Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Sketching for Kronecker Product Regression and Low Rank Approximation

We study the Kronecker product regression problem, in which the design matrix is a Kronecker product of two or more matrices. Formally, given $A_i \in \R^{n_i \times d_i}$ for $i=1,2,\dots,q$ where $n_i \gg d_i$ for each $i$, and $b \in \R^{n_1 n_2 \cdots n_q}$, let $\mathcal{A} = A_i \otimes A_2 \otimes \cdots \otimes A_q$. Then for $p \in [1,2]$, the goal is to find $x \in \R^{d_1 \cdots d_q}$ that approximately minimizes $\|\mathcal{A}x - b\|_p$. Recently, Diao, Song, Sun, and Woodruff (AISTATS, 2018) gave an algorithm which is faster than forming the Kronecker product $\mathcal{A} \in \R^{n_1 \cdots n_q \times d_1 \cdots d_q}$. Specifically, for $p=2$ they achieve a running time of $O(\sum_{i=1}^q \texttt{nnz}(A_i) + \texttt{nnz}(b))$, where $ \texttt{nnz}(A_i)$ is the number of non-zero entries in $A_i$. Note that $\texttt{nnz}(b)$ can be as large as $\Theta(n_1 \cdots n_q)$. For $p=1,$ $q=2$ and $n_1 = n_2$, they achieve a worse bound of $O(n_1^{3/2} \text{poly}(d_1d_2) + \texttt{nnz}(b))$. In this work, we provide significantly faster algorithms. For $p=2$, our running time is $O(\sum_{i=1}^q \texttt{nnz}(A_i) )$, which has no dependence on $\texttt{nnz}(b)$. For $p<2$, our running time is $O(\sum_{i=1}^q \texttt{nnz}(A_i) + \texttt{nnz}(b))$, which matches the prior best running time for $p=2$. We also consider the related all-pairs regression problem, where given $A \in \R^{n \times d}, b \in \R^n$, we want to solve $\min_{x \in \R^d} \|\bar{A}x - \bar{b}\|_p$, where $\bar{A} \in \R^{n^2 \times d}, \bar{b} \in \R^{n^2}$ consist of all pairwise differences of the rows of $A,b$. We give an $O(\texttt{nnz}(A))$ time algorithm for $p \in[1,2]$, improving the $\Omega(n^2)$ time required to form $\bar{A}$. Finally, we initiate the study of Kronecker product low rank and and low-trank approximation. For input $\mathcal{A}$ as above, we give $O(\sum_{i=1}^q \texttt{nnz}(A_i))$ time algorithms, which is much faster than computing $\mathcal{A}$.
Easy First Relation Extraction with Information Redundancy

Many existing relation extraction (RE) models make decisions globally using integer linear programming (ILP). However, it is nontrivial to make use of integer linear programming as a blackbox solver for RE. Its cost of time and memory may become unacceptable with the increase of data scale, and redundant information needs to be encoded cautiously for ILP. In this paper, we propose an easy first approach for relation extraction with information redundancies, embedded in the results produced by local sentence level extractors, during which conflict decisions are resolved with domain and uniqueness constraints. Information redundancies are leveraged to support both easy first collective inference for easy decisions in the first stage and ILP for hard decisions in a subsequent stage. Experimental study shows that our approach improves the efficiency and accuracy of RE, and outperforms both ILP and neural network-based methods.
Optic Disc Segmentation Using Cascaded Multiresolution Convolutional Neural Networks

Optic disc segmentation is a crucial step in the development of automated tools for the detection and diagnosis of optical pathologies such as glaucoma. In this paper, we build upon our previous work, where we introduced the Fine-Net [1] – a Convolutional Neural Network (CNN) for optic disc segmentation. In this work, we introduce a prior CNN called the P-Net, which is arranged in cascade with the Fine-Net, to generate a more accurate optic disc segmentation map. The P-Net generates a low-resolution (256 × 256) segmentation map which is then further upscaled along with the input image and is fed to the Fine-Net, which yields a high-resolution segmentation map (1024 × 1024). Both CNNs are separately trained on publicly available datasets: DRISHTI-GS, MESSIDOR, and DRIONS-DB. We demonstrate the advantage of providing a prior segmentation map via the P-Net and further improve on our previous predictions. We obtain state-of-the-art results with an average Dice coefficient of 0.966 and Jaccard coefficient of 0.934.
Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration

Class probabilities predicted by most multiclass classifiers are uncalibrated, often tending towards over-confidence. With neural networks, calibration can be improved by temperature scaling, a method to learn a single corrective multiplicative factor for inputs to the last softmax layer. On non-neural models the existing methods apply binary calibration in a pairwise or one-vs-rest fashion. We propose a natively multiclass calibration method applicable to classifiers from any model class, derived from Dirichlet distributions and generalising the beta calibration method from binary classification. It is easily implemented with neural nets since it is equivalent to log-transforming the uncalibrated probabilities, followed by one linear layer and softmax. Experiments demonstrate improved probabilistic predictions according to multiple measures (confidence-ECE, classwise-ECE, log-loss, Brier score) across a wide range of datasets and classifiers. Parameters of the learned Dirichlet calibration map provide insights to the biases in the uncalibrated model.
Simple Answers and Quick Fixes: Dyslexia and the Brain on the Internet

After well over a century of research about dyslexia, there is still no consensus about how it differs from other decoding difficulties, how it is identified, and its causes. Nevertheless, there is an abundance of research about dyslexia, mostly conducted outside of education, and much of it focused on the brain. This attention to the brain and dyslexia is also reflected on the Internet. In the study reported here, we analyzed information on the Internet focusing on dyslexia and the brain, grounding our examination in varying perspectives about the connections between neuroscience and education. We found that many of the sites include distortions, simplifications, and misinterpretations of neuroscience research, and some sites used this misinformation to bolster claims for the efficacy of the so-called brain-based interventions. We suggest that educators who become familiar with the limitations and affordances of neuroscience research, while maintaining a focus on the broad range of factors that influence literacy learning, can help to moderate the spread of misinformation.
Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis

In recent times, multi-modal analysis has been an emerging and highly sought-after field at the intersection of natural language processing, computer vision, and speech processing. The prime objective of such studies is to leverage the diversified information, (e.g., textual, acoustic and visual), for learning a model. The effective interaction among these modalities often leads to a better system in terms of performance. In this paper, we introduce a recurrent neural network based approach for the multi-modal sentiment and emotion analysis. The proposed model learns the inter-modal interaction among the participating modalities through an auto-encoder mechanism. We employ a context-aware attention module to exploit the correspondence among the neighboring utterances. We evaluate our proposed approach for five standard multi-modal affect analysis datasets. Experimental results suggest the efficacy of the proposed model for both sentiment and emotion analysis over various existing state-of-the-art systems.
An exploration of patients' perceived control, self efficacy and involvement in self care during chemotherapy for colorectal cancer

This thesis describes a three year study which explored perceptions and experiences of being involved in self care and perceptions of control and self-efficacy over time amongst patients receiving a six month course of chemotherapy treatment for colorectal cancer. The study was underpinned by Leventhal’s Self Regulation Model and aimed to explore how patients undergoing chemotherapy for colorectal cancer perceived the meaning of self care, what they did as part of their self care in managing the effects of their treatment and whether this changed between the beginning and end of their six month course of chemotherapy. The study also set out to explore the relationship between patients’ perceptions of control and self efficacy and their involvement in self care. The study adopted a patient focussed, mixed method, longitudinal approach for complementarity and expansion purposes in which the qualitative findings formed the focus of the investigation, supplemented by the quantitative findings. This was important to provide a greater breadth and range to the study and to obtain a realistic understanding of patients’ perceptions and experiences of being involved in self care during their six month course of chemotherapy treatment and the influence of their perceptions of control and self efficacy on their involvement in self care. Thirty one patients participated in the study and data were collected using qualitative semi structured interviews (with a subsample of patients who participated in the study) and quantitative questionnaires (Illness Perception Questionnaire-revised and the Strategies Used by People to Promote Health) and prospective self care diaries with the full study sample. Data were collected at several time points over the course of patients’ chemotherapy treatment (beginning, middle and end of treatment) and were analysed and integrated in accordance with Tashakkori and Teddlie (1998)’s guidance for integrating qualitative and quantitative findings in a mixed methods study.
Intelligent Health Vessel ABC-DE: An Electrocardiogram Cloud Computing Service

The severe challenges of the fast aging population and the prevalence of cardiovascular diseases highlight the needs for effective solutions supporting more accurate and affordable medical diagnosis and treatment. Recent advances in cloud computing have inspired numerous designs of cloud-based health care services. In this paper, we developed a cloud-computing platform monitored by physicians, which can receive 12-lead ECG records and send back diagnostic reports to users. Aiming to lessen the physicians’ workload, we implemented an analysis algorithm that can identify abnormal heart rate, irregular heartbeat, abnormal amplitude, atrial fibrillation and abnormal ECG in it. A large number of testing samples were used to evaluate performance. Our algorithm achieved a TPR95 (specificity under the condition of negative predictive value being equal to 95 percent) of 68.5 percent and 0.9317 AUC (area under the ROC curve) for classification of normal and abnormal ECG records and a sensitivity of 98.51 percent and specificity of 98.26 percent for atrial fibrillation classification, comparable to the state-of-the-art results for each subject. The proposed ECG cloud computing service has been applied in Hunan Jinshengda Aerial Hospital Network and it now can receive and analyze ECG records in real time.
CaRB: A Crowdsourced Benchmark for Open IE

Open Information Extraction (Open IE) systems have been traditionally evaluated via manual annotation. Recently, an automated evaluator with a benchmark dataset (OIE2016) was released – it scores Open IE systems automatically by matching system predictions with predictions in the benchmark dataset. Unfortunately, our analysis reveals that its data is rather noisy, and the tuple matching in the evaluator has issues, making the results of automated comparisons less trustworthy. We contribute CaRB, an improved dataset and framework for testing Open IE systems. To the best of our knowledge, CaRB is the first crowdsourced Open IE dataset and it also makes substantive changes in the matching code and metrics. NLP experts annotate CaRB’s dataset to be more accurate than OIE2016. Moreover, we find that on one pair of Open IE systems, CaRB framework provides contradictory results to OIE2016. Human assessment verifies that CaRB’s ranking of the two systems is the accurate ranking. We release the CaRB framework along with its crowdsourced dataset.
Optimal Choice of Motion Estimation Methods for Fine-Grained Action Classification with 3D Convolutional Networks

Detecting and classifying human actions in videos is one of the current challenges in visual content analysis and mining. This paper presents a method for performing a finegrained classification of sport actions using a Siamese SpatioTemporal Convolutional Neural Network (SSTCNN) model. This model takes RGB images and Optical Flow field as input data. Our first contribution is the comparison of different Optical flow methods and a study of their influence on the classification score. We also present different normalization methods for the optical flow that drastically impact results, boosting performances from 44% to 74% of accuracy. Our second contribution is the detection and classification of actions in videos performed using a sliding temporal window. It leads to a satisfying score of 81.3% over the whole dataset TTStroke–21.
Network Model for Online News Media Landscape in Twitter

Today, most studies of audience networks analyze the landscape of the news media on the web. However, media ecology has been drastically reconfigured by the emergence of social media. In this study, we use Twitter follower data to build an online news media network that represents the pattern of news consumption in Twitter. This study adopted a weighted network model proposed by Mukerjee et al. and implemented the Filter Disparity Method suggested by Majó-Vázquez et al. to identify the most significant overlaps in the network. The implementation result on news media outlets data in three countries, namely Indonesia, Malaysia, and Singapore, shows that network analysis of follower overlap data can offer relevant insights about media diet and the way readers navigate various news sources available on social media.
Neural News Recommendation with Multi-Head Self-Attention

News recommendation can help users find interested news and alleviate information overload. Precisely modeling news and users is critical for news recommendation, and capturing the contexts of words and news is important to learn news and user representations. In this paper, we propose a neural news recommendation approach with multi-head self-attention (NRMS). The core of our approach is a news encoder and a user encoder. In the news encoder, we use multi-head self-attentions to learn news representations from news titles by modeling the interactions between words. In the user encoder, we learn representations of users from their browsed news and use multi-head self-attention to capture the relatedness between the news. Besides, we apply additive attention to learn more informative news and user representations by selecting important words and news. Experiments on a real-world dataset validate the effectiveness and efficiency of our approach.
Effect of Rosiglitazone on early-morning plasma cortisol levels

The study by Catrina et al. published in this issue, showing that the long-term treatment of diabetic patients with PPaR gamma agonist rosaglitazone failed to affect the morning cortisol levels seemingly stays in opposition with our [1] and other earlier data concerning in vitro effects of rosiglitazone on animal pituitary tumors. However, several important points should be taken into consideration.
Unsupervised Seismic Data Interpolation via Deep Convolutional Autoencoder

Summary Seismic data interpolation is an essential prerequisite for multiple removal, imaging and seismic attributes analysis. We proposed a specific deep convolutional autoencoder (DCAE) for seismic data interpolation in an unsurpervised approach. DCAE is trained on undersampled seismic data with randomly 50% missing traces and only the reconstruction error on the uncurropted data is considered without generating labels on missing traces. Synthetic data and field data testing shows that the structure of DCEC is capble to capture of the low-level statics prior information from undersampled seismic data, and the tranied model has a good interpolation performance on regular and irrgular sampled seismic data.
Leadership Retention in Physical Therapy Education Programs

Background and Purpose. Program leadership is a key to achieving program outcomes and meeting the program’s mission. The Commission on Accreditation in Physical Therapy Education (CAPTE) requires that programs have qualified and effective academic administrators. In recent years, CAPTE has noted a high rate of attrition among academic administrator positions for physical therapist (PT) and physical therapist assistant (PTA) education programs. Therefore, a survey was developed to help identify the reasons program directors were leaving their administrative positions. Subjects. The survey was sent to individuals who had vacated positions as directors of PT or PTA education programs during the 3‐year time period from 2008 to 2011. Seventy‐nine individuals returned surveys for a response rate of 59%. Methods. The survey was developed by the members of CAPTE’s Central Panel. Members of the other 2 panels (PT and PTA) reviewed the survey for clarity and face validity. The survey was sent electronically to former program directors. Survey items included demographic information, institutional classification, years of academic experience, and reasons for leaving an administrative position. Results. Individuals who had directed PT programs comprised 58% of the sample versus 41% who had directed PTA programs (1% directed both). The most frequently cited reasons for leaving administrative positions included: (1) a perceived lack of resources or support from university administration or some type of internal conflict; (2) excessively high/demanding workloads; (3) inadequate compensation; (4) promotion to a higher administrative position; and (5) inability to hire or retain adequate faculty to operate the program. A greater proportion of PT program directors had prior academic experience compared to PTA program directors. Program directors who had at least 5 years of prior academic experience stayed in their administrative positions longer. Individuals who were hired to direct developing programs were less likely to have had prior academic experience than those who were hired to direct established programs, especially in PTA education. Retention was higher at public and nonprofit private institutions compared to proprietary institutions. Discussion and Conclusion. To enhance retention of program administrators, institutions should consider structuring a development program to support program directors; such a program could include formal or informal mentoring, networking with experienced colleagues in similar positions, and regular communication with supervising administrators (deans and/or chairs). Because most PT and PTA program directors enter their administrative positions without any formal management training, continuing education courses or workshops on topics such as strategic and long‐term planning, fiscal and personnel management, and accreditation issues may be particularly beneficial. Prospective program directors may also benefit from exploring the institutional expectations of an administrative position prior to accepting such a position.
Pattern of Workplace Violence and Perceived Effects on Nurses’ Work Productivity in Selected Hospitals in Ibadan, Oyo State

Background: Workplace violence (WPV) is a widespread occupational hazard affecting the health, safety, job attitude and productivity of the nursing workforce. This study was designed to investigate the pattern and perceived effects of WPV on nurses’ work productivity in two selected hospitals in Ibadan.Methods: This descriptive study was conducted in two purposively selected hospitals. Out of 1,418 nurses, 349 proportional sample was taken and respondents were selected using convenience sampling technique. Two validated questionnaires was used. Data were analysed using frequencies and percentages. Hypotheses were tested using chi-square at α=0.05. Results: Respondents’ age was 39 ± 6.5years; 46.8% were registered nurses, of which 48.1% had worked for 11-20 years. On pattern of violence, 49(15.6%) experienced physical violence without a weapon, 39(12.4%) experienced verbal harassment and bullying. Attackers of 33(10.5%) were patient relatives. Decreased work productivity due to WPV was reported(60, 19.1%) while no change in work productivity was reported(170, 54.1%). Type of WPV was significantly associated with nurses productivityx2=39.7; p=0.001. Conclusion: Nurses experience violence in workplace commonly instigated by patient relatives. This affects job productivity. Strict implementation of policies on violence is required to enhance workplace safety and productivity.
Iron deficiency and overload : from basic biology to clinical medicine

I. Iron biochemistry and metabolism 1. The cellular physiology of iron - Anderson GJ. and Vulpe C.D. 2. Regulation of iron absorption and distribution - Latunde-dada,G.O. & Simpson RJ. 3. The role of hepcidin in iron homeostasis - Ganz, T 4. Iron as nutrient: Strategies for iron acquisition and usage by pathogenic microorganisms - Masse E & Pawelek, PD 5. Iron as a drug and drug-drug interactions - Hagemann T.M. II. Iron overload and deficiency pathologies 6. Iron Deficiency and Excess in the brain: Implications for Cognitive Impairment and Neurodegeneration - Youdim MBH 7. Brain iron deposition in aging and disease: Role of HO-1 - Schipper HM. 8. Iron Deficiency and Neuropharmacology- Beard J 9. Peripheral effects of Iron Deficiency - Costa D. and Drews a R.E. 10. Iron Metabolism in neurons of the motor systems of the central nervous system: Lessons from Iron Deficiency and Overloading pathologies - Moos T. 11. Learning. Memory and Cognitive functioning - Yehuda, S. , Rabinovitz, S. , Carasso, R.L. and Mostofsky D.I. 12. Stress , Immunology and Cytokines - Sredni III. Diagnostic & clinical aspects in CNS disorders 13. MRI of brain iron and Neurodegenerative diseases: A Potential Biomarker - Schenck 14. Alzheimer's dementia - M. Mattson and S. Gulyani 15. Inherited disorders of brain iron homeostasis - Schipper, H.M. and P.Ponka IV. Public Health issues 16. Iron and Heart Disease: A Review of the epidemiologic data -Sempos, CT, Looker, AC, and McGee,Sr. DL 17. Global concept of iron deficiency - S.M. Lewis and J. Emmanuel 18. Nutritional Iron deficiency in early childhood- Moy, RJ 19. Iron in Women's Health - A. Bendich and R. Zilberboim Appendix I: Current normal values for bloodlevel of iron constituents - Serica and Kratz A Appendix II: Food sources of iron. - Serica and Kratz
Modeling shopping cart decisions with heterogeneous product involvement and reviews

The most recent consumer propensity study by SAP indicates that online shopping cart abandonment is high and the associated reasons are complex. In order to examine this phenomenon, we construct online SCA decision as a discrete choice model (DCM) and capture consumer segments by a latent class model (LCM) in this research-in-progress (RIP) paper, grounded on the theories of product involvement, word of mouth, and consumer heterogeneity. We will apply the clickstream dataset from 78,746 consumers at a large Chinese online platform to verify the proposed models in future study. The objective of this research project is to scrutinize the heterogeneous impacts of product involvement and online reviews on shopping cart decision-making in view of individual-level sequential behavior and the associated products in the form of stock-keeping-unit items. We conclude this RIP paper with the discussion of potential theoretical contributions and managerial implications.