Miracle-dz/UniFAR-FTUs huggingface dataset:
Oxaliplatin but Not Irinotecan Impairs Posthepatectomy Liver Regeneration in a Murine Model

Introduction. We examined the murine hepatectomy model of liver regeneration (LR) in the setting of neoadjuvant chemotherapy. Methods. C57BL/6 mice were randomized to receive neoadjuvant intraperitoneal (IP) injections of a control, oxaliplatin (15 mg/kg), or irinotecan (100 mg/Kg or 250 mg/Kg) solution. Hepatectomy (70%) was performed 14 days after the final IP treatment. Animals were sacrificed at postoperative day (D) 0, 1, 2, 3, and 7. Liver remnants and serum were collected for analysis. T-tests for independent samples were used for statistical comparisons. Results. For oxaliplatin, percent LR did not differ at D1 or D2 but was significantly less at D3 (89.0% versus 70.0%, P = 0.048) with no difference on D7 (P = 0.21). Irinotecan-treated mice at both dose levels (100 mg/Kg and 250 mg/Kg) showed no significant differences in LR. BrdU incorporation was significantly decreased in oxaliplatin-treated animals (D1,2,3). Conclusions. Neoadjuvant oxaliplatin but not irinotecan impairs early LR in a posthepatectomy murine model which correlates with decreased DNA synthesis.
Target recognition in SAR images via graph wavelet transform and 2DPCA

In this paper, we propose a new classification method based on graph wavelet filter banks and 2DPCA for target recognition in synthetic aperture radar (SAR) image. Graph wavelet transformation can provide multi-scale analysis similar to traditional wavelet transform, and it effectively detects image edge information in irregular domain. Therefore, the radar image is transformed into a graph wavelet domain using the Meyer spectral kernel function. Due to the two-dimensional PCA (2DPCA), developed from PCA, is common in pattern recognition and can extract features from two-dimensional SAR image directly, we introduce it to get features. The vectors derived from 2DPCA at different scales are then applied into the metasample-based sparse representation classifier (MSRC). Experiments on the moving and stationary acquisition and recognition (MSTAR) dataset demonstrate that the proposed method leads to an improvement in the recognition rate.
Feasible Requirements Model Based on the Holistic Problem-Solving Approach and Best Practices to Achieve the Success of an Erp Implementation

Investments in systems such as an Enterprise Resource Planning (ERP) are carried out by companies with the objective of developing capabilities and generating competitive advantages, as well as creating value and opportunities for long-term differential benefits. The effectiveness of the ERP is the outcome of a successful implementation of the project. Implementing an ERP system is a major complex project demanding a significant level of resources, commitment, and adjustments throughout the organization. While leading vendor's methodologies are more useful to implement the ERP projects, the gap between the functionality of the ERP systems and the enterprise's requirements is one of the most common problems during the implementation, causing the delay of the project, the over budget and the need of additional funding. Using the holistic approach to problem-solving and best practices, this paper presents a model to be executed before the beginning of the project to create feasible requirements, which should let companies to move from a poor definition of requirements to more explicit and achievable requirements as well as reach a better estimate of the cost and time of the project to achieve the success of the project. The paper also called the model as "Phase 0". Three case studies demonstrated the effectiveness and feasibility of the model.
Broadening the Conceptualization of Theory in the Information Systems Discipline: A Meta-Theory Approach

The traditional and most prevalent view of theory in the information systems (IS) discipline is conceptualized in what has been termed ? variance theory," or more properly ? variance meta-theory," where a meta-theory is a means by which to conceptualize the types of constructs and relationships used to develop a specific theory (instance). Variance meta-theory conceptualizes the constructs and relationships in a theory strictly as properties of entities, interrelated in a static (statistical) correlational manner. Theories formulated using variance meta-theory generally seek to explain or predict immutable inferred causal relationships. This view of theory is limiting and constraining in an applied field like IS where phenomena of interest are complex, constantly changing, and reliant on proactive human actions, as well as underlying forces. As new technologies are developed, users adapt and learn, take actions, and respond to results. We argue that alternative meta-theories more readily address this wider range of IS research questions. We examine three such meta-theories ? network, process, and co-evolution, comparing and contrasting their underlying conceptualizations, entities, relationships, and methodologies. We argue further that theory (instances) formulated in the language of each of these meta-theories can be used as part of a broad scientific process of proposing, testing, and reevaluating theory to develop increasingly nuanced understandings of IS phenomena, ultimately resulting in the accumulation of knowledge that is relevant to both theory and practice.
An Affect-Responsive Interactive Photo Frame

We develop an interactive photo-frame system in which a series of videos of a single person are automatically segmented and a response logic is derived to interact with the user in real-time. The system is composed of five modules. The first module analyzes the uploaded videos and prepares segments for interactive play, in an offline manner. The second module uses multi-modal input (activity levels, facial expression, etc.) to generate a user state. These states are used by the internal frame logic, the third module, to select segments from the offline-generated segment dictionary, and they determine the response of the system. A continuous video stream is synthesized from the prepared segments in accordance with the modeled state of the user. The system logic includes online/offline adaptation, which is based on stored input-output pairs during real-time operation, and offline learning to improve the system response. The fourth module is the application interface, which deals with handling the input and output streams. Finally, a dual-frame module is described to enhance the use of the system.
Restrictions on Monadic Context-Free Tree Grammars

In this paper, subclasses of monadic contextfree tree grammars (CFTGs) are compared. Since linear, nondeleting, monadic CFTGs generate the same class of string languages as tree adjoining grammars (TAGs), it is examined whether the restrictions of linearity and nondeletion on monadic CFTGs are necessary to generate the same class of languages. Epsilon-freeness on linear, nondeleting, monadic CFTG is also examined.
Finding All DC Operating Points of Piecewise-Linear Circuits Containing Neither Voltage nor Current Controlled Resistors

Recently, efficient algorithms have been proposed for finding all characteristic curves of one-port piecewise-linear resistive circuits. Using these algorithms, a middle scale one-port circuit can be represented by a piecewise-linear resistor that is neither voltage nor current controlled. In this letter, an efficient algorithm is proposed for finding all dc operating points of piecewise-linear circuits containing such neither voltage nor current controlled resistors.
Literature Review - Fibromyalgia

Fibromyalgia Syndrome (FMS) is the name given to a group of symptoms characterised by widespread muscular pain and stiffness (Arthritis Australia, 2011). Other common symptoms are extreme fatigue impacting on day to day activities, difficulty sleeping, increased sensitivity to stimuli such as heat, cold and smell, memory and concentration problems (Arthritis Victoria, 2010). Depression and anxiety can also be symptoms although it is possible that these symptoms are as a result of the other symptoms.
Novel compound heterozygous mutations in MYO7A in a Chinese family with Usher syndrome type 1

Purpose To identify the disease-causing mutation(s) in a Chinese family with autosomal recessive Usher syndrome type 1 (USH1). Methods An ophthalmic examination and an audiometric test were conducted to ascertain the phenotype of two affected siblings. The microsatellite marker D11S937, which is close to the candidate gene MYO7A (USH1B locus), was selected for genotyping. From the DNA of the proband, all coding exons and exon-intron boundaries of MYO7A were sequenced to identify the disease-causing mutation(s). Restriction fragment length polymorphism (RFLP) analysis was performed to exclude the alternative conclusion that the mutations are non-pathogenic rare polymorphisms. Results Based on severe hearing impairment, unintelligible speech, and retinitis pigmentosa, a clinical diagnosis of Usher syndrome type 1 was made. The genotyping results did not exclude the USH1B locus, which suggested that the MYO7A gene was likely the gene associated with the disease-causing mutation(s) in the family. With direct DNA sequencing of MYO7A, two novel compound heterozygous mutations (c.3742G>A and c.6051+1G>A) of MYO7A were identified in the proband. DNA sequence analysis and RFLP analysis of other family members showed that the mutations cosegregated with the disease. Unaffected members, including the parents, uncle, and sister of the proband, carry only one of the two mutations. The mutations were not present in the controls (100 normal Chinese subjects=200 chromosomes) according to the RFLP analysis. Conclusions In this study, we identified two novel mutations, c.3742G>A (p.E1248K) and c.6051+1G>A (donor splice site mutation in intron 44), of MYO7A in a Chinese non-consanguineous family with USH1. The mutations cosegregated with the disease and most likely cause the phenotype in the two affected siblings who carry these mutations compound heterozygously. Our finding expands the mutational spectrum of MYO7A.
A computer aided architecture design tool aimed at image processing applications

SAGAPA (System Architecture Generated from A Parallel Algorithm) is an image processing system design tool based on an original methodology that automatically carries out the minimal architecture adapted to the application algorithm in respect with a maximal execution time constraint. The obtained architecture is minimal in terms of number of processors, amount of memory, and complexity of interconnection network. An execution-driven type simulation is performed in order to estimate resource's utilization and to check the time constraint.
The Bidirectional Inference Calculation Model of Multimedia Image Semantics Perception

It is vital that we must have a major breakthrough on image semantic perception before text-based image retrieval can be replaced by content-based methods, hereby finding that solution becomes the theme of our article. We have drawn a framework of a calculation model built on the foundation of modern logic, cognitive psychology and multimedia technology, which involves the usage of eye tracker. Still it is a framework needs detailing, but it has already opened anew prospect of theoretical research assisted with practical engineering.
A New Program Classification Method Based on Binary Instrumentation and Instruction Flow Feature Extraction

With the development of Internet technology, more and more unknown programs appears in the network environment, the detection and classification technology is increasingly becoming an important technology in the field of information security. This paper presents a new method of program classification using binary instrumentation, dynamic instruction flow feature extraction, auto feature selection and Naive Bayes classifier technology. Finally we use two-class classification, five categories, and twenty-class classification experiments to prove the correctness of the method and present the future direction for improvement of the method.
Concepts et algorithmes pour la découverte des structures formelles des langues. (Concepts and Algorithms for Discovering Formal Structures of Languages)

Que peut-on apprendre sur la structure d'une langue a partir d'un texte ecrit dans cette langue, et ceci sans connaissance particuliere sur celle-ci et avec l'aide (disons l'utilisation) d'un ordinateur? Voila la question a laquelle nous avons essaye de repondre. Cette reponse peut etre vue comme une continuation des travaux en analyse distributionnelle developpee par Zellig Harris. L'objectif de ce travail est donc de decouvrir les structures formelles d'une langue en etudiant ces regularites formelles contenues dans un corpus Notre methode de decouverte se base sur une simple conception formelle de la langue: un objet lineaire dans lequel les frontieres (de debut et de fin) des differentes structures sont indiquees par des elements caracteristiques. Les structures ainsi identifiees sont le syntagme simple (non recursif), et la proposition, structures a la fois multilingues et formelles. Ces indicateurs de frontieres correspondent a des morphemes (libres ou lies) pour le syntagme, et a des morphemes ou des syntagmes pour la proposition. A partir de ces structures theoriques, nous construisons la liste de toutes les categories qu'un element (morpheme ou mot) peut prendre. Une fois ces structures et categories recensees, nous construisons des contextes specifiques a chaque categorie afin de categoriser les elements du texte. Nous obtenons donc un mecanisme permettant d'assigner a un element plusieurs categories si cet element apparait dans differents contextes. Ces contextes sont construits a l'aide des elements prototypiques de marqueurs de frontieres de structures, identifiables grâce a leur position par rapport a la segmentation physique du texte (en particulier les ponctuations). Les resultats obtenus permettent la categorisation des mots du corpus, ainsi qu'une segmentation partielle en syntagmes. La methode a ete appliquee a une dizaine de langues comme le francais, l'allemand, le turc, le vietnamien et le swahili.
A Fractional Laplace Equation: Regularity of Solutions and Finite Element Approximations

This paper deals with the integral version of the Dirichlet homogeneous fractional Laplace equation. For this problem weighted and fractional Sobolev a priori estimates are provided in terms of the Holder regularity of the data. By relying on these results, optimal order of convergence for the standard linear finite element method is proved for quasi-uniform as well as graded meshes. Some numerical examples are given showing results in agreement with the theoretical predictions.
Visual cues-based deception detection using two-class neural network

The deception detection technique which helps to analyse a person without his knowledge is convenient and effective than other methods of deception detection. In this paper, facial visual cues-based deception detection study is performed. In this study, an experiment was conducted with the participation of 62 subjects. Facial muscle variations of lie and truth responses of the subjects were recorded using a high speed camera and the corresponding action units (AUs) were trained and then tested for truth and lie prediction using two-class neural network. The prediction performance was analysed using five different sets each having 10%, 20% and 30% test samples.
Feature representation for microblog followee recommendation in classification framework

In recent years, microblog has experienced an explosive growth, which makes it difficult to find useful information, especially for new users. Followee recommendation can help users exploring new friends and build more social connections, in order to filter out the useless information, and promote the development of the social network. However, the large amount of online users and the diverse and dynamic social data possess great challenges to support the function. In this paper, by proposing a novel feature representation of microblog users, we design a novel friend recommendation framework, which transform the recommendation problem to a binary classification problem. This system focuses on analyzing and extracting multi-dimensional features of microblog users on followee recommendation, and modeling classifiers to distinct recommended users from non-recommended users. Experimental results show that the classification framework with the efficient feature representation can overcome data sparsity shortcomings in single collaborative filtering and get better recommendation effects.
Path selection using handover in mobile networks with cloud-enabled small cells

To overcome latency constrain of common mobile cloud computing, computing capabilities can be integrated into a base station in mobile networks. This exploitation of convergence of mobile networks and cloud computing enables to take advantage of proximity between a user equipment (UE) and its serving station to lower latency and to avoid backhaul overloading due to cloud computing services. This concept of cloud-enabled small cells is known as small cell cloud (SCC). In this paper, we propose algorithm for selection of path between the UE and the cell, which performs computing for this particular UE. As a path selection metrics we consider transmission delay and energy consumed for transmission of offloaded data. The path selection considering both metrics is formulated as Markov Decision Process. Comparing to a conventional delivery of data to the computing small cells, the proposed algorithm enables to reduce the delay by 9% and to increase users' satisfaction with experienced delay by 6.5%.
Comparing multimodal optimization and illumination

Illumination algorithms are a recent addition to the evolutionary computation toolbox that allows the generation of many diverse and high-performing solutions in a single run. Nevertheless, traditional multimodal optimization algorithms also search for diverse and high-performing solutions: could some multimodal optimization algorithms be better at illumination than illumination algorithms? In this study, we compare two illumination algorithms (Novelty Search with Local Competition (NSLC), MAP-Elites) with two multimodal optimization ones (Clearing, Restricted Tournament Selection) in a maze navigation task. The results show that Clearing can have comparable performance to MAP-Elites and NSLC.
A Novel HMM-Based TTS System using Both Continuous HMMS and Discrete HMMS

The conventional HMM-based speech synthesis system (HTS) has encountered two over-smoothing problems in both time domain and frequency domain. To resolve the two problems, the paper presents a new HTS framework using both continuous HMMs and discrete HMMs. By the replacement of spectral envelope represented by continuous Gaussian distribution with that represented by discrete codevector, the over-smoothing problem in frequency domain can be resolved, and by the replacement of the parameter generation algorithm using dynamic features with a new well-designed codevector selection algorithm, the over-smoothing problem in time domain can also be better resolved. Experimental results show that the using of both continuous HMMs and discrete HMMs significantly improves the voice quality of synthesized speech.
Variation in HbA1c prescription for patients with diabetes in French general practice: an observational study prior to the implementation of a P4P programme.

BACKGROUND Prior to a large diffusion of a pay-for-performance programme (P4P) in primary care in France, it seemed of particular interest to identify, the doctors not performing optimally who could be the main target of this programme. Based on the example of HbA1c prescription for patients with diabetes, this study examined the impact of general practitioner's (GPs) characteristics on the variation of a P4P indicator for diabetes care, i.e. the percentage of patients undergoing three or four HbA1c tests during one year. METHODS We used a large database from the national health insurance fund for salaried workers in Brittany to select a cohort of patients with diabetes who had been attended to by their doctors for 1 year. In all, 2545 GPs attending to 41,453 patients with diabetes were included. A two-level hierarchical logistic model was used to analyse the data. RESULTS Thirty-six per cent (SD = 22.3) of patients with diabetes underwent three or four HbA1c tests during the year (the target objective was 65% in a patient list). There was a large variability between GPs, even after adjusting for patient characteristics. Doctors who were female, young, working in a group practice, participating in quality-control groups, and who had a lower patient load prescribed the three or four recommended tests more often. DISCUSSION The results indicate a target group of doctors which require attention. There is still room to improve the quality of care for patients with diabetes in general practice, notably by encouraging doctors to train better and practice in groups.
Approches bayésiennes pour le débruitage des images dans le domaine des transformées multi-échelles parcimonieuses orientées et non orientées. (Bayesian approaches for image denoising in the oriented and non oriented parcimony multi-scale transformations)

Les images issues d'une chaine d'acquisition sont generalement degradees par le bruit du capteur. La tâche qui consiste a restaurer une image de bonne qualite a partir de sa version bruitee est communement appelee debruitage. Celui-ci a engendre une importante litterature en pre-traitement des images. Lors de ce travail de these, et apres avoir pose le probleme du debruitage en presence d'un bruit additif gaussien, nous avons effectue un etat de l'art methodique sur ce sujet. Les methodes presentees cherchent pour la plupart a reconstruire une solution qui presente une certaine regularite. En s'appuyant sur un cadre bayesien, la regularite de la solution, qui peut etre imposee de differentes manieres, a ete formellement mise en place en passant dans le domaine des transformees multi-echelle. Ainsi, afin d'etablir un modele d'a priori, nous avons mene une modelisation des statistiques marginales et jointes des coefficients d'images dans le domaine des transformees multi-echelles orientees (e.g. curvelets) et non-orientees (e.g. ondelettes). Ensuite, nous avons propose de nouveaux estimateurs bayesiens pour le debruitage. La mise en œuvre de ces estimateurs est effectuee en deux etapes, la premiere consistant a estimer les hyperparametres du modele de l'a priori en presence du bruit et la seconde a trouver une forme analytique pour l'estimateur bayesien correspondant. Dans un premier temps, nous avons mis en place des estimateurs bayesiens univaries en mettant a profit les statistiques marginales des coefficients des images dans des representations multi-echelle comme les ondelettes. Ces lois marginales ont ete analytiquement modelisees par le biais des distributions: ? -stable et les Formes K de Bessel. Dans un second temps, nous avons ameliore les performances de nos estimateurs univaries en introduisant l'information geometrique dans le voisinage des coefficients. Plus precisement, nous avons propose un cadre statistique bayesien multivarie permettant de prendre en compte les dependances inter- et intra-echelle des coefficients, en mettant a profit les statistiques jointes de ces derniers dans le domaine des curvelets et des ondelettes non decimees. Ensuite, nous avons mis en place l'estimateur bayesien multivarie correspondant base sur une extension multivariee de la distribution des Formes K de Bessel. Une large etude comparative a finalement ete menee afin de confronter nos algorithmes de debruitage a d'autres debruiteurs de l'etat de l'art.