain a minimal and local amount of per-node (amortized cost) information to decide where to migrate a VM and how; thus, the algorithms react quickly to changes in the load. The algorithms come in two main flavors, an indirect and distributed variant which keeps existing VM placements local, and a direct variant which keeps the number of affected VMs small. We show that naturally, inter-tenant optimizations yield a larger potential for optimization, but generally also a tenant itself can improve its embedding. Moreover, there exists an interesting tradeoff between direct and indirect strategies: indirect variants are preferable under skewed and sparse communication patterns due to their locality properties.
The balance between cell cycle arrest and cell proliferation: control by the extracellular matrix and by contact inhibition

To understand the dynamics of the cell cycle, we need to characterize the balance between cell cycle arrest and cell proliferation, which is often deregulated in cancers. We address this issue by means of a detailed computational model for the network of cyclin-dependent kinases (Cdks) driving the mammalian cell cycle. Previous analysis of the model focused on how this balance is controlled by growth factors (GFs) or the levels of activators (oncogenes) and inhibitors (tumour suppressors) of cell cycle progression. Supra-threshold changes in the level of any of these factors can trigger a switch in the dynamical behaviour of the Cdk network corresponding to a bifurcation between a stable steady state, associated with cell cycle arrest, and sustained oscillations of the various cyclin/Cdk complexes, corresponding to cell proliferation. Here, we focus on the regulation of cell proliferation by cellular environmental factors external to the Cdk network, such as the extracellular matrix (ECM), and contact inhibition, which increases with cell density. We extend the model for the Cdk network by including the phenomenological effect of both the ECM, which controls the activation of the focal adhesion kinase (FAK) that promotes cell cycle progression, and cell density, which inhibits cell proliferation via the Hippo/YAP pathway. The model shows that GFs and FAK activation are capable of triggering in a similar dynamical manner the transition to cell proliferation, while the Hippo/YAP pathway can arrest proliferation once cell density passes a critical threshold. The results account for the dependence or independence of cell proliferation on serum and/or cell anchorage to ECM. Whether the balance in the Cdk network is tilted towards cell cycle arrest or proliferation depends on the direction in which the threshold associated with the bifurcation is passed once the cell integrates the multiple, internal or external signals that promote or impede progression in the cell cycle.
Challenges in collecting clinical samples for research from pregnant women of South Asian origin: evidence from a UK study

Objective To recruit South Asian pregnant women, living in the UK, into a clinicoepidemiological study for the collection of lifestyle survey data and antenatal blood and to retain the women for the later collection of cord blood and meconium samples from their babies for biochemical analysis. Design A longitudinal study recruiting pregnant women of South Asian and Caucasian origin living in the UK. Setting Recruitment of the participants, collection of clinical samples and survey data took place at the 2 sites within a single UK Northern Hospital Trust. Participants Pregnant women of South Asian origin (study group, n=98) and of Caucasian origin (comparison group, n=38) living in Leeds, UK. Results Among the participants approached, 81% agreed to take part in the study while a ‘direct approach’ method was followed. The retention rate of the participants was a remarkable 93.4%. The main challenges in recruiting the ethnic minority participants were their cultural and religious conservativeness, language barrier, lack of interest and feeling of extra ‘stress’ in taking part in research. The chief investigator developed an innovative participant retention method, associated with the women's cultural and religious practices. The method proved useful in retaining the participants for about 5 months and in enabling successful collection of clinical samples from the same mother–baby pairs. The collection of clinical samples and lifestyle data exceeded the calculated sample size required to give the study sufficient power. The numbers of samples obtained were: maternal blood (n=171), cord blood (n=38), meconium (n=176), lifestyle questionnaire data (n=136) and postnatal records (n=136). Conclusions Recruitment and retention of participants, according to the calculated sample size, ensured sufficient power and success for a clinicoepidemiological study. Results suggest that development of trust and confidence between the participant and the researcher is the key to the success of a clinical and epidemiological study involving ethnic minorities.
Estimation of the Hurst Parameter of Long-Range Dependent Time Series

This paper is a condensed introduction to self-similarity, self-similar processes, and the estimation of the Hurst parameter in the context of time series analysis. It gives an overview of the literature on this subject and provides some assistance in implementing Hurst parameter estimators and carrying out experiments with empirical time series.
A Molecular Electron Density Theory Study of the Reactivity of Azomethine Imine in [3+2] Cycloaddition Reactions

The electronic structure and the participation of the simplest azomethine imine (AI) in [3+2] cycloaddition (32CA) reactions have been analysed within the Molecular Electron Density Theory (MEDT) using Density Functional Theory (DFT) calculations at the MPWB1K/6-311G(d) level. Topological analysis of the electron localisation function reveals that AI has a pseudoradical structure, while the conceptual DFT reactivity indices characterises this three-atom-component (TAC) as a moderate electrophile and a good nucleophile. The non-polar 32CA reaction of AI with ethylene takes place through a one-step mechanism with moderate activation energy, 8.7 kcal·mol−1. A bonding evolution theory study indicates that this reaction takes place through a non-concerted [2n + 2τ] mechanism in which the C–C bond formation is clearly anticipated prior to the C–N one. On the other hand, the polar 32CA reaction of AI with dicyanoethylene takes place through a two-stage one-step mechanism. Now, the activation energy is only 0.4 kcal·mol−1, in complete agreement with the high polar character of the more favourable regioisomeric transition state structure. The current MEDT study makes it possible to extend Domingo’s classification of 32CA reactions to a new pseudo(mono)radical type (pmr-type) of reactivity.
Scheduling instructions on hierarchical machines

The aim of this work is to study the problem of scheduling fine grain task graphs on hierarchical distributed systems with communication delay. We consider as a case study how to schedule the instructions on a processor that implements incomplete bypass ( ST200). We show first how this problem can be expressed as scheduling unitary tasks on a hierarchical architecture with heavy communications between clustered units. The proposed analysis is generic and can be extended to other challenging problems like scheduling in clusters of multi-cores. Our main result is an approximation algorithm based on list scheduling whose approximation ratio is the minimum of two expressions, the first one depends on the number of clusters while the second one depends on the communication delay. Experiments run on random graphs and on structured graphs demonstrate the effectiveness of the proposed approach.
A discrete-continuous algorithm for molecular energy minimization

A parallel algorithm for minimizing molecular energy potential functions is applied to the case of pure Lennard-Jones (LJ) clusters. The algorithm demonstrates the combination of discrete, lattice-based optimization with continuous optimization (relaxation) techniques. The suggested approach is not restricted to the LJ potential and is aimed at problems in which the potential of interest may be significantly more costly than the LJ. The intended audience includes researchers interested in practical computational problems involving minimum energy cluster conformation, such as may arise in catalysis, and those interested in algorithm development. The advantage of the algorithm is that the time required to find the minimum-energy structure for a relatively large cluster reduces to that of an interactive session. The current parallel implementation is capable of determining the best-known previously published binding energies for n<or=150 LJ clusters in a matter of seconds and has provided new results on minimum energies for clusters of up to n=1000 atoms using a massively parallel processor, the CM-5. <<ETX>>