Miracle-dz/UniFAR-FTUs huggingface dataset:
Simplified model for on-chip interconnects in electromagnetic modelling of System-in-Package

We present effective medium approximation (EMA) that provides an homogeneous equivalent for the top layer of on-chip interconnects in an integrated circuit. An empirical mixing model for straightforward and fast extraction of the effective dielectric constant of a slab of integrated circuit wiring and its analytical formulation based on Maxwell-Garnett rule is presented for a range of aspect ratios, dielectric materials, metal fills, and frequencies. We expect this approach to find use in the electromagnetic modeling of System-in-Package. Experimental validation of proposed approach is demonstrated by free space measurement of a grating structure illuminated by a pair of lens horn antennas.
Positional versus directional control loops in visuomotor pointing

A psychophysical study of visual motion perception suggested that two different feedback loops may be separately involved in the visual guidance of goal directed movement (Paillard & Amblard, 1985). The present study addresses this hypothesis by examining the performance of human subjects in pointing and aiming tasks which required visual feedback for the correction of distance and direction errors
KNOWLEDGE MANAGEMENT FOR THE CONSTRUCTION INDUSTRY: THE E-COGNOS PROJECT

The paper focuses upon the contribution which adequate use of the latest development in Information and Communication Technologies can make to the enhancement, development and improvement of professional expertise in the Construction domain. The paper is based on the e-COGNOS project, which aims at specifying and developing an open model-based infrastructure and a set of tools that promote consistent knowledge management within collaborative construction environments. The specified solution emerged from a comprehensive analysis of the business and information / knowledge management practices of the project endusers, and makes use of a Construction specific ontology that is used as a basis for specifying adaptive mechanisms that can organise documents according to their contents and interdependencies, while maintaining their overall consistency. The e-Cognos web-based infrastructure will include services, which allow the creation, capture, indexing, retrieval and dissemination of knowledge. It also promotes the integration of third-party services, including proprietary tools. The e-COGNOS approach will be tested and evaluated through a series of field trials. This will be followed by the delivery of business recommendations regarding the deployment of eCOGNOS in the construction sector. The research is ongoing and supported by the European Commission under the IST programme – Key Action II.
Wavelet-Based Parameter Estimation for Trend Contaminated Fractionally Differenced Processes

A common problem in the analysis of time series is how to deal with a possible trend component, which is usually thought of as large scale (or low frequency) variations or patterns in the series that might be best modeled separately from the rest of the series. Trend is often confounded with low frequency stochastic fluctuations, particularly in the case of models such as fractionally differenced (FD) processes, that can account for long memory dependence (slowly decaying autocorrelation) and can be extended to encompass nonstationary processes exhibiting quite significant low frequency components. In this paper we assume a model of polynomial trend plus FD noise and apply the discrete wavelet transform (DWT) to separate a time series into pieces that can be used to estimate both the FD parameters and the trend. The estimation of the FD parameters is based on an approximate maximum likelihood approach that is made possible by the fact that the DWT decorrelates FD processes approximately. We demonstrate our methodology by applying it to a popular climate dataset.
Performance ratios for the Karmarker-Karp differencing method

We consider the multiprocessor scheduling problem in which one must schedule n independent tasks nonpreemptively on m identical, parallel machines, such that the completion time of the last task is minimal. For this well-studied problem the Largest Differencing Method due to Karmarkar and Karp outperforms other existing polynomial-time approximation algorithms from an average-case perspective. For m (cid:1) 3, its worst-case performance has remained a challenging open problem. In this paper, we show that its performance ratio is bounded between 43 (cid:2) 1 3 (cid:3) m (cid:4) 1 (cid:5) and 43 (cid:2) 13 m . We also analyze the performance ratio if in addition to the number of machines, the number of tasks n is (cid:2)xed as well.
Improved algorithms for exact and approximate boolean matrix decomposition

An arbitrary m×n Boolean matrix M can be decomposed exactly as M = UοV, where U (resp. V) is an m×k (resp. k ×n) Boolean matrix and ο denotes the Boolean matrix multiplication operator. We first prove an exact formula for the Boolean matrix J such that M = MοJT holds, where J is maximal in the sense that if any 0 element in J is changed to a 1 then this equality no longer holds. Since minimizing k is NP-hard, we propose two heuristic algorithms for finding suboptimal but good decomposition. We measure the performance (in minimizing k) of our algorithms on several real datasets in comparison with other representative heuristic algorithms for Boolean matrix decomposition (BMD). The results on some popular benchmark datasets demonstrate that one of our proposed algorithms performs as well or better on most of them. Our algorithms have a number of other advantages: They are based on exact mathematical formula, which can be interpreted intuitively. They can be used for approximation as well with competitive “coverage. ” Last but not least, they also run very fast. Due to interpretability issues in data mining, we impose the condition, called the “column use condition,” that the columns of the factor matrix U must form a subset of the columns of M. In educational databases, the “ideal item response matrix” R, the “knowledge state matrix” A and the “Q-matrix” Q play important roles. We show that they are related exactly by R̅ = A ̅ο QT. Thus, given R, we can find A and Q with a small number (k) of “knowledge states,” using our exact BMD heuristics.
Green Resource Allocation Algorithms for Publish/Subscribe Systems

A popular trend in large enterprises today is the adoption of green IT strategies that use resources as efficiently as possible to reduce IT operational costs. With the publish/subscribe middleware playing a vital role in seamlessly integrating applications at large enterprises including Google and Yahoo, our goal is to search for resource allocation algorithms that enable publish/subscribe systems to use system resources as efficiently as possible. To meet this goal, we develop methodologies that minimize system-wide message rates, broker load, hop count, and the number of allocated brokers, while maximizing the resource utilization of allocated brokers to achieve maximum efficiency. Our contributions consist of developing a bit vector supported resource allocation framework, designing and comparing four different classes with a total of ten variations of subscription allocation algorithms, and developing a recursive overlay construction algorithm. A compelling feature of our work is that it works under any arbitrary workload distribution and is independent of the publish/subscribe language, which makes it easily applicable to any topic and content-based publish/subscribe system. Experiments on a cluster testbed and a high performance computing platform show that our approach reduces the average broker message rate by up to 92% and the number of allocated brokers by up to 91%.
Industrial robotic assembly process modeling using support vector regression

The process parameters of high precision robotic assembly have to be tuned in order to deal with part variations and system uncertainties. Some methods such as design-of-experiment, artificial neural network and genetic algorithms have been proposed to optimize these parameters offline. However, these parameters have to be retuned for different batches due to part variations, which increases the production cost and lowers the manufacturing efficiency. Therefore new methods have to be developed to solve the problem. Because of the complexity of high precision assembly process, it is challenging to build a physical model to establish the relationship between an assembly process and its process parameters. Therefore we propose an assembly process modeling method based on support vector regression that constructs a model by observing the relationship between the assembly parameters and assembly output. The effectiveness and accuracy of the support vector regression based algorithm are further demonstrated by experiments using a robotic valve body assembly process in automotive manufacturing. The results show that the proposed method is capable of modeling complex assembly processes.
Identification of artesunate as a specific activator of ferroptosis in pancreatic cancer cells

Oncogenic KRas reprograms pancreatic ductal adenocarcinoma (PDAC) cells to states which are highly resistant to apoptosis. Thus, a major preclinical goal is to identify effective strategies for killing PDAC cells. Artesunate (ART) is an anti-malarial that specifically induces programmed cell death in different cancer cell types, in a manner initiated by reactive oxygen species (ROS)-generation. In this study we demonstrate that ART specifically induced ROS- and lysosomal iron-dependent cell death in PDAC cell lines. Highest cytotoxicity was obtained in PDAC cell lines with constitutively-active KRas, and ART did not affect non-neoplastic human pancreatic ductal epithelial (HPDE) cells. We determined that ART did not induce apoptosis or necroptosis. Instead, ART induced ferroptosis, a recently described mode of ROS- and iron-dependent programmed necrosis which can be activated in Ras-transformed cells. Co-treatment with the ferroptosis inhibitor ferrostatin-1 blocked ART-induced lipid peroxidation and cell death, and increased long-term cell survival and proliferation. Importantly, analysis of PDAC patient mRNA expression indicates a dependency on antioxidant homeostasis and increased sensitivity to free intracellular iron, both of which correlate with Ras-driven sensitivity to ferroptosis. Overall, our findings suggest that ART activation of ferroptosis is an effective, novel pathway for killing PDAC cells.
Semi-Automatic Entity Set Refinement

State of the art set expansion algorithms produce varying quality expansions for different entity types. Even for the highest quality expansions, errors still occur and manual refinements are necessary for most practical uses. In this paper, we propose algorithms to aide this refinement process, greatly reducing the amount of manual labor required. The methods rely on the fact that most expansion errors are systematic, often stemming from the fact that some seed elements are ambiguous. Using our methods, empirical evidence shows that average R-precision over random entity sets improves by 26% to 51% when given from 5 to 10 manually tagged errors. Both proposed refinement models have linear time complexity in set size allowing for practical online use in set expansion systems.
Online 3D world reconstruction with independently moving point detection using stereo vision

This paper deals with a problem of 3D world reconstruction and independently moving point detection using stereo vision. Our algorithm reconstructs 3D world through dense stereo matching, triangulation at local frame, and registration in world frame. In addition, independently moving points are detected by geometric constraints. We have tested our method with real outdoor datasets. The proposed algorithm runs online. Hence, it can apply to mobile platform applications such as intelligent vehicles and mobile robots.
Identity Based Key Encapsulation with Wildcards

We propose a hybrid (KEM/DEM) model for the recently proposed primitive of identity-based encryption with wildcards (WIBE), and confirm that the hybrid construction is secure. We also propose new chosenciphertext secure WIBE schemes that have somewhat more efficient security reductions and some performance benefits. Our first construction is a generic one from any one-way secure WIBE in the random oracle model, the second is a direct construction in the standard model.
Biochemistry and evolutionary biology: Two disciplines

Biochemical information has been crucial for the development of evolutionary biology. On the one hand, the sequence information now appearing is producing a huge increase in the amount of data available for phylogenetic analysis; on the other hand, and perhaps more fundamentally, it allows understanding of the mechanisms that make evolution possible. Less well recognized, but just as important, understanding evolutionary biology is essential for understanding many details of biochemistry that would otherwise be mysterious, such as why the structures of NAD and other coenzymes are far more complicated than their functions would seem to require. Courses of biochemistry should thus pay attention to the essential role of evolution in selecting the molecules of life.
Linking stomatal traits and expression of slow anion channel genes HvSLAH1 and HvSLAC1 with grain yield for increasing salinity tolerance in barley

Soil salinity is an environmental and agricultural problem in many parts of the world. One of the keys to breeding barley for adaptation to salinity lies in a better understanding of the genetic control of stomatal regulation. We have employed a range of physiological (stomata assay, gas exchange, phylogenetic analysis, QTL analysis), and molecular techniques (RT-PCR and qPCR) to investigate stomatal behavior and genotypic variation in barley cultivars and a genetic population in four experimental trials. A set of relatively efficient and reliable methods were developed for the characterization of stomatal behavior of a large number of varieties and genetic lines. Furthermore, we found a large genetic variation of gas exchange and stomatal traits in barley in response to salinity stress. Salt-tolerant cultivar CM72 showed significantly larger stomatal aperture under 200 mM NaCl treatment than that of salt-sensitive cultivar Gairdner. Stomatal traits such as aperture width/length were found to significantly correlate with grain yield under salt treatment. Phenotypic characterization and QTL analysis of a segregating double haploid population of the CM72/Gairdner resulted in the identification of significant stomatal traits-related QTLs for salt tolerance. Moreover, expression analysis of the slow anion channel genes HvSLAH1 and HvSLAC1 demonstrated that their up-regulation is linked to higher barley grain yield in the field.
Dispersion for the Schr\"odinger equation on the line with multiple Dirac delta potentials and on delta trees

In this paper we consider the time dependent one-dimensional Schr\"odinger equation with multiple Dirac delta potentials {of different strengths}. We prove that the classical dispersion property holds under some restrictions on the strengths and on the lengths of the finite intervals. The result is obtained in a more general setting of a Laplace operator on a tree with $\delta$-coupling conditions at the vertices. The proof relies on a careful analysis of the properties of the resolvent of the associated Hamiltonian. With respect to the analysis done in \cite{MR2858075} for Kirchhoff conditions, here the resolvent is no longer in the framework of Wiener algebra of almost periodic functions, and its expression is harder to analyze.
ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble Classifier

Protein fold classification plays an important role in both protein functional analysis and drug design. The number of proteins in PDB is very large, but only a very small part is categorized and stored in the SCOPe database. Therefore, it is necessary to develop an efficient method for protein fold classification. In recent years, a variety of classification methods have been used in many protein fold classification studies. In this study, we propose a novel classification method called proFold. We import protein tertiary structure in the period of feature extraction and employ a novel ensemble strategy in the period of classifier training. Compared with existing similar ensemble classifiers using the same widely used dataset (DD-dataset), proFold achieves 76.2% overall accuracy. Another two commonly used datasets, EDD-dataset and TG-dataset, are also tested, of which the accuracies are 93.2% and 94.3%, higher than the existing methods. ProFold is available to the public as a web-server.
A Systematic Approach for Synchronization of Identical Nosé-Hoover Systems with Multiple Number of Thermostats

This paper introduces a systematic approach to synchronize identical Nosé-Hoover systems that can have multiple number of thermostats. Synchronization is achieved via using a Lyapunov-based approach for which both stability and performance satisfaction are investigated. Both modeling and simulation of the system are presented using different scenarios to verify its similarity to chaotic systems that are very sensitive to initial conditions. The proposed design is exemplified by two case studies that correspond to one and two thermostats. The tuning effort of the proposed controller is explored and conflicts between maximum control effort and fast decay rates of the synchronization errors are investigated. The suitability of the proposed synchronization technique to implement secure communication systems is verified and robustness of the design is highlighted due to its capability to utilize a single state for achieving synchronization. Finally a few practical considerations are investigated, along with suggestions for feasible improvements and extensions to the proposed design. MATLAB was used for the simulations.
Design of an eye movement-controlled wheelchair using Kalman filter algorithm

Nowadays, electric wheelchairs on the market mostly were controlled by joystick which required users to operate by hands flexibly. But this wheelchair cound not be used by patients with hand disability or paraplegics. This paper designs an electric wheelchair controlled by eye movements. An eye tracker of Tobii is connected with a electric wheelchair. The eye tracker of Tobii is used to convert eye movement signals into coordinate of gazing points. But these data of gazing points are too diverging to be used directly. Kalman filter algorithm is used to filter data of coordinate and obtain optimal data. By discriminating eye movement-controlled panel area where gazing points is located in, a computer sends appropriate signal to the wheelchair, so as to achieve the goal of moving wheelchair. Thus patients are no longer using hands to control electric wheelchairs.
Race, Gender, and Chains of Disadvantage

We use a life course approach to guide an investigation of relationships and health at the nexus of race and gender. We consider childhood as a sensitive period in the life course, during which significant adversity may launch chains of disadvantage in relationships throughout the life course that then have cumulative effects on health over time. Data from a nationally representative panel study (Americans’ Changing Lives, N = 3,477) reveal substantial disparities between black and white adults, especially pronounced among men, in the quality of close relationships and in the consequences of these relationships for health. Greater childhood adversity helps to explain why black men have worse health than white men, and some of this effect appears to operate through childhood adversity’s enduring influence on relationship strain in adulthood. Stress that occurs in adulthood plays a greater role than childhood adversity in explaining racial disparities in health among women.
Model of Video on Demand Service Provisioning on Multiple Third Party Cloud Storage Services

In this paper, we present a solution model to tackle the problem of providing Video-on-Demand (VoD) using cloud computing storage service composition. We present related works, the problem motivation and some preliminary results. As part of the problem, we study the performance and scalability model for this VoD cloud service by performing a statistical analysis and Principal Component Analysis (PCA) on real cloud data. In order to simplify the stochastic modeling, we created a Characteristic Cloud Delay Time trace (using PCA), and determined the self- similarity nature of the data itself to pursue modeling using heavy-tailed probability distributions.
A Review on Feature Extraction for Indian and American Sign Language

In the field of human computer interaction (HCI) Sign Language have been the emphasis of significant research in real time. Such systems are advance and they are meant to substitute interpreters. Recently several techniques have been advanced in this area with the improvement of image processing and artificial intelligence techniques. Indian Sign Language(ISL) are double handed and hence it is more intricate compare to single handed American Sign Language(ASL). Many researchers use only single ASL or ISL sign for creating their database. In this paper recent research and development of sign language are reviewed based on manual communication and body language. Sign language recognition system typically elaborate three steps preprocessing, feature extraction and classification. Classification methods used for recognition are Neural Network(NN), Support Vector Machine(SVM), Hidden Markov Models(HMM), Scale Invariant Feature Transform (SIFT),etc. Keywords-Feature extraction, Hidden Markov Model(HMM) , Neural Network (NN), Support Vector Machine(SVM), Scale Invariant Feature Transform.
Smart-sensor placement optimization under energy objectives

Over the last decade, France has seen its electrical consumption increase by 15 percent. The root causes are multiple: demographic growth, electrical heating, an increasing use of electronic devices, etc. Meanwhile, the development of smart technologies for lighting and control offers new opportunities to significantly reduce energy consumption. Using smart technologies such as LEDs combined with a smart system reduces by 62 percent the energy consumption usually spent with neon tubes. This paper addresses the problem of optimizing the number of motion and brightness sensors in a given office area and to cover pre-identified zones (e.g. doors, critical areas, etc.) while balancing costs and performance objectives. Such problems are crucial for the (reconfiguration of critical sites which are facing more and more complex issues in security and energy consumption related matters. We chose evolutionary algorithms which are well-known for addressing multi-objective problems where, for instance, one minimizes costs on one hand and maximizes performance on the other. Our first results are very encouraging.
Columbus - reverse engineering tool and schema for C++

One of the most critical issues in large-scale software development and maintenance is the rapidly growing size and complexity of software systems. As a result of this rapid growth there is a need to better understand the relationships between the different parts of a large software system. In this paper we present a reverse engineering framework called Columbus that is able to analyze large C++ projects, and a schema for C++ that prescribes the form of the extracted data. The flexible architecture of the Columbus system with a powerful C++ analyzer and schema makes it a versatile and readily extendible toolset for reverse engineering. This tool is free for scientific and educational purposes and we fervently hope that it will assist academic persons in any research work related to C++ re- and reverse engineering.