Miracle-dz/UniFAR-FTUs huggingface dataset:
Use of High Resolution 3D Diffusion Tensor Imaging to Study Brain White Matter Development in Live Neonatal Rats

High resolution diffusion tensor imaging (DTI) can provide important information on brain development, yet it is challenging in live neonatal rats due to the small size of neonatal brain and motion-sensitive nature of DTI. Imaging in live neonatal rats has clear advantages over fixed brain scans, as longitudinal and functional studies would be feasible to understand neuro-developmental abnormalities. In this study, we developed imaging strategies that can be used to obtain high resolution 3D DTI images in live neonatal rats at postnatal day 5 (PND5) and PND14, using only 3 h of imaging acquisition time. An optimized 3D DTI pulse sequence and appropriate animal setup to minimize physiological motion artifacts are the keys to successful high resolution 3D DTI imaging. Thus, a 3D rapid acquisition relaxation enhancement DTI sequence with twin navigator echoes was implemented to accelerate imaging acquisition time and minimize motion artifacts. It has been suggested that neonatal mammals possess a unique ability to tolerate mild-to-moderate hypothermia and hypoxia without long term impact. Thus, we additionally utilized this ability to minimize motion artifacts in magnetic resonance images by carefully suppressing the respiratory rate to around 15/min for PND5 and 30/min for PND14 using mild-to-moderate hypothermia. These imaging strategies have been successfully implemented to study how the effect of cocaine exposure in dams might affect brain development in their rat pups. Image quality resulting from this in vivo DTI study was comparable to ex vivo scans. fractional anisotropy values were also similar between the live and fixed brain scans. The capability of acquiring high quality in vivo DTI imaging offers a valuable opportunity to study many neurological disorders in brain development in an authentic living environment.
Rates for the reactions pp → πφ and γφ

We study pp annihilation at rest into πφ and γφ. and ρ + ρ − for pp → πφ states is sizable, of order (0.90 to 2.6) × 10 −4 in the branching ratio, but smaller than experiment. For pp → γφ the rescattering contributions are negligible, but the γφ channel is well explained by a ρφ intermediate state combined with vector meson dominance.
Flows on surfaces of arbitrary topology

In this paper we introduce a method to simulate fluid flows on smooth surfaces of arbitrary topology: an effect never seen before. We achieve this by combining a two-dimensional stable fluid solver with an atlas of parametrizations of a Catmull-Clark surface. The contributions of this paper are: (i) an extension of the Stable Fluids solver to arbitrary curvilinear coordinates, (ii) an elegant method to handle cross-patch boundary conditions and (iii) a set of new external forces custom tailored for surface flows. Our techniques can also be generalized to handle other types of processes on surfaces modeled by partial differential equations, such as reaction-diffusion. Some of our simulations allow a user to interactively place densities and apply forces to the surface, then watch their effects in real-time. We have also computed higher resolution animations of surface flows off-line.
A 1.5-D Multi-Channel EEG Compression Algorithm Based on NLSPIHT

This letter proposes a novel 1.5-D algorithm for multi-channel electroencephalogram (EEG) compression. The proposed algorithm only needs to perform 1-D Discrete Wavelet Transform (DWT) rather than the 2-D version employed by previous works, and thus it results in lower computational complexity and power dissipation. In this algorithm, a new 2-D arranging method that exploits correlations between different sub-bands is developed to concentrate the energy, which causes more efficient compression using No List Set Partitioning in Hierarchical Trees (NLSPIHT) algorithm. Experimental results demonstrate that the proposed algorithm outperforms 2-D NLSPIHT algorithm under the same compression ratio (CR) and it is slightly inferior to 2-D SPIHT algorithm in the near-lossless compression regime, but it can provide a better fidelity with respect to higher CRs.
Semantic Expansion of Tweet Contents for Enhanced Event Detection in Twitter

This paper aims to enhance event detection methods in a micro-blogging platform, namely Twitter. The enhancement technique we propose is based on lexico-semantic expansion of tweet contents while applying document similarity and clustering algorithms. Considering the length limitations and idiosyncratic spelling in Twitter environment, it is possible to take advantage of word similarities and to enrich texts with similar words. The semantic expansion technique we implement is based on syntagmatic and paradigmatic relationships between words, extracted from their co-occurrence statistics. As our technique does not depend on an existing ontology or a lexicon database such as Word Net, it should be applicable for any language. The proposed technique is applied on a tweet set collected for three days from the users in Turkey. The results indicate earlier detection of events and improvements in accuracy.
A Neonatal Mouse Spinal Cord Injury Model for Assessing Post-Injury Adaptive Plasticity and Human Stem Cell Integration

Despite limited regeneration capacity, partial injuries to the adult mammalian spinal cord can elicit variable degrees of functional recovery, mediated at least in part by reorganization of neuronal circuitry. Underlying mechanisms are believed to include synaptic plasticity and collateral sprouting of spared axons. Because plasticity is higher in young animals, we developed a spinal cord compression (SCC) injury model in the neonatal mouse to gain insight into the potential for reorganization during early life. The model provides a platform for high-throughput assessment of functional synaptic connectivity that is also suitable for testing the functional integration of human stem and progenitor cell-derived neurons being considered for clinical cell replacement strategies. SCC was generated at T9–T11 and functional recovery was assessed using an integrated approach including video kinematics, histology, tract tracing, electrophysiology, and high-throughput optical recording of descending inputs to identified spinal neurons. Dramatic degeneration of axons and synaptic contacts was evident within 24 hours of SCC, and loss of neurons in the injured segment was evident for at least a month thereafter. Initial hindlimb paralysis was paralleled by a loss of descending inputs to lumbar motoneurons. Within 4 days of SCC and progressively thereafter, hindlimb motility began to be restored and descending inputs reappeared, but with examples of atypical synaptic connections indicating a reorganization of circuitry. One to two weeks after SCC, hindlimb motility approached sham control levels, and weight-bearing locomotion was virtually indistinguishable in SCC and sham control mice. Genetically labeled human fetal neural progenitor cells injected into the injured spinal cord survived for at least a month, integrated into the host tissue and began to differentiate morphologically. This integrative neonatal mouse model provides opportunities to explore early adaptive plasticity mechanisms underlying functional recovery as well as the capacity for human stem cell-derived neurons to integrate functionally into spinal circuits.
Mapping Simple Polygons: The Power of Telling Convex from Reflex

We consider the exploration of a simple polygon P by a robot that moves from vertex to vertex along edges of the visibility graph of P. The visibility graph has a vertex for every vertex of P and an edge between two vertices if they see each other—that is, if the line segment connecting them lies inside P entirely. While located at a vertex, the robot is capable of ordering the vertices it sees in counterclockwise order as they appear on the boundary, and for every two such vertices, it can distinguish whether the angle between them is convex (⩽ π) or reflex ( > π). Other than that, distant vertices are indistinguishable to the robot. We assume that an upper bound on the number of vertices is known. We obtain the general result that a robot exploring any locally oriented, arc-labeled graph G can always determine the base graph of G. Roughly speaking, this is the smallest graph that cannot be distinguished by a robot from G by its observations alone, no matter how it moves. Combining this result with various other techniques allows the ability to show that a robot exploring a polygon P with the preceding capabilities is always capable of reconstructing the visibility graph of P. We also show that multiple identical, indistinguishable, and deterministic robots of this kind can always solve the weak rendezvous problem in which they need to position themselves such that they mutually see each other—for instance, such that they form a clique in the visibility graph.
Improving Open Information Extraction Using Domain Knowledge

Open Information Extraction (OIE) aims to identify all the possible assertions within a sentence. Recent and thus the most efficient OIE-tools use the grammatical dependencies or the syntactic tree of the sentence to perform extraction. When they provide a wrong extraction it is mainly due to parsing errors. In this paper, we propose to handle these parsing errors before doing OIE itself. To achieve our goal we focus on multi-word expressions (MWE). They represent more than 45% of wrong extractions. We show how the MWE-problem can be handle in a given domain and how MWE-unbreakable property is a good filter for OIE.
The Power of Fair Information Practices - A Control Agency Approach

Most companies' new business practices are based on customer data. These practices have raised privacy concerns because of the associated risks. Privacy laws require companies to gain customer consent before using their information, which stands as the biggest roadblock to monetise this asset. Privacy literature suggests that reducing privacy concerns and building trust may increase individuals' intention to authorise the use of personal information. Fair information practices (FIPs) are potential means to achieve this goal. However, there is lack of empirical evidence on the mechanisms through which the FIPs affect privacy concerns and trust. This research argues that FIPs load individuals with control, which has been found to influence privacy concerns and trust level. We will use an experimental design methodology to conduct the study. The results are expected to have both theoretical and managerial implications.
BPEL'n'Aspects: Adapting Service Orchestration Logic

The need for flexibility in process-based applications, in particular during their execution, places the demand for enabling adaptability of processes. AOP is considered to be one of the approaches to flexibly switch on and off functionality on per-instance basis in applications during their execution; analogously, this paradigm can be applied in a BPEL environment to enable adaptation of running orchestrations. In the presented approach we strive towards reuse of as much concepts and technology already available in a Web service (WS) environment as possible. We combine standard BPEL, the publish/subscribe paradigm and WS-Policy so that WS operations play the role of aspects with respect to BPEL processes. We present the syntax for such aspects as an extension of the WS-Policy framework. We introduce the architecture of the supporting infrastructure and a prototypical implementation. The approach draws on the combined benefits of service orientation and the AOP paradigm to improve the state-of-the-art techniques for flexibility of service orchestrations in a non-intrusive manner.
In-vehicle technology functional requirements for older drivers

Older drivers represent the fastest growing segment of the road user population. Cognitive and physiological capabilities diminishes with ages. The design of future in-vehicle interfaces have to take into account older drivers' needs and capabilities. Older drivers have different capabilities which impact on their driving patterns and subsequently on road crash patterns. New in-vehicle technology could improve safety, comfort and maintain elderly people's mobility for longer. Existing research has focused on the ergonomic and Human Machine Interface (HMI) aspects of in-vehicle technology to assist the elderly. However there is a lack of comprehensive research on identifying the most relevant technology and associated functionalities that could improve older drivers' road safety. To identify future research priorities for older drivers, this paper presents: (i) a review of age related functional impairments, (ii) a brief description of some key characteristics of older driver crashes and (iii) a conceptualisation of the most relevant technology interventions based on traffic psychology theory and crash data.
Query Expansion Using Augmented Terms in an Extended Boolean Model

We propose a new query expansion method in the extended Boolean model that improves precision without degrading recall. For improving precision, our method promotes the ranks of documents having more query terms since users typically prefer such documents. The proposed method consists of the following three steps: (1) expanding the query by adding new terms related to each term of the query, (2) further expanding the query by adding augmented terms, which are conjunctions of the terms, (3) assigning a weight on each term so that augmented terms have higher weights than the other terms. We conduct extensive experiments to show the effectiveness of the proposed method. The experimental results show that the proposed method improves precision by up to 102% for the TREC-6 data compared with the existing query expansion method using a thesaurus proposed by Kwon et al. [Kwon et al. 1994].
LQR-trees: Feedback Motion Planning via Sums-of-Squares Verification

Advances in the direct computation of Lyapunov functions using convex optimization make it possible to efficiently evaluate regions of attraction for smooth non-linear systems. Here we present a feedback motion-planning algorithm which uses rigorously computed stability regions to build a sparse tree of LQR-stabilized trajectories. The region of attraction of this non-linear feedback policy “probabilistically covers” the entire controllable subset of state space, verifying that all initial conditions that are capable of reaching the goal will reach the goal. We numerically investigate the properties of this systematic non-linear feedback design algorithm on simple non-linear systems, prove the property of probabilistic coverage, and discuss extensions and implementation details of the basic algorithm.
Actual causes of death in the United States, 2000.

CONTEXT Modifiable behavioral risk factors are leading causes of mortality in the United States. Quantifying these will provide insight into the effects of recent trends and the implications of missed prevention opportunities. OBJECTIVES To identify and quantify the leading causes of mortality in the United States. DESIGN Comprehensive MEDLINE search of English-language articles that identified epidemiological, clinical, and laboratory studies linking risk behaviors and mortality. The search was initially restricted to articles published during or after 1990, but we later included relevant articles published in 1980 to December 31, 2002. Prevalence and relative risk were identified during the literature search. We used 2000 mortality data reported to the Centers for Disease Control and Prevention to identify the causes and number of deaths. The estimates of cause of death were computed by multiplying estimates of the cause-attributable fraction of preventable deaths with the total mortality data. MAIN OUTCOME MEASURES Actual causes of death. RESULTS The leading causes of death in 2000 were tobacco (435 000 deaths; 18.1% of total US deaths), poor diet and physical inactivity (365 000 deaths; 15.2%) [corrected], and alcohol consumption (85 000 deaths; 3.5%). Other actual causes of death were microbial agents (75 000), toxic agents (55 000), motor vehicle crashes (43 000), incidents involving firearms (29 000), sexual behaviors (20 000), and illicit use of drugs (17 000). CONCLUSIONS These analyses show that smoking remains the leading cause of mortality. However, poor diet and physical inactivity may soon overtake tobacco as the leading cause of death. These findings, along with escalating health care costs and aging population, argue persuasively that the need to establish a more preventive orientation in the US health care and public health systems has become more urgent.
Access and Barriers to Immunization in West Bengal, India: Quality Matters

While many studies attempted to evaluate performance of immunization programmes in developing countries by full coverage, there is a growing awareness about the limitations of such evaluation, irrespective of the overall quality of performance. Availability of human resources, equipment, supporting drugs, and training of personnel are considered to be crucial indicators of the quality of immunization programme. Also, maintenance of time schedule has been considered crucial in the context of the quality of immunization. In addition to overall coverage of vaccination, the coverage of immunization given at right time (month-specific) is to be considered with utmost importance. In this paper, District Level Household and Facility Survey-3 (DLHS-3) 2007-2008 data have been used in exploring the quality of immunization in terms of month-specific vaccine coverage and barriers to access inWest Bengal, India. In West Bengal, the month-specific coverage stands badly below 20% but the simple non-month-specific coverage is as high as 75%. Among the demand-side factors, birthplace of the child and religion of the household heads came out as significant predictors while, from the supply-side, availability of male health workers and equipment at the subcentres, were the important determinants for month-specific vaccine coverage. Hence, there should be a vigorous attempt to make more focused planning, keeping in mind the nature of the barriers, for improvement of the month-specific coverage in West Bengal.
Prognostic impact of average iodine density assessed by dual-energy spectral imaging for predicting lung tumor recurrence after stereotactic body radiotherapy

The purpose of this study was to investigate the prognostic significance of average iodine density as assessed by dual-energy computed tomography (DE-CT) for lung tumors treated with stereotactic body radiotherapy (SBRT). From March 2011 to August 2014, 93 medically inoperable patients with 74 primary lung cancers and 19 lung metastases underwent DE-CT prior to SBRT of a total dose of 45–60 Gy in 5–10 fractions. Of these 93 patients, nine patients had two lung tumors. Thus, 102 lung tumors were included in this study. DE-CT was performed for pretreatment evaluation. Regions of interest were set for the entire tumor, and average iodine density was obtained using a dedicated imaging software and evaluated with regard to local control. The median follow-up period was 23.4 months (range, 1.5–54.5 months). The median value of the average iodine density was 1.86 mg/cm3 (range, 0.40–9.27 mg/cm3). Two-year local control rates for the high and low average iodine density groups divided by the median value of the average iodine density were 96.9% and 75.7% (P = 0.006), respectively. Tumors with lower average iodine density showed a worse prognosis, possibly reflecting a hypoxic cell population in the tumor. The average iodine density exhibited a significant impact on local control. Our preliminary results indicate that iodine density evaluated using dual-energy spectral CT may be a useful, noninvasive and quantitative assessment of radio-resistance caused by presumably hypoxic cell populations in tumors.
HABITABLE CLIMATES: THE INFLUENCE OF OBLIQUITY

Extrasolar terrestrial planets with the potential to host life might have large obliquities or be subject to strong obliquity variations. We revisit the habitability of oblique planets with an energy balance climate model (EBM) allowing for dynamical transitions to ice-covered snowball states as a result of ice-albedo feedback. Despite the great simplicity of our EBM, it captures reasonably well the seasonal cycle of global energetic fluxes at Earth's surface. It also performs satisfactorily against a full-physics climate model of a highly oblique Earth-like planet, in an unusual regime of circulation dominated by heat transport from the poles to the equator. Climates on oblique terrestrial planets can violate global radiative balance through much of their seasonal cycle, which limits the usefulness of simple radiative equilibrium arguments. High obliquity planets have severe climates, with large amplitude seasonal variations, but they are not necessarily more prone to global snowball transitions than low obliquity planets. We find that terrestrial planets with massive CO2 atmospheres, typically expected in the outer regions of habitable zones, can also be subject to such dynamical snowball transitions. Some of the snowball climates investigated for CO2-rich atmospheres experience partial atmospheric collapse. Since long-term CO2 atmospheric build-up acts as a climatic thermostat for habitable planets, partial CO2 collapse could limit the habitability of such planets. A terrestrial planet's habitability may thus sensitively depend on its short-term climatic stability.
Robust control with linear matrix inequality approach for ship steering problem

This paper presents a yaw control problem for ship steering using a complex fourth order Nomoto model. A linear matrix iinequality (LMI) based H robust control to deal with poles on imaginary axis is applied to solve the yaw control problem for ship steering. The model uncertainty of ship steering problem is tackled using μ-synthesis control approach. The tracking ability and robust stability of H∞ control with LMI are compared with μ-synthesis approach to show the robust performance of the control systems in the presence of disturbance and measurement noise. Both of the designed controllers trade off the two performances.
Evoke - Developing Skills in Youth to Solve the World’s Most Complex Problems

This paper presents the findings of an impact evaluation designed to study an educational initiative called Evoke: Youth as Agents of Change in the Colombian Peace Process. Evoke is a project-based learning model that uses storytelling, game mechanics, and global social networks, to imbue young people with the skills they need to develop social innovations that address grand challenges (e.g., refugees, poverty, climate change) in their communities. Creating novel solutions to these complex and intractable problems requires curiosity, creativity, collaboration, aggregative thinking, empathy, plus a host of other 21st century and socioemotional skills. The development of each skill individually is important but these skills are deeply intertwined and even more relevant when combined in a project-based framework. These transferable skills enable young people to see, listen, question, imagine, think, care, act, and reflect, in a fundamentally different and potentially transformative way.