Miracle-dz/UniFAR-FTUs huggingface dataset:
Vestibular rehabilitation – For whom and how? A systematic review

Vestibular rehabilitation was first developed as treatment for vestibular disorders but has also been used as treatment for other causes of dizziness. Many outpatients with dizziness are seen in primary care but vestibular rehabilitation is probably not well known among physiotherapist outside tertiary care. The aim of this systematic literature review was to describe and elucidate vestibular rehabilitation as a feasible treatment for various causes of dizziness. Using the keyword vestibular rehabilitation, searches were performed in PubMed, AMED, CINAHL and PeDro. Seventy-one studies were included in the review. Strong scientific evidence for vestibular rehabilitation was found for vestibular hypofunction, for multisensory dizziness and for Ménière's disease. Moderately strong scientific evidence was found for vestibular rehabilitation after vestibular surgery. For neurological causes of dizziness, benign positional paroxysmal vertigo (BPPV), phobic postural vertigo (PPV), dizziness concomitant to whiplash-associated disorders (WAD) and migraine-associated dizziness there seems to be insufficient scientific evidence. Vestibular rehabilitation can be recommended as treatment for vestibular hypofunction, for multisensory dizziness, for Ménière's disease and after vestibular surgery. The results of research on vestibular rehabilitation for neurological causes of dizziness, BPPV, PPV, dizziness concomitant to WAD and migraine-associated dizziness are promising enough for recommending vestibular rehabilitation for these diagnoses also, even though more studies are needed.
Facebook addiction among health university students in Bengaluru

Introduction: Facebook was founded in 2004 and is one of the most popular social networking sites. Although Facebook is used to connect and stay in touch with friends and also to maintain relationships, there may be situations where an individual uses the site way too much. Relatively little research has been carried out to explain effects of usage to Facebook in terms of addiction in India. Objectives: The aim was to find out the burden of Facebook addiction among college students of health university. Subjects and Methods: This exploratory qualitative study was conducted in Bengaluru City covering six colleges attached to a health university using convenient sampling from May 2013 to July 2013. About 400 students had enrolled for the study. Results : It was observed that the burden of addiction was 7.25% and high risk was 24.75% in the study subjects. 61 (31.12%) males and 38 (18.62%) females were categorized as high-risk behavior subjects. 12 (6.12%) males and 17 (8.33%) females were categorized as having Facebook addiction. 64% of study subjects used Facebook daily. 32% of the subjects felt strain in the eye, 11% watering of the eye, 20% felt frustrated and 17% were anxious when they did not have access to Facebook. Conclusion: The burden of Facebook addiction and high-risk behavior was observed in one-third of the subjects.
Stationary-Process Approximations for the Nonstationary Erlang Loss Model

In this paper we consider the Mt/G/s/0 model, which has s servers in parallel, no extra waiting space, and i.i. d. service times that are independent of a nonhomogeneous Poisson arrival process. Arrivals finding all servers busy are blocked lost. We consider approximations for the average blocking probabilities over subintervals e.g., an hour when the expected service time is five minutes obtained by replacing the nonstationary arrival process over that subinterval by a stationary arrival process. The stationary-Poisson approximation, using a Poisson M process with the average rate, tends to significantly underestimate the blocking probability. We obtain much better approximations by using a non-Poisson stationary G arrival process with higher stochastic variability to capture the effect of the time-varying deterministic arrival rate. In particular, we propose a specific approximation based on the heavy-traffic peakedness formula, which is easy to apply with either known arrival-rate functions or data from system measurements. We compare these approximations to exact numerical results for the Mt/M/s/0 model with linear arrival rate.
Polling Models With and Without Switchover Times

We consider two different single-server cyclic polling models: (i) a model with zero switchover times, and (ii) a model with nonzero switchover times, in which the server keeps cycling when the system is empty. For both models we relate the steady-state queue length distribution at a queue to the queue length distributions at server visit beginning and visit completion instants at that queue; as a by-product we obtain a short proof of the Fuhrmann-Cooper decomposition. For the large class of polling systems that allow a multitype branching process interpretation, we expose a strong relation between the queue length, as well as waiting-time, distributions in the two models. The results enable a very efficient numerical computation of the waiting-time moments under different switchover time scenarios.
Improved Fashion Buying with Bayesian Updates

We focus on the problem of buying fashion goods for the “big book” of a catalogue merchandiser. This company also owns outlet stores and thus has the opportunity, as the season evolves, to divert inventory originally purchased for the big book to the outlet store. The obvious questions are: (1) how much to order originally, and (2) how much to divert to the outlet store as actual demand is observed. We develop a model of demand for an individual item. The model is motivated by data from the women's designer fashion department and uses both historical data and buyer judgement. We build a stochastic dynamic programming (DP) model of the fashion buying problem that incorporates the model of demand. The DP model is used to derive the structure of the optimal inventory control policy. We then develop an updated Newsboy heuristic that is intuitively appealing and easily implemented. When this heuristic is compared to the optimal solution for a wide variety of scenarios, we observe that it performs very well. Si.
Scheduling Semiconductor Burn-In Operations to Minimize Total Flowtime

This paper addresses a problem of batch scheduling which arises in the burn-in stage of semiconductor manufacturing. Burn-in ovens are modeled as batch-processing machines which can handle up to B jobs simultaneously. The processing time of a batch is equal to the longest processing time among the jobs in the batch. The scheduling problem involves assigning jobs to batches and determining the batch sequence so as to minimize the total flowtime. In practice, there is a small number m of distinct job types. Previously, the only solution techniques known for the single-machine version of this problem were an O(m23Bm+1) pseudopolynomial algorithm, and a branch-and-bound procedure. We present an algorithm with a running time of O(m23m), which is independent of B, the maximum batch size. We also present a polynomial heuristic for the general problem (when m is not fixed), which is a two-approximation algorithm. For any problem instance, this heuristic provides a solution at least as good as that given by previo.
Long-term neuropathy after oxaliplatin treatment: challenging the dictum of reversibility.

OBJECTIVES Oxaliplatin-induced neuropathy is a significant and dose-limiting toxicity that adversely affects quality of life. However, the long-term neurological sequelae have not been adequately described. The present study aimed to describe the natural history of oxaliplatin-induced neuropathy, using subjective and objective assessments. METHODS From a population of 108 oxaliplatin-treated patients referred for neurological assessment in 2002-2008, 52.2% of the surviving patient cohort (n = 24) was available for follow-up at a median of 25 months post-oxaliplatin. Patients underwent a protocol that incorporated clinical assessment scales, patient questionnaires, standard electrodiagnostic assessments, and novel nerve excitability studies to precisely assess nerve function. RESULTS At follow-up, 79.2% of patients reported residual neuropathic symptoms, with distal loss of pin-prick sensibility in 58.3% of patients and loss of vibration sensibility in 83.3% of patients. Symptom severity scores were significantly correlated with cumulative dose. There was no recovery of sensory action potential amplitudes in upper and lower limbs, consistent with persistent axonal sensory neuropathy. Sensory excitability parameters had not returned to baseline levels, suggesting persisting abnormalities in nerve function. The extent of excitability abnormalities during treatment was significantly correlated with clinical outcomes at follow-up. CONCLUSIONS These findings establish the persistence of subjective and objective deficits in oxaliplatin-treated patients post-oxaliplatin, suggesting that sensory neuropathy is a long-term outcome, thereby challenging the literature on the reversibility of oxaliplatin-induced neuropathy.
The health-promoting prison: can it improve oral health? An evaluation of an Oral Health Improvement Project for high-security prisoners

The aim of the evaluation was to examine the impact of an Oral Health Improvement Project on the oral health-related knowledge, attitudes and behaviours of prisoners in this high-security gaol. Prisoners in a high-security prison that had adopted a health-promoting prison agenda were invited to take part. Prisoners in locations where the Project had been active acted as the intervention group and those in other settings where there was no formal involvement with the Project acted as controls. All consenting prisoners were asked to complete questionnaires to assess their Project awareness, oral health-related knowledge, attitudes and behaviours. The data were subjected to Chi-squared tests, t-tests, correlation analysis and ANCOVA. Fifty-nine intervention and 48 control group prisoners completed questionnaires. Intervention group prisoners were more Project-aware (F[1,97] = 6.98, p = 0.01) and more knowledgeable about oral health (F[1,97] = 12.02, p = 0.001) when controlling for duration of current imprisonment. No significant associations between intervention group status and oral health-related attitudes or behaviours were found. Increased reported smoking and poorer dietary habits were significantly explained by duration of current imprisonment. This evaluation highlights the difficulties in changing health-related attitudes and behaviours in the prison setting. It also demonstrates the need for effective deployment of the health-promoting prison approach to improve the oral health of prisoners.
Learning Graph-based POI Embedding for Location-based Recommendation

With the rapid prevalence of smart mobile devices and the dramatic proliferation of location-based social networks (LBSNs), location-based recommendation has become an important means to help people discover attractive and interesting points of interest (POIs). However, the extreme sparsity of user-POI matrix and cold-start issue create severe challenges, causing CF-based methods to degrade significantly in their recommendation performance. Moreover, location-based recommendation requires spatiotemporal context awareness and dynamic tracking of the user's latest preferences in a real-time manner. To address these challenges, we stand on recent advances in embedding learning techniques and propose a generic graph-based embedding model, called GE, in this paper. GE jointly captures the sequential effect, geographical influence, temporal cyclic effect and semantic effect in a unified way by embedding the four corresponding relational graphs (POI-POI, POI-Region, POI-Time and POI-Word)into a shared low dimensional space. Then, to support the real-time recommendation, we develop a novel time-decay method to dynamically compute the user's latest preferences based on the embedding of his/her checked-in POIs learnt in the latent space. We conduct extensive experiments to evaluate the performance of our model on two real large-scale datasets, and the experimental results show its superiority over other competitors, especially in recommending cold-start POIs. Besides, we study the contribution of each factor to improve location-based recommendation and find that both sequential effect and temporal cyclic effect play more important roles than geographical influence and semantic effect.
An Inventory Model with Order Crossover

In inventory systems with stochastic lead times, it is possible that more than a single order will be outstanding. In that case, orders may cross; that is, they may not be received in the same sequence in which they are placed. In this paper we develop a multicycle analysis for the case when lead times are independently and identically distributed. When contrasted with the conventional single cycle analysis, the multicycle approach provides a more accurate assessment of the overall inventory cost and leads to a superior inventory policy.
Deep Residual Networks with Exponential Linear Unit

The depth of convolutional neural networks is a crucial ingredient for reduction in test errors on benchmarks like ImageNet and COCO. However, training a neural network becomes difficult with increasing depth. Problems like vanishing gradient and diminishing feature reuse are quite trivial in very deep convolutional neural networks. The notable recent contributions towards solving these problems and simplifying the training of very deep models are Residual and Highway Networks. These networks allow earlier representations (from the input or those learned in earlier layers) to flow unimpededly to later layers through skip connections. Such very deep models with hundreds or more layers have lead to a considerable decrease in test errors, on benchmarks like ImageNet and COCO. In this paper, we propose to replace the combination of ReLU and Batch Normalization with Exponential Linear Unit (ELU) in Residual Networks. Our experiments show that this not only speeds up the learning behavior in Residual Networks, but also improves the classification performance as the depth increases. Our model increases the accuracy on datasets like CIFAR-10 and CIFAR-100 by a significant margin.
Losses of chromosome 5q and 14q are associated with favorable clinical outcome of patients with gastric cancer.

PURPOSE To improve the clinical outcome of patients with gastric cancer, intensified combination strategies are currently in clinical development, including combinations of more extensive surgery, (neo)adjuvant chemotherapy, and radiotherapy. The present study used DNA copy number profiling to identify subgroups of patients with different clinical outcomes. We hypothesize that, by identification of subgroups, individual treatment strategies can be selected to improve clinical outcome and to reduce unnecessary treatment toxicity for patients with gastric cancer. EXPERIMENTAL DESIGN DNA from 206 gastric cancer patients was isolated and analyzed by genomewide array comparative genomic hybridization. DNA copy number profiles were correlated with lymph node status and patient survival. In addition, heat shock protein 90 (HSP90) expression was analyzed and correlated with survival in 230 gastric cancer patients. RESULTS Frequent (>20%) DNA copy number gains and losses were observed on several chromosomal regions. Losses on 5q11.2-q31.3 and 14q32.11-q32.33 (14% of patients) were correlated with good clinical outcome in univariate and multivariate analyses, with a median disease-free survival interval of 9.2 years. In addition, loss of expression of HSP90, located on chromosome 14q32.2, was correlated with better patient survival. CONCLUSION Genomewide DNA copy number profiling allowed the identification of a subgroup of gastric cancer patients, marked by losses on chromosomes 5q11.2-q31.3 and 14q32.11-q32.33 or low HSP90 protein expression, with an excellent clinical outcome after surgery alone. We hypothesize that this subgroup of patients most likely will not benefit from (neo)adjuvant systemic treatment and/or radiotherapy, whereas anti-HSP90 therapy may have clinical potential in patients with HSP90-expressing gastric cancer, pending validation in an independent dataset.
Digital Gastronomy: Methods & Recipes for Hybrid Cooking

Several recent projects have introduced digital machines to the kitchen, yet their impact on culinary culture is limited. We envision a culture of Digital Gastronomy that enhances traditional cooking with new interactive capabilities, rather than replacing the chef with an autonomous machine. Thus, we deploy existing digital fabrication instruments in traditional kitchen and integrate them into cooking via hybrid recipes. This concept merges manual and digital procedures, and imports parametric design tools into cooking, allowing the chef to personalize the tastes, flavors, structures and aesthetics of dishes. In this paper we present our hybrid kitchen and the new cooking methodology, illustrated by detailed recipes with degrees of freedom that can be set digitally prior to cooking. Lastly, we discuss future work and conclude with thoughts on the future of hybrid gastronomy.
Food Image Recognition Using Very Deep Convolutional Networks

We evaluated the effectiveness in classifying food images of a deep-learning approach based on the specifications of Google's image recognition architecture Inception. The architecture is a deep convolutional neural network (DCNN) having a depth of 54 layers. In this study, we fine-tuned this architecture for classifying food images from three well-known food image datasets: ETH Food-101, UEC FOOD 100, and UEC FOOD 256. On these datasets we achieved, respectively, 88.28%, 81.45%, and 76.17% as top-1 accuracy and 96.88%, 97.27%, and 92.58% as top-5 accuracy. To the best of our knowledge, these results significantly improve the best published results obtained on the same datasets, while requiring less computation power, since the number of parameters and the computational complexity are much smaller than the competitors? Because of this, even if it is still rather large, the deep network based on this architecture appears to be at least closer to the requirements for mobile systems.
USE OF CARDIOVASCULAR MEDICATIONS AFTER KIDNEY TRANSPLANTATION: ASSOCIATIONS WITH CARDIOVASCULAR RISK PROFILE: 409

H. Pilmore1, B. Kasiske2, A. Israni2, M. Skeans3, J. Snyder4 1Auckland City Hospital, Department of Renal Medicine, Auckland / NEW ZEALAND, 2, University of Minnesota, Minneapolis/UNITED STATES OF AMERICA, 3Chronic Disease Research Group, Minneapolis Medical Research Foundation, Minneapolis/UNITED STATES OF AMERICA, 4Chronic Disease Research Group, Minneapolis Medical Research Foundation, Minneapolis/MN/UNITED STATES OF AMERICA
Time-Aware Click Model

Click-through information is considered as a valuable source of users’ implicit relevance feedback for commercial search engines. As existing studies have shown that the search result position in a search engine result page (SERP) has a very strong influence on users’ examination behavior, most existing click models are position based, assuming that users examine results from top to bottom in a linear fashion. Although these click models have been successful, most do not take temporal information into account. As many existing studies have shown, click dwell time and click sequence information are strongly correlated with users’ perceived relevance and search satisfaction. Incorporating temporal information may be important to improve performance of user click models for Web searches. In this article, we investigate the problem of properly incorporating temporal information into click models. We first carry out a laboratory eye-tracking study to analyze users’ examination behavior in different click sequences and find that the user common examination path among adjacent clicks is linear. Next, we analyze the user dwell time distribution in different search logs and find that we cannot simply use a click dwell time threshold (e.g., 30 seconds) to distinguish relevant/irrelevant results. Finally, we propose a novel time-aware click model (TACM), which captures the temporal information of user behavior. We compare the TACM to several existing click models using two real-world search engine logs. Experimental results show that the TACM outperforms other click models in terms of both predicting click behavior (perplexity) and estimating result relevance (NDCG).
The Relationship Between Preexisting Gastroesophageal Reflux Disease in Lung Transplant Recipients and the Development of Post-Transplant Bronciolitis Obliterans

Lung transplant is a treatment modality for patients with end stage lung disease. Bronchiolitis obliterans syndrome (BOS) is the number one cause of morbidity and mortality in patients the first year after lung transplant. There are many risk factors which have been identified to increase the risk of BOS including acute rejection, lymphocytic bronchitis, medication non-compliance, bacterial or viral infections, older donor age, extended ischemic time, donor antigen-specific reactivity, human leukocyte antigen (HLA) mismatch, underlying disease and gastroesophageal reflux disease (GERD). Advanced practice nurses can help in the primary prevention of BOS through the assessment and treatment of pre-transplant patients with GERD. A descriptive study using retrospective chart reviews of lung transplant recipients was conducted to evaluate the relationship between pre-transplant GERD and post-transplant BOS. The incidence of pre-transplant GERD was 39%. The incidence ofBOS at year one was 17% and at year two was 32%. There was not a significant relationship between pre-transplant GERD and post-transplant BOS. CHAPTER ONE Introduction Over 1700 transplants are performed world wide every year for select end stage lung diseases (Trulock et al., 2005). The most common diseases for which lung transplant is an indication are chronic obstructive lung disease, idiopathic pulmonary fibrosis (IPF), cystic fibrosis (CF), pulmonary hypertension, and Eisenmenger syndromes. Less common indications for lung transplant include bronchiectasis, sarcoidosis, and lymphangioleiomyomatosis (LAM) and pulmonary Langerhans cell histiocytosis (Trulock et al., 2005). One possible complication of lung transplant is chronic rejection, more commonly known as bronchiolitis obliterans syndrome (BOS). BOS is the most common cause of death after the first year post-transplant (Trulock et al., 2005). The cause ofBOS is unclear and controversial. Gastroesophageal reflux disease (GERD) is a comorbid condition in end stage lung disease and is a suspected risk factor for the development of BOS in lung transplant recipients (D'Ovidio & Keshavjee, 2006; Trulock et al., 2005). Although there have been multiple studies that have looked at the role of GERD and allograft dysfunction, further research examining GERD and its association with BOS is warranted to optimize prevention and treatment options.
AVEC 2016: Depression, Mood, and Emotion Recognition Workshop and Challenge

The Audio/Visual Emotion Challenge and Workshop (AVEC 2016) "Depression, Mood and Emotion" will be the sixth competition event aimed at comparison of multimedia processing and machine learning methods for automatic audio, visual and physiological depression and emotion analysis, with all participants competing under strictly the same conditions. The goal of the Challenge is to provide a common benchmark test set for multi-modal information processing and to bring together the depression and emotion recognition communities, as well as the audio, video and physiological processing communities, to compare the relative merits of the various approaches to depression and emotion recognition under well-defined and strictly comparable conditions and establish to what extent fusion of the approaches is possible and beneficial. This paper presents the challenge guidelines, the common data used, and the performance of the baseline system on the two tasks.