Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of secondary cyclones and cyclone families for the North Atlantic storm track and clustering over western Europe

Secondary cyclones are those that form in association with a pre‐existing primary cyclone, typically along a trailing cold front. In previously studied cases they have been shown to cause extreme damage across Europe, particularly when multiple cyclones track over the same location in rapid succession (known as cyclone clustering). To determine the dynamical relationship between primary and secondary cyclones over the North Atlantic, a frontal identification algorithm is partnered with a cyclone identification method to objectively identify secondary cyclones in 35 extended winter periods using reanalysis data. Cyclones are grouped into “cyclone families” consisting of a single primary cyclone and one or more secondary cyclones. This paper aims to quantify the differences between secondary and primary cyclones over the North Atlantic, and how cyclone families contribute to episodes of cyclone clustering across western Europe. Secondary cyclones are shown to occur most frequently in the central and eastern North Atlantic, whereas primary cyclones are commonly found over the western North Atlantic. Cyclone families have their strongest presence over the North Atlantic Ocean and contribute more than 50% of cyclones over the main North Atlantic storm track. A final category, solo cyclones, which are not associated with cyclogenesis on any connected fronts, are most commonly identified over continental regions as well as the Mediterranean Sea. Primary cyclones are associated with the development of an environment that is favourable for secondary cyclone growth. Enhanced Rossby wave breaking following primary cyclone development leads to an increase in the upper‐level jet speed and a decrease in low‐level stability. Secondary cyclogenesis commonly occurs in this region of anomalously low stability, close to the European continent. During periods of cyclone clustering, secondary cyclones are responsible for approximately 50% of the total number of cyclones. The increase in jet speed and decrease in static stability initiated by the primary cyclones acts to concentrate the genesis region of secondary cyclones and direct the cyclones that form along a similar track. While there is an increase in the secondary cyclogenesis rate near western Europe during periods of European clustering, the basin‐wide secondary cyclogenesis rate decreases during these periods. Thus the large‐scale environment redistributes secondary cyclones during periods of clustering rather than increasing the total number of secondary cyclones.
An Enhanced Computer Vision Based Hand Movement Capturing System with Stereo Vision

This framework is a hand movement capturing method which could be done in three different depth levels. The algorithm has the capability of capturing and identifying when the hand is moving up, down, right and left. From these captured movements four signals could be generated. Moreover, when these hand movements are done, 15cm-75cm, 75cm-100cm, 100cm200cm from the camera (3 depth levels), twelve different signals could be generated. These generated signals could be used for applications such as game controlling (gaming). The existing method uses an object area based method for depth analysis. The results of the proposed work shows it has high accuracy compared to the existing method when tested for depth analysis.
Mapping the Web Relations of Science Centres and Museums from Northern Europe

Northern Europe is a region characterized by the highest index of science centres and museums visits per year. Their regional association is the Nordiska Science Center Förbundet – NSCF – and it is considered a key institution for science popularization in the area. In this study we explore the relations of its associated science centres and museums by performing a Web co-link analysis applied to 37 Websites of NSCF affiliates in order to map their relationships over the Internet. Clustering analysis, multidimensional scaling (MDS) and an analysis of the language versions of each Website were performed. Danish, Norwegian and Swedish, for its linguistic similarities, constitute a community with great mutual intelligibility. In addition, the analyzed data shows strong geographical and/or linguistic influences in the formation of major groups, with external recognition and thematic issues being responsible for secondary cluster formation.
On Representing Skylines by Distance∗

Given an integer k, a representative skyline contains the k skyline points that best describe the tradeoffs among different dimensions offered by the full skyline. This paper proposes a distance-based formulation, which aims at minimizing the distance between a non-representative skyline point and its nearest representative. In 2D space, there is a dynamic-programming algorithm for computing an optimal representative skyline, whereas for dimensionality at least 3, we prove that the problem is NP-hard, and give a 2-approximate polynomial-time solution. The effectiveness and efficiency of our techniques have been confirmed by extensive experimentation. ∗A short version of this paper has appeared in ICDE’09.
Identification of Surgical Instruments Contained in Laparoscopic Images

In this work, an approach for surgical instruments recognition within images taken from an endoscope video camera is presented. This approach is based on the analysis of the images using the SIFT transform and a clustering method called k-means, jointly with the use of Support Vector Machines. The instrument identification might be used for recognizing the action the surgeon is performing during the intervention, which may be useful for monitoring or training purposes. By correlating the action which is being performed with the intervention protocol, a robotic assistant might warns about the correct order in which these actions should be done, or the time they should take, according to average measurements normally accepted by medical organizations. Other approaches, based on the analysis of the instruments trajectories and forces/torques exerted by the surgeon have been proponed by Rosen et al. That approach implies the need for attaching sensors to the handles used by the surgeon to manipulate the laparoscopic tools, which not always is possible or might be not admitted by the surgeon for his own comfort. The original aspect of this work is to take the images directly from the Camera Control Unit (CCU) of a laparoscopic system, which provides video signal output of the embedded camera, instead of wiring additional sensors, which have to be connected and calibrated each time a new intervention is started.
Analysis of mRNA Expression for Injury-Age Estimation

ackground: Determination of wound age is pivotal in forensic medical sciences, criminal and civil cases for the construction of crime scene and answering the questions like, time of infliction, manner of wound infliction, how long the person survives after infliction of wound and characterizing antemortem or postmortem wounds. The findings differ considerably among individuals due to the biological variations. Previous developed method in the injury-age determination is clinical, microscopic, enzymatic reaction at the wound margins, histological, and immunohistochemistry with the pitfalls associated with it.
Named Entity Recognition System for Hindi Language: A Hybrid Approach

Named Entity Recognition (NER) is a major early step in Natural Language Processing (NLP) tasks like machine translation, text to speech synthesis, natural language understanding etc. It seeks to classify words which represent names in text into predefined categories like location, person-name, organization, date, time etc. In this paper we have used a combination of machine learning and Rule based approaches to classify named entities. The paper introduces a hybrid approach for NER. We have experimented with Statistical approaches like Conditional Random Fields (CRF) & Maximum Entropy (MaxEnt) and Rule based approach based on the set of linguistic rules. Linguistic approach plays a vital role in overcoming the limitations of statistical models for morphologically rich language like Hindi. Also the system uses voting method to improve the performance of the NER system.
Perceived Stress, Anxiety, and Depression among Mizo Women

The present study aims to explore the relationship between perceived stress, anxiety, and depression among Mizo women. In addition, the study attempts to highlight the predictability of perceived stress and anxiety on depression. Data is collected from a random sample of forty-three (43) Mizo educated women in Aizawl, with their age ranging between 18 to 39 ( M= 28.42 years). Results indicate significant positive relationships between perceived stress, anxiety and depression. The results further show that the variables significantly predicted depression and that perceived stress is the best predictor of depression accounting for a very large proportion of the variance. Intervention programs for women that reduce perceived stress and anxiety are needed in order to manage their effects on depression.
Development of a stage-structured analytical population model for strategic decision making: the case of ticks and tick-borne diseases (**)

Mathematical models are an important tool for understanding disease systems in general and as aids for designing control and management strategies both for vectors and vector-borne diseases. In this paper a model is derived for the interaction of the brown ear tick (Rhipicephalus appendiculatus) with its hosts. First a general model consisting of a system of differential equations with constant coefficients on a stage structured tick population with unlimited host density is presented. The model is then improved by incorporating host abundance and availability by means of a demand-driven, ratio-dependent functional response model. The improved model adequately represents the dynamics of a stage structured vector population under conditions of varying host density. The model efficiently synthesizes existing information allowing for a qualitative evaluation of several management strategies and the identification of gaps in the actual understanding of the system. The model is expected to guide future research work in the area.
Ear Detection Techniques In Biometrics: A Survey

Due to an ever-growing need to automatically authenticate individuals, biometrics remained an active field of research over the course of the last decade. Biometric has been proved to be a reliable means of enforcing constraint in a security sensitive environment. Identifying the people through their ear is the emerging trend in the modern era. Human ear is the unique and clearly visible trait that is permanent for his/her lifetime. The increasing age of human being affects very less on the ear. Detection and recognition are the two major components of an automatic biometrics system. Ear detection is a critical component since the errors in this stage can undermine the utility of the biometric system. This paper provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might wish to exploit this new approach.
A Richardson-Lucy Algorithm Using a Varying Point Spread Function Along the Iterations

Image restorations with the Richardson-Lucy algorithm suffer the usual drawback imposed by the constraint of a constant Point Spread Function – PSF as unfolding function. Indeed, even when the image exhibits a constant spatial resolution over its whole surface, an important aspect is that as the iterations advance, the overall resolution is improved while the PSF remains constant. This work proposes an algorithm which restores images by the Richardson–Lucy (RL) algorithm, using however, a varying PSF as the iterations proceed. For this purpose, the PSF width is reduced to cope with the last-achieved image resolution and the next iteration would be carried out with it. The process is repeated until the PSF does not change significantly. A main point in this procedure is how to evaluate the PSF tied to the image resolution. In this work this is performed on the grounds that the global contrast increases with the resolution improvement, for many gray pixels migrate towards darker or brighter regions. Hence, deconvolving an image with a steadily increasing PSF width, somewhere a maximum global contrast would be reached, corresponding to the best PSF. Synthetic, as well as experimental images deconvolved with the proposed technique, outperform the final quality of the same ones treated with the original Richardson-Lucy algorithm for any number of iterations. The algorithm and ancillary procedures have been embedded into an ad hoc written Fortran 90 program capable to generate synthetic images and process them and the real ones.
On the Quest for Supply Chain Competence: The Impact of Competitive Strategy

In the epoch of globalization and electronic commerce, supply chain competence is one of the most critical core competences for many enterprises. However, the role of supply chain competence has seldom been addressed in the research of competitive strategy. This paper investigates the impact of business level strategy on supply chain competence. In this study, empirical data from enterprises were collected and analyzed to assess the association between business strategy and supply chain competence. The results supported our hypotheses and the implications for management decisions are elaborated.
Abstraction Without Regret for Efficient Data Processing

ion Without Regret for Efficient Data Processing Tiark Rompf ‡∗ Nada Amin∗ Thierry Coppey† Mohammad Dashti† Manohar Jonnalagedda∗ Yannis Klonatos† Martin Odersky∗ Christoph Koch† ‡Oracle Labs: {first.last}@oracle.com ∗Programming Methods Lab / † DATA Lab, EPFL: {first.last}@epfl.ch Growing data sets require efficiency on all levels of the processing stack. This leads to a trade-off between generality and specialization: On the one hand, we want reusable, generic solutions that can support many different kinds of data and many different processing tasks. But on the other hand, programs need to be specialized to data schemata and execution environments to obtain good performance. To give a real-world example, popular open-source and commercial database systems have been shown [10, 12] to perform 10 or 100x worse on certain queries than specialized, hand-written C implementations of the same query. At the same time such systems contain hundreds of thousands of lines of optimized C code, which suggests that manual optimization may not be cost effective. The database community has realized this problem, with prominent researchers arguing to replace generic database systems with specialized solutions [9]. In this talk, we make the case for a) more collaboration between DB and PL researchers, and b) for using cutting-edge PL technology such as generative metaprogramming (staging [11]) to turn interpreters, which are ubiquitous in data processing pipelines, into compilers. We present a range of examples from previous and ongoing work in the context of Scala and LMS (Lightweight Modular Staging) [8], including recent collaborative efforts on developing database systems using these techniques. As a takeaway for programming language designers, we further argue that for truly expressive multi-stage programming, quotation mechanisms should offer more semantics-preservation guarantees, in particular about maintaining statement execution order across stage boundaries. Motivating Example Let us consider a small programming example in Scala. We would like to implement a generic library function to read CSV files. A CSV file contains tabular data, where the first line defines the schema, i.e. the names of the columns. We would like to iterate over all the rows in a file and access the data fields by name: processCSV("data.txt") { record => // sample data: if (record("Flag") == "yes") // Name, Value, Flag println(record("Name")) // A, 7, no
AR-based Method for ECG Classification and Patient Recognition

The electrocardiogram (ECG) is the recording of heart activity obtained by measuring the signals from electrical contacts placed on the skin of the patient. By analyzing ECG, it is possible to detect the rate and consistency of heartbeats and identify possible irregularities in heart operation. This paper describes a set of techniques employed to pre-process the ECG signals and extract a set of features – autoregressive (AR) signal parameters used to characterise ECG signal. Extracted parameters are in this work used to accomplish two tasks. Firstly, AR features belonging to each ECG signal are classified in groups corresponding to three different heart conditions – normal, arrhythmia and ventricular arrhythmia. Obtained classification results indicate accurate, zero-error classification of patients according to their heart condition using the proposed method. Sets of extracted AR coefficients are then extended by adding an additional parameter – power of AR modelling error and a suitability of developed technique for individual patient identification is investigated. Individual feature sets for each group of detected QRS sections are classified in p clusters where p represents the number of patients in each group. Developed system has been tested using ECG signals available in MIT/BIH and Politecnico of Milano VCG/ECG database. Achieved recognition rates indicate that patient identification using ECG signals could be considered as a possible approach in some applications using the system developed in this work. Pre-processing stages, applied parameter extraction techniques and some intermediate and final classification results are described and presented in this paper.
SVM Based Recognition of Facial Expressions Used In Indian Sign Language

In sign language systems, facial expressions are an intrinsic component that usually accompanies hand gestures. The facial expressions would modify or change the meaning of hand gesture into a statement, a question or improve the meaning and understanding of hand gestures. The scientific literature available in Indian Sign Language (ISL) on facial expression recognition is scanty. Contrary to American Sign Language (ASL), head movements are less conspicuous in ISL and the answers to questions such as yes or no are signed by hand. Purpose of this paper is to present our work in recognizing facial expression changes in isolated ISL sentences. Facial gesture pattern results in the change of skin textures by forming wrinkles and furrows. Gabor wavelet method is well-known for capturing subtle textural changes on surfaces. Therefore, a unique approach was developed to model facial expression changes with Gabor wavelet parameters that were chosen from partitioned face areas. These parameters were incorporated with Euclidian distance measure. Multi class SVM classifier was used in this recognition system to identify facial expressions in an isolated facial expression sequences in ISL. An accuracy of 92.12 % was achieved by our proposed system.
Poster: Leveraging Prior Knowledge Asymmetries in the Design of IoT Privacy-Preserving Mechanisms

The Internet of Things (IoT) promises to improve user utility by tuning applications to user behavior, but the revealing of the characteristics of a user’s behavior presents a significant privacy risk. Recently, the technique of remapping has been introduced in the privacy literature. Remapping exploits asymmetries in knowledge and/or sophistication between the intended application and the adversary; in particular, the user publishes a more accurate version of her data than they might have otherwise because a sophisticated adversary could obtain that accurate version anyway. Here, after introducing the system model, we first demonstrate the mechanism behind the remapping technique. Next, we characterize the loss in privacy when the user lacks knowledge of the accuracy of the adversary’s statistical model; this loss in privacy occurs both because the adversary obtains a more accurate view of the user data than expected and because the adversary can exploit the remapping to improve their statistical model more than would have been possible when remapping is not employed. Finally, we introduce a random remapping approach as a countermeasure; in particular, for a given utility, the random remapping approach makes it difficult for the adversary to improve their statistical model.
Breaking out of the Glassbox: Employee Responses to Disability Inclusion in the Workforce in Thailand

Many organizations and enterprises have been dealing with diversity and inclusion in the workforce covering differences in culture, age, race, and disability among many others. This study will focus on the diversity brought about by disability, or difference in capabilities, in the workforce particularly exploring employee perspectives to disability inclusion in the workplace. A phenomenological analysis was conducted based on the qualitative data gathered through semi-structured interviews with employees who have experienced working with People With Disabilities (PWD). This research extracted themes surrounding employees’ perceptions formed from their experiences working with disabled colleagues, as well as link its possible effects to the formation of organizational culture and shared values among the employees in the enterprise. The findings show that employees working in a disability-inclusive environment had enhanced motivation to work harder and gained a sense of pride as influenced by their disabled colleagues. Furthermore, a culture of open-mindedness and equity sensitivity were also observed in the employees who have experienced working with disabled people. This study will be deemed useful in further understanding employee responses to disability inclusion and give insights into the management of diversity in organizations.
Global aspects of the space of 6D N = 1 supergravities

We perform a global analysis of the space of consistent 6D quantum gravity theories withN = 1 supersymmetry, including models with multiple tensor multiplets. We prove that for theories with fewer than T = 9 tensor multiplets, a nite number of distinct gauge groups and matter content are possible. We
Hybrid Beamforming for Multi-User Millimeter-Wave Networks

This paper considers hybrid beamforming by combining an analog beamformer with a new regularized zero forcing baseband one, for multi-user millimeter-wave networks under a limited number of radio frequency (RF) chains. Three popular scenarios are examined: i) the number of users is up to the number of RF chains in a single-cell network, ii) the number of users is up to twice the number of RF chains in a single-cell network, and iii) the number of users is up to twice the number of RF chains in each cell of a two-cell network. In the second and third scenarios, we group the users into two categories of cell-center users as well as cell-edge users and serve them in two different time fractions. In the third scenario, we propose to suppress the inter-cell interference by serving the cell-center and cell-edge users in alternate fractional-time slots. In all the three scenarios, we determine the optimal power allocation maximizing the users’ minimum rate. Finally, low-complexity path-following algorithms having rapid convergence are developed for the computation of the optimal power. Our simulation results show that the proposed algorithms achieve a clear performance gain over the existing benchmarkers.