Miracle-dz/UniFAR-FTUs huggingface dataset:
Subtotal laryngectomy with myomucosal shunt

Abstract This is a modified subtotal laryngectomy. On the tumour-free side of the larynx, some posterior structures, with their neurovascular supply are preserved. The endolaryngeal mucosa is tubed in continuity with the trachea below and projects into the pharynx above. Thus a myomucosal shunt is formed. Air is directed into it by finger occlusion of the tracheal stoma. The voice production is highly satisfactory. Aspiration is prevented by constriction of the thyroarytenoid muscle which provides a valved upper end of the tube. The possibility of leaving tumour in the laryngeal remnant is eliminated by careful selection of patients, and re-confirmation of tumour extent intra-operatively and a frozen section. Eleven such operations have been performed since October 1983 for squamous cell carcinoma, some previously irradiated. None of the patients had local recurrence. Only one had an aspiration problem which later resolved. All acquired a satisfactory voice
Universal scaling of fluid permeability for sphere packings.

Results from the numerical simulation of Stokes flow through random packings of nonoverlapping or overlapping sphers and a [ital scaling] [ital ansatz] are used to obtain universal curves for the fluid permeability. The scaling ansatz is motivated by previous analysis of rigorous bounds on the permeability. Excellent agreement was found for a variety of model microstructures of porous media in the low and high porosity regimes. Experimentally obtained permeabilities of several sandstones were found to agree well with our universal curve.
Zoledronic acid inhibits macrophage/microglia-assisted breast cancer cell invasion

The bisphosphonate zoledronic acid (ZA) significantly reduces complications of bone metastasis by inhibiting resident macrophages, the osteoclasts. Recent clinical trials indicate additional anti-metastatic effects of ZA outside the bone. However, which step of metastasis is influenced and whether this is due to direct toxicity on cancer cells or inhibition of the tumor promoting microenvironment, is unknown. In particular, tumor-associated and resident macrophages support each step of organ metastasis and could be a crucial target of ZA. Thus, we comparatively investigate the ZA effects on: i) different types of macrophages, ii) on breast cancer cells but also iii) on macrophage-induced invasion. We demonstrate that ZA concentrations reflecting the plasma level affected viability of human macrophages, murine bone marrow-derived macrophages as well as their resident brain equivalents, the microglia, while it did not influence the tested cancer cells. However, the effects on the macrophages subsequently reduced the macrophage/microglia-induced invasiveness of the cancer cells. In line with this, manipulation of microglia by ZA in organotypic brain slice cocultures reduced the tissue invasion by carcinoma cells. The characterization of human macrophages after ZA treatment revealed a phenotype/response shift, in particular after external stimulation. In conclusion, we show that therapeutic concentrations of ZA affect all types of macrophages but not the cancer cells. Thus, anti-metastatic effects of ZA are predominantly caused by modulating the microenvironment. Most importantly, our findings demonstrate that ZA reduced microglia-assisted invasion of cancer cells to the brain tissue, indicating a potential therapeutic role in the prevention of cerebral metastasis.
Efficient Estimation of Generalization Error and Bias-Variance Components of Ensembles

For many applications, an ensemble of base classifiers is an effective solution. The tuning of its parameters(number of classes, amount of data on which each classifier is to be trained on, etc.) requires G, the generalization error of a given ensemble. The efficient estimation of G is the focus of this paper. The key idea is to approximate the variance of the class scores/probabilities of the base classifiers over the randomness imposed by the training subset by normal/beta distribution at each point x in the input feature space. We estimate the parameters of the distribution using a small set of randomly chosen base classifiers and use those parameters to give efficient estimation schemes for G. We give empirical evidence for the quality of the various estimators. We also demonstrate their usefulness in making design choices such as the number of classifiers in the ensemble and the size of a subset of data used for training that is needed to achieve a certain value of generalization error. Our approach also has great potential for designing distributed ensemble classifiers.
Improving and simplifying the generation of reference trajectories by usage of road-aligned coordinate systems

Short term motion planners for automated vehicles typically require a reference path as input to optimize the ride quality between distinct vehicle states. This paper presents a novel approach to simplify the generation of such reference paths. It is based on the idea of converting the driving route and all relevant objects from the current vehicle's environment into a road-aligned coordinate system eliminating road's curvature. Based on this, a suitable path using geometric primitives can be constructed, which is then converted back into the original coordinate system. When considered during generation, the resulting reference path guarantees to respect vehicle kinodynamics and is checked against collisions.
Retrospective evaluation of crestal bone changes around implants with reduced abutment diameter placed non-submerged and at subcrestal positions: the effect of bone grafting at implant placement.

BACKGROUND There is limited information regarding the effect of grafting of the osteotomy after subcrestal implant placement. The primary aim of this study is to retrospectively evaluate the effect of bone grafting of the defect between the bone crest and the coronal aspect of implants with reduced abutment diameter placed non-submerged and at subcrestal positions. METHODS Records of 50 consecutive patients treated with subcrestally placed dental implants grafted with a xenograft (Group A) and 50 consecutive patients with subcrestally placed dental implants without any grafting material (Group B) were reviewed. For each implant, the radiographs after placement were compared to images from the last follow-up visit and evaluated regarding the following: 1) degree of subcrestal positioning of the implant, 2) changes of marginal hard-tissue height over time, and 3) whether marginal hard-tissue could be detected on the implant platform at the follow-up visit. RESULTS The mean marginal loss of hard tissues was 0.11 ± 0.30 mm for Group A and 0.08 ± 0.22 mm for Group B. Sixty-nine percent of the implants in Group A and 77% of the implants in Group B demonstrated hard tissue on the implant platform. There were no statistically significant differences between the groups regarding marginal peri-implant hard-tissue loss. CONCLUSION The present study fails to demonstrate that grafting of the remaining osseous wound defect between the bone crest and the coronal aspect of the implant has a positive effect on marginal peri-implant hard-tissue changes.
Irinotecan-encapsulated double-reverse thermosensitive nanocarrier system for rectal administration

Abstract Intravenously administered for the treatment of rectum cancer, irinotecan produces severe side effects due to very high plasma concentrations. A novel irinotecan-encapsulated double reverse thermosensitive nanocarrier system (DRTN) for rectal administration was developed as an alternative. The DRTN was fabricated by dispersing the thermosensitive irinotecan-encapsulated solid lipid nanoparticles (SLN) in the thermosensitive poloxamer solution. Its gel properties, pharmacokinetics, morphology, anticancer activity and immunohistopathology were assessed after its rectal administration to rats and tumor-bearing mice. In the DRTN, the solid form of the SLN and the liquid form of the poloxamer solution persisted at 25 °C; the former melted to liquid, and the latter altered to gel at 36.5 °C. The DRTN was easily administered to the anus, gelling rapidly and strongly after rectal administration. Compared to the conventional hydrogel and intravenously administered solution, it retarded dissolution and initial plasma concentration. The DRTN gave sustained release and nearly constant plasma concentrations of irinotecan at 1–3 h in rats, resulting in improved anticancer activity. It induced no damage to the rat rectum and no body weight loss in tumor-bearing mice. Thus, this irinotecan-encapsulated DRTN associated with a reduced burst effect, lack of toxicity and excellent antitumor efficacy would be strongly recommended as a rectal pharmaceutical product alternative to commercial intravenous injection in the treatment of rectum and colon cancer.
Artificial Intelligent Advisory System for PC Purchasing

The relevance of this paper in today’s ICT driven society is to ensure that PC buyers are assisted on decision making in PC purchasing. Development of an artificial intelligence advisory system (AIAS) that uses artificial neural network (ANN) as its knowledge base appears to be a promising method for offering expert advise to intended PC users such as schools, organizations, banks and the amateur. In this paper we confronted the problem of making right choices on PC purchase, which is an important decision - making issue that must be addressed based on certain criteria including user preferences, set of needs, values, system configuration, nature of work, etc. The paper therefore introduces a novel approach which could assist a buyer in decision making on the right choice of PC to go for. The paper presented the development and evaluation of highly personalized advisory system to complement the cognitive and computational capacities of humans. AIAS will leverage the experience and contextual knowledge of seasoned computer engineer to facilitate the decision making process on PC purchase. The system will assist users in a collaborative manner by gathering and presenting information, and by recommending actions. The AIAS construction and training using expert system architecture design is presented. Rigorous training with data collected from available PC models in Nigeria showed that the system is capable of successfully offering matching advice with 90 percent accuracy.
How demanding is the revealed preference approach to demand

A well known problem with revealed preference methods is that when data are found to satisfy their restrictions it is hard to know whether this should be viewed as a triumph for economic theory, or a warning that these conditions are so undemanding that almost anything goes. This paper allows researchers to make this distinction. Our approach builds on theoretical support in the form of an axiomatic cardinal characterisation of a measure of predictive success due to Selten(1991). We illustrate the idea using a large, nationally representative panel survey of Spanish consumers with broad commodity coverage. The results show that this approach to revealed preference methods can lead us radically to reassess our view of the empirical performance of economic theory.
Putting Learning First in Writing to Learn.

Writing to learn has become a fashionable idea in progressive language education circles. Following the work in Great Britain of James Britton (1970), Nancy Martin (1976), and their associates, American educators have adopted the term and recognized the value of such types of writing as the content journal (Fulwiler and Young 1982) to promote more lasting learning across the curriculum. Although many educators have embraced the phrase, our experience tells us that little change in using writing as a means of learning content material has actually taken place in American classrooms. Journals have proliferated, and there does seem to have been some increase in the amount of personal or expressive writing that students are asked to do (Brown 1983). This increase in the amount of writing students engage in is highly desirable, but personal writing alone does not guarantee the learning of content. The potential and actual connection of writing and learning has been stymied by a failure to reconceptualize learning itself. The primary theory of learning in many schools is still based on the empty-vessel metaphor of students as receivers of information and on an atomistic division of things to be learned into discrete bits of knowledge or skill. Neither permits an adequate understanding and implementation of writing as a means of learning. Writing itself is still too often viewed as a discrete collection of skills. The goal of writing instruction, therefore, is seen as the mastery of separable skills (i.e., beginning a paragraph with a topic sentence or ending each sentence with an appropriate punctuation mark). Similarly, the goal of history instruction, for example, is seen as the mastery of appropriate dates, names, and other facts. Changing writing instruction to a more organic, process-based approach is a step in the right direction, but such a change does not automatically suggest how writing can function as a means of learning. Our goal in this paper is to critique what learning seems to mean in the typical American school setting, and to suggest a more appropriate construct of learning. This construct will enable us to see more clearly how writing fits as a means of enhancing learning.
A new model-discriminant training algorithm for hybrid NN-HMM systems

This paper describes a hybrid system for continuous speech recognition consisting of a neural network (NN) and a hidden Markov model (HMM). The system is based on a multilayer perceptron, which approximates the a-posteriori probability of a sequence of states, derived from semi-continuous hidden Markov models. The classification is based on a total score for each hybrid model, attained from a Viterbi search on the state probabilities. Due to the unintended discrimination between the states in each model, a new training algorithm for the hybrid neural networks is presented. The utilized error function approximates the misclassification rate of the hybrid system. The discriminance between the correct and the incorrect models is optimized during the training by the 'Generalized Probabilistic Descent Algorithm', resulting in a minimum classification error. No explicit target values for the neural net output nodes are used, as in the usual backpropagation algorithm with a quadratic error function. In basic experiments up to 56% recognition rate were achieved on a vowel classification task and up to 69% on a consonant cluster classification task. <<ETX>>
To Lose is to Win: The Effects of Student Evaluations in a Multicultural Education Class on a Japanese Faculty with a Non-native English Accent

This study investigated how a a Japanese Faculty with a Non-native English Accent who teaches a multicultural education class at a predominantly white university struggled with student evaluation for her tenure and promotion using AsianCrit as a theoretical framework. The analysis of student evaluation scores and comments and her personal narratives revealed that she was constantly framed in the model minority Asian stereotypes and her accented English stirred nativistic attitudes among the students, enforcing anti-American sentiments among some students. The professor realized that without providing her students with an opportunity to achieve a sense of empowerment in her class, she would never be successful, and used a power-shift strategy to navigate the students’ resistance. The study concludes with suggestions on how to dismantle model minority stereotypes.
An evaluation of VTS and IMM for speaker verification in noise

The performance of speaker veriﬁcation (SV) systems degrades rapidly in noise rendering them unsuitable for security-critical applications in mobile phones, where false acceptance rates (FAR) of ∼ 10 − 4 are required. However, less demanding applications for which equal error rates (EER) comparable to word error rates (WER) of speech recognizers are acceptable could beneﬁt from the SV technology. In this paper we evaluate two feature-based noise compensation algorithms in the context of SV: vector Taylor series (VTS) combined with statistical linear approximation (SLA), and Kalman ﬁlter-based interacting multiple models (IMM). Tests with theYOHO database and the NTT-AT ambient noises show that EERs as low as 5%–10% in medium to high noise conditions can be achieved for a text-independent SV system.
Objective Monitoring of Physical Activity Using Motion Sensors and Heart Rate

Although neither motion sensors nor heart rate are perfect markers of physical activity, they certainly eliminate subjectivity of obtaining physical activity information. The objective method of choice depends on how the measurement will be used. For example, if walking behavior is the desired outcome, then a pedometer may be sufficient. If patterns and intensity of activity over longer periods of times such as a week or longer are needed, then an accelerometer with large memory capacity should be selected. In the future, efforts should be directed towards developing an objective motion sensor as inexpensive as a pedometer but with the data acquisition capabilities of the CSA or Tritrac accelerometer. Providing simultaneous heart rate with motion is also recommended to further verify that elevated heart rate does in fact represent a physical activity response. As the cost of the electronic components continues to decrease, these activity monitor configurations may become possible.
Macro-scale phenomena of arterial coupled cells: a massively parallel simulation

Impaired mass transfer characteristics of blood-borne vasoactive species such as adenosine triphosphate in regions such as an arterial bifurcation have been hypothesized as a prospective mechanism in the aetiology of atherosclerotic lesions. Arterial endothelial cells (ECs) and smooth muscle cells (SMCs) respond differentially to altered local haemodynamics and produce coordinated macro-scale responses via intercellular communication. Using a computationally designed arterial segment comprising large populations of mathematically modelled coupled ECs and SMCs, we investigate their response to spatial gradients of blood-borne agonist concentrations and the effect of micro-scale-driven perturbation on the macro-scale. Altering homocellular (between same cell type) and heterocellular (between different cell types) intercellular coupling, we simulated four cases of normal and pathological arterial segments experiencing an identical gradient in the concentration of the agonist. Results show that the heterocellular calcium (Ca2+) coupling between ECs and SMCs is important in eliciting a rapid response when the vessel segment is stimulated by the agonist gradient. In the absence of heterocellular coupling, homocellular Ca2+ coupling between SMCs is necessary for propagation of Ca2+ waves from downstream to upstream cells axially. Desynchronized intracellular Ca2+ oscillations in coupled SMCs are mandatory for this propagation. Upon decoupling the heterocellular membrane potential, the arterial segment looses the inhibitory effect of ECs on the Ca2+ dynamics of the underlying SMCs. The full system comprises hundreds of thousands of coupled nonlinear ordinary differential equations simulated on the massively parallel Blue Gene architecture. The use of massively parallel computational architectures shows the capability of this approach to address macro-scale phenomena driven by elementary micro-scale components of the system.
Data Mining Meets Collocations Discovery

In this paper we discuss the problem of discovering interesting word sequences in the light of two traditions: sequential pattern mining (from data mining) and collocations discovery (from computational linguistics). Smadja (1993) defines a collocation as “a recurrent combination of words that cooccur more often than chance and that correspond to arbitrary word usages. ” The notion of arbitrariness underlines the fact that if one word of a collocation is substituted by a synonym, the resulting phrase may become peculiar or even incorrect. For instance, “strong tea” cannot be replaced with “powerful tea”. Acquisition of collocations, a.k. a multi-word units, are crucial for many fields, like lexicography, machine translation, foreign language learning, and information retrieval. We attempt to describe the collocations discovery problem as a general problem of discovering interesting sequences in text. Moreover, we give a survey of common approaches from both collocations discovery and data mining and propose new avenues for fruitful combination of these approaches.
Compression for Smooth Shape Analysis

Most 3D shape analysis methods use triangular meshes to discretize both the shape and functions on it as piecewise linear functions. With this representation, shape analysis requires fine meshes to represent smooth shapes and geometric operators like normals, curvatures, or Laplace-Beltrami eigenfunctions at large computational and memory costs. We avoid this bottleneck with a compression technique that represents a smooth shape as subdivision surfaces and exploits the subdivision scheme to parametrize smooth functions on that shape with a few control parameters. This compression does not affect the accuracy of the Laplace-Beltrami operator and its eigenfunctions and allow us to compute shape descriptors and shape matchings at an accuracy comparable to triangular meshes but a fraction of the computational cost. Our framework can also compress surfaces represented by point clouds to do shape analysis of 3D scanning data.
Towards an integrated model of speech and gesture production for multi-modal robot behavior

The generation of communicative, speech-accompanying robot gesture is still largely unexplored. We present an approach to enable the humanoid robot ASIMO to flexibly produce speech and co-verbal gestures at run-time, while not being limited to a pre-defined repertoire of motor actions. Since much research has already been dedicated to this challenge within the domain of virtual conversational agents, we build upon the experience gained from the development of a speech and gesture production model used for the virtual human Max. We propose a robot control architecture building upon the Articulated Communicator Engine (ACE) that was developed to allow virtual agents to flexibly realize planned multi-modal behavior representations on the spot. Our approach tightly couples ACE with ASIMO's perceptuo-motor system, combining conceptual representation and planning with motor control primitives for speech and arm movements of a physical robot body. First results of both gesture production and speech synthesis using ACE and the MARY text-to-speech system are presented and discussed.