Miracle-dz/UniFAR-FTUs huggingface dataset:
An ITK framework for deterministic global optimization for medical image registration

Similarity metric optimization is an essential step in intensity-based rigid and nonrigid medical image registration. For clinical applications, such as image guidance of minimally invasive procedures, registration accuracy and efficiency are prime considerations. In addition, clinical utility is enhanced when registration is integrated into image analysis and visualization frameworks, such as the popular Insight Toolkit (ITK). ITK is an open source software environment increasingly used to aid the development, testing, and integration of new imaging algorithms. In this paper, we present a new ITK-based implementation of the DIRECT (Dividing Rectangles) deterministic global optimization algorithm for medical image registration. Previously, it has been shown that DIRECT improves the capture range and accuracy for rigid registration. Our ITK class also contains enhancements over the original DIRECT algorithm by improving stopping criteria, adaptively adjusting a locality parameter, and by incorporating Powell's method for local refinement. 3D-3D registration experiments with ground-truth brain volumes and clinical cardiac volumes show that combining DIRECT with Powell's method improves registration accuracy over Powell's method used alone, is less sensitive to initial misorientation errors, and, with the new stopping criteria, facilitates adequate exploration of the search space without expending expensive iterations on non-improving function evaluations. Finally, in this framework, a new parallel implementation for computing mutual information is presented, resulting in near-linear speedup with two processors.
Elucidating the Public Sentiment Fluctuations on Twitter

: Now a day’s numbers of people share their opinion on different aspect of life every day. So, twitter is used as microblogging platform for communicate with each other and also tracking and evaluating public emotions. It facilitates the details for making decisions in different domain. It is an attraction for organization and in academics field. Antecedently, research concentrated modelling and tracking public emotions. Here we proceed for the next step that is elucidating sentiment fluctuations. We have to take recent topic within the sentiment fluctuation periods which are strongly linked to the reason behind fluctuations. Grounded on this, we propose a Latent Dirichlet Allocation (LDA) base model. Foreground and Background LDA (FB-LDA), take the recent (foreground) topics and separate out long-lasting (background) topics. These recent topics give the possible elucidation of sentiment fluctuations. For better readability, we choose the illustrative tweets for recent topics and trained another productive model addressed Reason Candidate and Background LDA (RCB LDA). For ranking this model according to their popularity within the fluctuation period. This method can detect recent topics and rank reason candidates.
Assessment of Mesophotic Coral Ecosystem Connectivity for Proposed Expansion of a Marine Sanctuary in the Northwest Gulf of Mexico: Population Genetics

While there are several areas containing shallow coral habitats in the Gulf of Mexico (GOM), the availability of suitable reef habitat at mesophotic depths (∼30–150m) along the continental shelf margin suggests the potential for ecologically connected coral populations across hundreds of kilometers in the northwest (NW) GOM. The NW GOM includes a relatively high proportion of mesophotic habitats, including salt diapirs in Flower Garden Banks National Marine Sanctuary (FGBNMS), Bright Bank, and McGrail Bank, the latter two being Habitat Areas of Particular Concern (HAPCs). In response to a proposed expansion plan for the sanctuary boundaries to include additional mesophotic banks in the NW GOM, we investigated the genetic connectivity of the depth-generalist coral Montastraea cavernosa, a ubiquitous member of scleractinian communities throughout the Tropical Western Atlantic.Montastraea cavernosa populations in the NW GOM demonstrated strong connectivity with relatively high levels of gene flow and no significant genetic differentiation occurring over banks up to 120 km apart. Historical migration models based on genetic data predicted panmixia of M. cavernosa across the NW GOM. The comparisons between genetic and biophysical models (see Garavelli et al., 2018) highlight not only the importance of incorporating multiple assessments of connectivity into management schemes, but also the potentially stochastic nature of oceanographic patterns in the NW GOM and their effect on migration estimates among coral habitats. These trends indicate that M. cavernosa populations have remained well-connected in the NW GOM and that coral populations on each bank have likely been receiving larval recruitment through time. Thus, M. cavernosa populations should be managed as a combined unit within the NW GOM, which supports the proposal to expand the NMS boundaries to includemesophotic habitats beyondWest and East FGB.
Towards a Diagnosis of Textual Difficulties for Children with Dyslexia

Children's books are generally designed for children of a certain age group. For underage children or children with reading disorders, like dyslexia, there may be passages of the books that are difficult to understand. This can be due to words not known in the vocabulary of underage children, to words made of complex subparts (to pronounce, for example), or to the presence of anaphoras that have to be resolved by the children during the reading. In this paper, we present a study on diagnosing the difficulties appearing in French children's books. We are more particularly interested on the difficulties coming from pronouns that can disrupt the story comprehension for children with dyslexia and we focus on the subject pronouns " il " and " elle " (corresponding to the pronoun " it "). We automatically identify the pleonastic pronouns (e.g., in " it's raining ") and the pronominal anaphoras, as well as the referents of the pronominal anaphoras. We also detect difficult anaphoras that are more likely to lead to miscomprehension from the children: this is the first step to diagnose the textual difficulties of children's books. We evaluate our approach on several French children's books that were manually annotated by a speech therapist. Our first results show that we are able to detect half of the difficult anaphorical pronouns.
Traffic Differentiation on Internet of Things

The Internet of Things (IoT) is expected to constitute a significant portion of the Internet in the future, both in terms of traffic, and market share. For it to achieve its full potential, innovative solutions are necessary to address several open challenges. In this context we discuss Network Neutrality, which states that all traffic in the Internet must be treated equally, i.e., without traffic differentiation (TD). Unfair traffic management may result in a non-competitive market, affecting selectively the quality of experience of different IoT applications. This scenario might hinder innovation, threatening IoT success. Monitoring TD on the IoT is thus important for a more competitive market. In this paper, we first study the impact of TD on common IoT traffic patterns, such as periodic updates and real-time notifications. We present simulation results, and discuss which types of IoT applications are most affected by TD. We then discuss a solution for monitoring TD on IoT. The solution takes advantage of the IoT to address several open challenges of TD detection. For instance, the large amount of devices results in a prolific environment for making TD-related measurements. The solution can thus employ machine learning for continuously monitoring TD as the numerous IoT devices and applications communicate.
Ribosome–Lamella Complex Precursors in Acute Monocytic Leukemia: A Study of 6 Cases

The ribosome–lamella complex (RLC) is a cylindrical structure composed of annular lamella associated particles, regarded as ribosomes, around a central core, which is best known in hairy cell leukemia. RLC has been presumed to originate from aggregating rER and ribosomes. Incomplete and maturing RLC structures have been called RLC precursors (pre-RLC). The present paper investigates the various architectural aspects of pre-RLC and the ultrastructural characteristics of the blasts in 6 cases of acute monocytic leukemia (M5) in which these structures occur. Blasts bearing pre-RLC contained irregular nuclei with less heterochromatin and a prominent nucleolus, and many cytoplasmic organelles in an abundant cytoplasm. The findings indicate that pre-RLC might result from an asymmetrical differentiation of organelles in blasts associated with expression of CD117 and CD56 but default of CD14 in M5.
Action-Driven Visual Object Tracking With Deep Reinforcement Learning

In this paper, we propose an efficient visual tracker, which directly captures a bounding box containing the target object in a video by means of sequential actions learned using deep neural networks. The proposed deep neural network to control tracking actions is pretrained using various training video sequences and fine-tuned during actual tracking for online adaptation to a change of target and background. The pretraining is done by utilizing deep reinforcement learning (RL) as well as supervised learning. The use of RL enables even partially labeled data to be successfully utilized for semisupervised learning. Through the evaluation of the object tracking benchmark data set, the proposed tracker is validated to achieve a competitive performance at three times the speed of existing deep network-based trackers. The fast version of the proposed method, which operates in real time on graphics processing unit, outperforms the state-of-the-art real-time trackers with an accuracy improvement of more than 8%.
Potentially Curable Pancreatic Cancer: American Society of Clinical Oncology Clinical Practice Guideline.

PURPOSE To provide evidence-based recommendations to oncologists and others on potentially curative therapy for patients with localized pancreatic cancer. METHODS ASCO convened a panel of medical oncology, radiation oncology, surgical oncology, palliative care, and advocacy experts and conducted a systematic review of literature from January 2002 to June 2015. Outcomes included overall survival, disease-free survival, progression-free survival, and adverse events. RESULTS Nine randomized controlled trials met the systematic review criteria. RECOMMENDATIONS A multiphase computed tomography scan of the abdomen and pelvis or magnetic resonance imaging should be performed for all patients to assess the anatomic relationships of the primary tumor and for the presence of intra-abdominal metastases. Baseline performance status, comorbidity profile, and goals of care should be evaluated and established. Primary surgical resection is recommended for all patients who have no metastases, appropriate performance and comorbidity profiles, and no radiographic interface between primary tumor and mesenteric vasculature. Preoperative therapy is recommended for patients who meet specific characteristics. All patients with resected pancreatic cancer who did not receive preoperative therapy should be offered 6 months of adjuvant chemotherapy in the absence of contraindications. Adjuvant chemoradiation may be offered to patients who did not receive preoperative therapy with microscopically positive margins (R1) after resection and/or who had node-positive disease after completion of 4 to 6 months of systemic adjuvant chemotherapy. Patients should have a full assessment of symptoms, psychological status, and social supports and should receive palliative care early. Patients who have completed treatment and have no evidence of disease should be monitored. Additional information is available at www.asco. org/guidelines/PCPC and www.asco. org/guidelineswiki.
Molecular‐clock methods for estimating evolutionary rates and timescales

The molecular clock presents a means of estimating evolutionary rates and timescales using genetic data. These estimates can lead to important insights into evolutionary processes and mechanisms, as well as providing a framework for further biological analyses. To deal with rate variation among genes and among lineages, a diverse range of molecular‐clock methods have been developed. These methods have been implemented in various software packages and differ in their statistical properties, ability to handle different models of rate variation, capacity to incorporate various forms of calibrating information and tractability for analysing large data sets. Choosing a suitable molecular‐clock model can be a challenging exercise, but a number of model‐selection techniques are available. In this review, we describe the different forms of evolutionary rate heterogeneity and explain how they can be accommodated in molecular‐clock analyses. We provide an outline of the various clock methods and models that are available, including the strict clock, local clocks, discrete clocks and relaxed clocks. Techniques for calibration and clock‐model selection are also described, along with methods for handling multilocus data sets. We conclude our review with some comments about the future of molecular clocks.
A Very Low Resource Language Speech Corpus for Computational Language Documentation Experiments

Most speech and language technologies are trained with massive amounts of speech and text information. However, most of the world languages do not have such resources or stable orthography. Systems constructed under these almost zero resource conditions are not only promising for speech technology but also for computational language documentation. The goal of computational language documentation is to help field linguists to (semi-)automatically analyze and annotate audio recordings of endangered and unwritten languages. Example tasks are automatic phoneme discovery or lexicon discovery from the speech signal. This paper presents a speech corpus collected during a realistic language documentation process. It is made up of 5k speech utterances in Mboshi (Bantu C25) aligned to French text translations. Speech transcriptions are also made available: they correspond to a non-standard graphemic form close to the language phonology. We present how the data was collected, cleaned and processed and we illustrate its use through a zero-resource task: spoken term discovery. The dataset is made available to the community for reproducible computational language documentation experiments and their evaluation.
Presidential Management of International Crises: Structured Management Approaches and Crisis Learning

This multi-disciplinary project examines the style, structure, and performance of presidential management of international crises in order to determine both the impact of earlier crisis management experiences on subsequent crisis management behavior, and the role of a structured presidential approach to crisis management in either encouraging or discouraging such institutional learning. The aim of such an approach is twofold. First, it is to discover the level of learning and adaptation, from both positive and negative experiences, that takes place among presidents and their advisors during and after the handling of an international crisis, including how that learning may be maximized and directed toward more effective management efforts in the future. Second, it is to determine the role of a structured crisis management approach in shaping decision-making behavior, including how such an approach may affect the ability of presidents and their advisors to learn from prior experiences. The study integrates research from the international crisis, presidential management, and organizational learning literatures, utilizing George’s (1979) comparative case study method to examine several crises during the presidency of Dwight D. Eisenhower. The paper concludes with a presentation of findings and discusses the relevance of such findings on issues such as; recent and looming international crisis situations in North Korea, the Middle East, and elsewhere; the impact of a changing global landscape that includes such dangers as nuclear proliferation; and the prospects for successful crisis management efforts by the current and future American presidents in the 21 century. st For my parents. Columbia, my grandmother, my greatest teacher, 1909-2003 Sandra, my mom, my inspiration to cherish life, 1942-2003 You dedicated your lives to lifting me up beyond the clouds and from there you reached down and guided me to my destination . and my family Whether bonded through blood, or through soul, spirit, and loyalty my true family will always be a state of mind and a gift of love Your undying faith and hope have always overcome my doubts and fears and together we finally pushed my rock – this rock – over the mountain
M-CNER: A Corpus for Chinese Named Entity Recognition in Multi-Domains

In this paper, we present a new corpus for Chinese Named Entity Recognition (NER) from three domains : human-computer interaction, social media, and e-commerce. The annotation procedure is conducted in two rounds. In the first round, one sentence is annotated by more than one persons independently. In the second round, the experts discuss the sentences for which the annotators do not make agreements. Finally, we obtain a corpus which have five data sets in three domains. We further evaluate three popular models on the newly created data sets. The experimental results show that the system based on Bi-LSTM-CRF performs the best among the comparison systems on all the data sets. The corpus can be used for further studies in research community.
Direct power control of brushless DC motor drive

Due to significant increase in the demand of electric motors, design and manufacturing of high efficiency motors and related variable speed drives has been a major interest by many suppliers. Brushless DC (BLDC) motor has many advantages including high torque capability. In this paper, the possibilities of direct power control (DPC) of BLDC motors fed by voltage source inverter have been studied. BLDC motor has been driven with three control methods. It is shown that DPC method enjoys all advantages of direct torque control method including fast response and ease of implementation, without its lacks. Theoretical analysis and performance of the developed controller is confirmed via simulation under various conditions.
Pseudomonas sp . Strain 273 , an Aerobic a , v-Dichloroalkane-Degrading Bacterium

A gram-negative, aerobic bacterium was isolated from soil; this bacterium grew in 50% (vol/vol) suspensions of 1,10-dichlorodecane (1,10-DCD) as the sole source of carbon and energy. Phenotypic and small-subunit ribosomal RNA characterizations identified the organism, designated strain 273, as a member of the genus Pseudomonas. After induction with 1,10-DCD, Pseudomonas sp. strain 273 released stoichiometric amounts of chloride from C5 to C12 a,v-dichloroalkanes in the presence of oxygen. No dehalogenation occurred under anaerobic conditions. The best substrates for dehalogenation and growth were C9 to C12 chloroalkanes. The isolate also grew with nonhalogenated aliphatic compounds, and decane-grown cells dechlorinated 1,10-DCD without a lag phase. In addition, cells grown on decane dechlorinated 1,10-DCD in the presence of chloramphenicol, indicating that the 1,10-DCD-dechlorinating enzyme system was also induced by decane. Other known alkane-degrading Pseudomonas species did not grow with 1,10-DCD as a carbon source. Dechlorination of 1,10-DCD was demonstrated in cell extracts of Pseudomonas sp. strain 273. Cell-free activity was strictly oxygen dependent, and NADH stimulated dechlorination, whereas EDTA had an inhibitory effect.
Collaborative representation based classification for vehicle recognition in acoustic sensor networks

Sparse Representation Classification has led to state-of-the-art results in pattern classification tasks. However, as Sparse Representation Classification has significantly high lower complexity, and vehicle recognition is a typical small-samplesize problem and trained dictionary is under-complete, all these give rise to big representation errors and unstable recognition results. In this paper, we develop a new Collaborative Representation based vehicle recognition framework, using acoustic sensor networks to reduce the time complexity in the training and testing phases, and to improve the classification accuracy in complex scenes. In the recognition, the acoustic signals of vehicles are extracted from the acoustic information to get linearly separable samples by Fast Fourier Transform, and then we encode a testing sample through linear combination of all the training samples with regularized least square and classify the testing sample into the class with the minimum representation error. As demonstrated by experimental results, the proposed method has the following two unique and important characteristics: (1) it achieves a superior performance under the circumstance of complex data sets (2) It also shows highly competitive recognition accuracy while has low computational complexity and memory requirements, compared to k-Nearest Neighbor, Support Vector Machines and Sparse Representation Classification algorithm.
Crowd Counting by Adaptively Fusing Predictions from an Image Pyramid

Because of the powerful learning capability of deep neural networks, counting performance via density map estimation has improved significantly during the past several years. However, it is still very challenging due to severe occlusion, large scale variations, and perspective distortion. Scale variations (from image to image) coupled with perspective distortion (within one image) result in huge scale changes of the object size. Earlier methods based on convolutional neural networks (CNN) typically did not handle this scale variation explicitly, until Hydra-CNN and MCNN. MCNN uses three columns, each with different filter sizes, to extract features at different scales. In this paper, in contrast to using filters of different sizes, we utilize an image pyramid to deal with scale variations. It is more effective and efficient to resize the input fed into the network, as compared to using larger filter sizes. Secondly, we adaptively fuse the predictions from different scales (using adaptively changing per-pixel weights), which makes our method adapt to scale changes within an image. The adaptive fusing is achieved by generating an across-scale attention map, which softly selects a suitable scale for each pixel, followed by a 1x1 convolution. Extensive experiments on three popular datasets show very compelling results.
Conceptualizing Clusters as Dynamic and Path-Dependent Pools of Skills

Kasabov E. and Sundaram U. Conceptualizing clusters as dynamic and path-dependent pools of skills, Regional Studies. This paper conceptualizes clusters as pools of dynamic, path-dependent skills that sustain economic resilience, transformation and development. It draws on extensive archival, historical material and empirical findings from the city of Coventry, UK, to demonstrate the long-term nature and processes of skills accumulation, growth and development. This novel approach to theorizing and understanding clusters provides a critique of current cluster conceptualizations, and offers an alternative imaginary to understanding agglomerations of economic activity. The paper also highlights problems and failures in cluster development and their sustenance through a skills pool. It develops implications for the sustained nourishment and replenishment of skills and validates skills as a critical unit of observation and analysis of clusters. The paper provides a longitudinal, socio-historical account of clusters that morph and evolve over time through the reconfiguration of path-dependent skills in support of the conceptualization.